<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Méndez-Vidal, Cristina</style></author><author><style face="normal" font="default" size="100%">Bravo-Gil, Nereida</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Marcos-Luque, Irene</style></author><author><style face="normal" font="default" size="100%">Fernández, Raquel M</style></author><author><style face="normal" font="default" size="100%">Fernandez-Rueda, Jose Luis</style></author><author><style face="normal" font="default" size="100%">González-del Pozo, María</style></author><author><style face="normal" font="default" size="100%">Martín-Sánchez, Marta</style></author><author><style face="normal" font="default" size="100%">Fernández-Suárez, Elena</style></author><author><style face="normal" font="default" size="100%">Mena, Marcela</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A genomic strategy for precision medicine in rare diseases: integrating customized algorithms into clinical practice.</style></title><secondary-title><style face="normal" font="default" size="100%">J Transl Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Transl Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Precision Medicine</style></keyword><keyword><style  face="normal" font="default" size="100%">rare diseases</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2025 Jan 20</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">86</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Despite the use of Next-Generation Sequencing (NGS) as the gold standard for the diagnosis of rare diseases, its clinical implementation has been challenging, limiting the cost-effectiveness of NGS and the understanding, control and safety essential for decision-making in clinical applications. Here, we describe a personalized NGS-based strategy integrating precision medicine into a public healthcare system and its implementation in the routine diagnosis process during a five-year pilot program.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Our approach involved customized probe designs, the generation of virtual panels and the development of a personalized medicine module (PMM) for variant prioritization. This strategy was applied to 6500 individuals including 6267 index patients and 233 NGS-based carrier screenings.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Causative variants were identified in 2061 index patients (average 32.9%, ranging from 12 to 62% by condition). Also, 131 autosomal-recessive cases could be partially genetically diagnosed. These results led to over 5000 additional studies including carrier, prenatal and preimplantational tests or pharmacological and gene therapy treatments.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;This strategy has shown promising improvements in the diagnostic rate, facilitating timely diagnosis and gradually expanding our services portfolio for rare diseases. The steps taken towards the integration of clinical and genomic data are opening new possibilities for conducting both retrospective and prospective healthcare studies. Overall, this study represents a major milestone in the ongoing efforts to improve our understanding and clinical management of rare diseases, a crucial area of medical research and care.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martínez-Nava, Gabriela Angélica</style></author><author><style face="normal" font="default" size="100%">Altamirano-Molina, Efren</style></author><author><style face="normal" font="default" size="100%">Vázquez-Mellado, Janitzia</style></author><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Lozada-Pérez, Carlos</style></author><author><style face="normal" font="default" size="100%">Herrera-López, Brígida</style></author><author><style face="normal" font="default" size="100%">Martínez-Gómez, Laura Edith</style></author><author><style face="normal" font="default" size="100%">Martínez-Armenta, Carlos</style></author><author><style face="normal" font="default" size="100%">Guido-Gómora, Dafne Lissete</style></author><author><style face="normal" font="default" size="100%">Valle-Gutiérrez, Sarahí</style></author><author><style face="normal" font="default" size="100%">Suarez-Ahedo, Carlos</style></author><author><style face="normal" font="default" size="100%">Camacho-Rea, María Del Carmen</style></author><author><style face="normal" font="default" size="100%">Martínez-García, Mireya</style></author><author><style face="normal" font="default" size="100%">Gutiérrez-Esparza, Guadalupe</style></author><author><style face="normal" font="default" size="100%">Amezcua-Guerra, Luis M</style></author><author><style face="normal" font="default" size="100%">Zamudio-Cuevas, Yessica</style></author><author><style face="normal" font="default" size="100%">Martínez-Flores, Karina</style></author><author><style face="normal" font="default" size="100%">Fernández-Torres, Javier</style></author><author><style face="normal" font="default" size="100%">Burguete-García, Ana I</style></author><author><style face="normal" font="default" size="100%">Orbe-Orihuela, Yaneth Citlalli</style></author><author><style face="normal" font="default" size="100%">Lagunas-Martínez, Alfredo</style></author><author><style face="normal" font="default" size="100%">Méndez-Salazar, Eder Orlando</style></author><author><style face="normal" font="default" size="100%">Francisco-Balderas, Adriana</style></author><author><style face="normal" font="default" size="100%">Palacios-González, Berenice</style></author><author><style face="normal" font="default" size="100%">Pineda, Carlos</style></author><author><style face="normal" font="default" size="100%">López-Reyes, Alberto</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Metatranscriptomic analysis reveals gut microbiome bacterial genes in pyruvate and amino acid metabolism associated with hyperuricemia and gout in humans.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Amino Acids</style></keyword><keyword><style  face="normal" font="default" size="100%">Bacteria</style></keyword><keyword><style  face="normal" font="default" size="100%">Case-Control Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Feces</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gastrointestinal Microbiome</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Gout</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Hyperuricemia</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Pyruvic Acid</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2025 Mar 22</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">9981</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Several pathologies with metabolic origin, such as hyperuricemia and gout, have been associated with the gut microbiota taxonomic profile. However, there is no evidence of which bacterial genes are being expressed in the gut microbiome, and of their potential effects on hyperuricemia and gout. We sequenced the RNA of 26 fecal samples from 10 healthy normouricemic controls, 10 with asymptomatic hyperuricemia (AH), and six gout patients. The coding sequences were mapped to KEGG orthologues (KO). We compared the expression levels using generalized linear models and validated the expression of four KO in a larger sample by qRT-PCR. A distinct genetic expression pattern was identified among groups. AH individuals and gout patients showed an over-expression of KOs mainly related to pyruvate metabolism (Log2foldchange &gt; 23, p-adj ≤ 3.56 × 10), the pentose pathway (Log2foldchange &gt; 24, p-adj &lt; 1.10 × 10) and purine metabolism (Log2foldchange &gt; 22, p-adj &lt; 1.25 × 10). AH subjects had lower expression of KO related to glycine metabolism (Log2foldchange=-18, p-adj &lt; 1.72 × 10) than controls. Gout patients had lower expression (Log2foldchange=-22.42, p-adj &lt; 3.31 × 10) of a KO involved in phenylalanine biosynthesis, in comparison to controls and AH subjects. The over-expression seen for the KO related to pyruvate metabolism and the pentose pathway in gout patients´ microbiome was validated. There is a differential gene expression pattern in the gut microbiome of normouricemic individuals, AH subjects and gout patients. These differences are mainly located in metabolic pathways involved in acetate precursors and bioavailability of amino acids.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Greene, Daniel</style></author><author><style face="normal" font="default" size="100%">De Wispelaere, Koenraad</style></author><author><style face="normal" font="default" size="100%">Lees, Jon</style></author><author><style face="normal" font="default" size="100%">Codina-Solà, Marta</style></author><author><style face="normal" font="default" size="100%">Jensson, Brynjar O.</style></author><author><style face="normal" font="default" size="100%">Hales, Emma</style></author><author><style face="normal" font="default" size="100%">Katrinecz, Andrea</style></author><author><style face="normal" font="default" size="100%">Nieto Molina, Esther</style></author><author><style face="normal" font="default" size="100%">Sevilla Porras, Marta</style></author><author><style face="normal" font="default" size="100%">Pascoal, Sonia</style></author><author><style face="normal" font="default" size="100%">Valenzuela, Irene</style></author><author><style face="normal" font="default" size="100%">Pfundt, Rolph</style></author><author><style face="normal" font="default" size="100%">Schot, Rachel</style></author><author><style face="normal" font="default" size="100%">Sleutels, Frank</style></author><author><style face="normal" font="default" size="100%">Wijngaard, Robin</style></author><author><style face="normal" font="default" size="100%">Arroyo Carrera, Ignacio</style></author><author><style face="normal" font="default" size="100%">Duat Rodríguez, Anna</style></author><author><style face="normal" font="default" size="100%">Atton, Giles</style></author><author><style face="normal" font="default" size="100%">Casas-Alba, Didac</style></author><author><style face="normal" font="default" size="100%">Donnelly, Deirdre</style></author><author><style face="normal" font="default" size="100%">Fernández Garoz, Bárbara</style></author><author><style face="normal" font="default" size="100%">Foulds, Nicola</style></author><author><style face="normal" font="default" size="100%">García-Navas Núñez, Deyanira</style></author><author><style face="normal" font="default" size="100%">González Alguacil, Elena</style></author><author><style face="normal" font="default" size="100%">Jarvis, Joanna</style></author><author><style face="normal" font="default" size="100%">Kant, Sarina G.</style></author><author><style face="normal" font="default" size="100%">Madrigal Bajo, Irene</style></author><author><style face="normal" font="default" size="100%">Martinez-Monseny, Antonio F.</style></author><author><style face="normal" font="default" size="100%">McKee, Shane</style></author><author><style face="normal" font="default" size="100%">Sariego Jamardo, Andrea</style></author><author><style face="normal" font="default" size="100%">Stefansson, Kari</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Revenga Bodi, Laia</style></author><author><style face="normal" font="default" size="100%">Ortiz Cabrera, Nelmar Valentina</style></author><author><style face="normal" font="default" size="100%">Sulem, Patrick</style></author><author><style face="normal" font="default" size="100%">Suri, Mohnish</style></author><author><style face="normal" font="default" size="100%">Van Karnebeek, Clara</style></author><author><style face="normal" font="default" size="100%">Vasudevan, Pradeep</style></author><author><style face="normal" font="default" size="100%">Vega Pajares, Ana Isabel</style></author><author><style face="normal" font="default" size="100%">Carracedo, Ángel</style></author><author><style face="normal" font="default" size="100%">Engelen, Marc</style></author><author><style face="normal" font="default" size="100%">Lapunzina, Pablo</style></author><author><style face="normal" font="default" size="100%">Morgan, Natasha P.</style></author><author><style face="normal" font="default" size="100%">Morte, Beatriz</style></author><author><style face="normal" font="default" size="100%">Rump, Patrick</style></author><author><style face="normal" font="default" size="100%">Stirrups, Kathy</style></author><author><style face="normal" font="default" size="100%">Tizzano, Eduardo F.</style></author><author><style face="normal" font="default" size="100%">Barakat, Tahsin Stefan</style></author><author><style face="normal" font="default" size="100%">O’Donoghue, Michael</style></author><author><style face="normal" font="default" size="100%">Pérez-Jurado, Luis Alberto</style></author><author><style face="normal" font="default" size="100%">Freson, Kathleen</style></author><author><style face="normal" font="default" size="100%">Mumford, Andrew D.</style></author><author><style face="normal" font="default" size="100%">Turro, Ernest</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mutations in the small nuclear RNA gene RNU2-2 cause a severe neurodevelopmental disorder with prominent epilepsy</style></title><secondary-title><style face="normal" font="default" size="100%">Nature Genetics</style></secondary-title><short-title><style face="normal" font="default" size="100%">Nat Genet</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct-04-2025</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.nature.com/articles/s41588-025-02159-5https://www.nature.com/articles/s41588-025-02159-5.pdfhttps://www.nature.com/articles/s41588-025-02159-5.pdfhttps://www.nature.com/articles/s41588-025-02159-5</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Loucera-Muñecas, C</style></author><author><style face="normal" font="default" size="100%">Canal-Rivero, M</style></author><author><style face="normal" font="default" size="100%">Ruiz-Veguilla, M</style></author><author><style face="normal" font="default" size="100%">Carmona, R</style></author><author><style face="normal" font="default" size="100%">Bostelmann, G</style></author><author><style face="normal" font="default" size="100%">Garrido-Torres, N</style></author><author><style face="normal" font="default" size="100%">Dopazo, J</style></author><author><style face="normal" font="default" size="100%">Crespo-Facorro, B</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aripiprazole as protector against COVID-19 mortality.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged, 80 and over</style></keyword><keyword><style  face="normal" font="default" size="100%">Antipsychotic Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Aripiprazole</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19 Drug Treatment</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Retrospective Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 May 29</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">12362</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The relation of antipsychotics with severe Coronavirus Disease 19 (COVID-19) outcomes is a matter of debate since the beginning of the pandemic. To date, controversial results have been published on this issue. We aimed to prove whether antipsychotics might exert adverse or protective effects against fatal outcomes derived from COVID-19. A population-based retrospective cohort study (January 2020 to November 2020) comprising inpatients (15,968 patients) who were at least 18 years old and had a laboratory-confirmed COVID-19 infection. Two sub-cohorts were delineated, comprising a total of 2536 inpatients: individuals who either had no prescription medication or were prescribed an antipsychotic within the 15 days preceding hospitalization. We conducted survival and odds ratio analyses to assess the association between antipsychotic use and mortality, reporting both unadjusted and covariate-adjusted results. We computed the average treatment effects, using the untreated group as the reference, and the average treatment effect on the treated, focusing solely on the antipsychotic-treated population. Among the eight antipsychotics found to be in use, only aripiprazole showed a significant decrease in the risk of death from COVID-19 [adjusted odds ratio (OR) = 0.86; 95% CI, 0.79-0.93, multiple-testing adjusted p-value &lt; 0.05]. Importantly, these findings were consistent for both covariate-adjusted and unadjusted analyses. Aripiprazole has been shown to have a differentiated beneficial effect in protecting against fatal clinical outcome in COVID-19 infected individuals. We speculate that the differential effect of aripiprazole on controlling immunological pathways and inducible inflammatory enzymes, that are critical in COVID19 illness, may be associated with our findings herein.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maillo, Alberto</style></author><author><style face="normal" font="default" size="100%">Huergo, Estefania</style></author><author><style face="normal" font="default" size="100%">Apellániz-Ruiz, María</style></author><author><style face="normal" font="default" size="100%">Urrutia-Lafuente, Edurne</style></author><author><style face="normal" font="default" size="100%">Miranda, María</style></author><author><style face="normal" font="default" size="100%">Salgado, Josefa</style></author><author><style face="normal" font="default" size="100%">Pasalodos-Sanchez, Sara</style></author><author><style face="normal" font="default" size="100%">Delgado-Mora, Luna</style></author><author><style face="normal" font="default" size="100%">Teijido, Óscar</style></author><author><style face="normal" font="default" size="100%">Goicoechea, Ibai</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Aquino, Virginia</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Beltran, Sergi</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Lasa, Iñigo</style></author><author><style face="normal" font="default" size="100%">Beloqui, Juan José</style></author><author><style face="normal" font="default" size="100%">Alonso, Ángel</style></author><author><style face="normal" font="default" size="100%">Gomez-Cabrero, David</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Characterization of the Common Genetic Variation in the Spanish Population of Navarre.</style></title><secondary-title><style face="normal" font="default" size="100%">Genes (Basel)</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genes (Basel)</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetics, Population</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Spain</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 May 04</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Large-scale genomic studies have significantly increased our knowledge of genetic variability across populations. Regional genetic profiling is essential for distinguishing common benign variants from disease-causing ones. To this end, we conducted a comprehensive characterization of exonic variants in the population of Navarre (Spain), utilizing whole genome sequencing data from 358 unrelated individuals of Spanish origin. Our analysis revealed 61,410 biallelic single nucleotide variants (SNV) within the Navarrese cohort, with 35% classified as common (MAF &gt; 1%). By comparing allele frequency data from 1000 Genome Project (excluding the Iberian cohort of Spain, IBS), Genome Aggregation Database, and a Spanish cohort (including IBS individuals and data from Medical Genome Project), we identified 1069 SNVs common in Navarre but rare (MAF ≤ 1%) in all other populations. We further corroborated this observation with a second regional cohort of 239 unrelated exomes, which confirmed 676 of the 1069 SNVs as common in Navarre. In conclusion, this study highlights the importance of population-specific characterization of genetic variation to improve allele frequency filtering in sequencing data analysis to identify disease-causing variants.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Niarakis, Anna</style></author><author><style face="normal" font="default" size="100%">Ostaszewski, Marek</style></author><author><style face="normal" font="default" size="100%">Mazein, Alexander</style></author><author><style face="normal" font="default" size="100%">Kuperstein, Inna</style></author><author><style face="normal" font="default" size="100%">Kutmon, Martina</style></author><author><style face="normal" font="default" size="100%">Gillespie, Marc E</style></author><author><style face="normal" font="default" size="100%">Funahashi, Akira</style></author><author><style face="normal" font="default" size="100%">Acencio, Marcio Luis</style></author><author><style face="normal" font="default" size="100%">Hemedan, Ahmed</style></author><author><style face="normal" font="default" size="100%">Aichem, Michael</style></author><author><style face="normal" font="default" size="100%">Klein, Karsten</style></author><author><style face="normal" font="default" size="100%">Czauderna, Tobias</style></author><author><style face="normal" font="default" size="100%">Burtscher, Felicia</style></author><author><style face="normal" font="default" size="100%">Yamada, Takahiro G</style></author><author><style face="normal" font="default" size="100%">Hiki, Yusuke</style></author><author><style face="normal" font="default" size="100%">Hiroi, Noriko F</style></author><author><style face="normal" font="default" size="100%">Hu, Finterly</style></author><author><style face="normal" font="default" size="100%">Pham, Nhung</style></author><author><style face="normal" font="default" size="100%">Ehrhart, Friederike</style></author><author><style face="normal" font="default" size="100%">Willighagen, Egon L</style></author><author><style face="normal" font="default" size="100%">Valdeolivas, Alberto</style></author><author><style face="normal" font="default" size="100%">Dugourd, Aurélien</style></author><author><style face="normal" font="default" size="100%">Messina, Francesco</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Soliman, Sylvain</style></author><author><style face="normal" font="default" size="100%">Aghamiri, Sara Sadat</style></author><author><style face="normal" font="default" size="100%">Puniya, Bhanwar Lal</style></author><author><style face="normal" font="default" size="100%">Naldi, Aurélien</style></author><author><style face="normal" font="default" size="100%">Helikar, Tomáš</style></author><author><style face="normal" font="default" size="100%">Singh, Vidisha</style></author><author><style face="normal" font="default" size="100%">Fernández, Marco Fariñas</style></author><author><style face="normal" font="default" size="100%">Bermudez, Viviam</style></author><author><style face="normal" font="default" size="100%">Tsirvouli, Eirini</style></author><author><style face="normal" font="default" size="100%">Montagud, Arnau</style></author><author><style face="normal" font="default" size="100%">Noël, Vincent</style></author><author><style face="normal" font="default" size="100%">Ponce-de-Leon, Miguel</style></author><author><style face="normal" font="default" size="100%">Maier, Dieter</style></author><author><style face="normal" font="default" size="100%">Bauch, Angela</style></author><author><style face="normal" font="default" size="100%">Gyori, Benjamin M</style></author><author><style face="normal" font="default" size="100%">Bachman, John A</style></author><author><style face="normal" font="default" size="100%">Luna, Augustin</style></author><author><style face="normal" font="default" size="100%">Piñero, Janet</style></author><author><style face="normal" font="default" size="100%">Furlong, Laura I</style></author><author><style face="normal" font="default" size="100%">Balaur, Irina</style></author><author><style face="normal" font="default" size="100%">Rougny, Adrien</style></author><author><style face="normal" font="default" size="100%">Jarosz, Yohan</style></author><author><style face="normal" font="default" size="100%">Overall, Rupert W</style></author><author><style face="normal" font="default" size="100%">Phair, Robert</style></author><author><style face="normal" font="default" size="100%">Perfetto, Livia</style></author><author><style face="normal" font="default" size="100%">Matthews, Lisa</style></author><author><style face="normal" font="default" size="100%">Rex, Devasahayam Arokia Balaya</style></author><author><style face="normal" font="default" size="100%">Orlic-Milacic, Marija</style></author><author><style face="normal" font="default" size="100%">Gomez, Luis Cristobal Monraz</style></author><author><style face="normal" font="default" size="100%">De Meulder, Bertrand</style></author><author><style face="normal" font="default" size="100%">Ravel, Jean Marie</style></author><author><style face="normal" font="default" size="100%">Jassal, Bijay</style></author><author><style face="normal" font="default" size="100%">Satagopam, Venkata</style></author><author><style face="normal" font="default" size="100%">Wu, Guanming</style></author><author><style face="normal" font="default" size="100%">Golebiewski, Martin</style></author><author><style face="normal" font="default" size="100%">Gawron, Piotr</style></author><author><style face="normal" font="default" size="100%">Calzone, Laurence</style></author><author><style face="normal" font="default" size="100%">Beckmann, Jacques S</style></author><author><style face="normal" font="default" size="100%">Evelo, Chris T</style></author><author><style face="normal" font="default" size="100%">D'Eustachio, Peter</style></author><author><style face="normal" font="default" size="100%">Schreiber, Falk</style></author><author><style face="normal" font="default" size="100%">Saez-Rodriguez, Julio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Kuiper, Martin</style></author><author><style face="normal" font="default" size="100%">Valencia, Alfonso</style></author><author><style face="normal" font="default" size="100%">Wolkenhauer, Olaf</style></author><author><style face="normal" font="default" size="100%">Kitano, Hiroaki</style></author><author><style face="normal" font="default" size="100%">Barillot, Emmanuel</style></author><author><style face="normal" font="default" size="100%">Auffray, Charles</style></author><author><style face="normal" font="default" size="100%">Balling, Rudi</style></author><author><style face="normal" font="default" size="100%">Schneider, Reinhard</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">COVID-19 Disease Map Community</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.</style></title><secondary-title><style face="normal" font="default" size="100%">Front Immunol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Front Immunol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">drug repositioning</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">1282859</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;INTRODUCTION: &lt;/b&gt;The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19.&lt;/p&gt;&lt;p&gt;&lt;b&gt;DISCUSSION: &lt;/b&gt;The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fernández-Palacios, Pablo</style></author><author><style face="normal" font="default" size="100%">Galán-Sánchez, Fátima</style></author><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Jurado-Tarifa, Estefanía</style></author><author><style face="normal" font="default" size="100%">Arroyo, Federico</style></author><author><style face="normal" font="default" size="100%">Lara, María</style></author><author><style face="normal" font="default" size="100%">Chaves, J Alberto</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Iglesias, Manuel A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genotypic characterization and antimicrobial susceptibility of human  isolates in Southern Spain.</style></title><secondary-title><style face="normal" font="default" size="100%">Microbiol Spectr</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Microbiol Spectr</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged, 80 and over</style></keyword><keyword><style  face="normal" font="default" size="100%">Anti-Bacterial Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Campylobacter Infections</style></keyword><keyword><style  face="normal" font="default" size="100%">Campylobacter jejuni</style></keyword><keyword><style  face="normal" font="default" size="100%">Child</style></keyword><keyword><style  face="normal" font="default" size="100%">Child, Preschool</style></keyword><keyword><style  face="normal" font="default" size="100%">Ciprofloxacin</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug Resistance, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Erythromycin</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Infant</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Microbial Sensitivity Tests</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">Spain</style></keyword><keyword><style  face="normal" font="default" size="100%">Tetracycline</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Oct 03</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">e0102824</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt; is the main cause of bacterial gastroenteritis and a public health problem worldwide. Little information is available on the genotypic characteristics of human  in Spain. This study is based on an analysis of the resistome, virulome, and phylogenetic relationship, antibiogram prediction, and antimicrobial susceptibility of 114 human isolates of  from a tertiary hospital in southern Spain from October 2020 to June 2023. The isolates were sequenced using Illumina technology, and a bioinformatic analysis was subsequently performed. The susceptibility of  isolates to ciprofloxacin, tetracycline, and erythromycin was also tested. The resistance rates for each antibiotic were 90.3% for ciprofloxacin, 66.7% for tetracycline, and 0.88% for erythromycin. The fluoroquinolone resistance rate obtained is well above the European average (69.1%). CC-21 ( = 23), ST-572 ( = 13), and ST-6532 ( = 13) were the most prevalent clonal complexes (CCs) and sequence types (STs). In the virulome, the , and  genes were detected in all the isolates. A prevalence of 20.1% was obtained for the genes  and , which are related to the pathogenesis of Guillain-Barré syndrome (GBS). The prevalence of the main antimicrobial resistance markers detected were CmeABC (92.1%), RE-cmeABC (7.9%), the T86I substitution in  (88.9%),  (72.6%) (65.8%), and  (17.1%). High antibiogram prediction rates (&gt;97%) were obtained, except for in the case of the erythromycin-resistant phenotype. This study contributes significantly to the knowledge of  genomics for the prevention, treatment, and control of infections caused by this pathogen.IMPORTANCEDespite being the pathogen with the greatest number of gastroenteritis cases worldwide,  remains a poorly studied microorganism. A sustained increase in fluoroquinolone resistance in human isolates is a problem when treating  infections. The development of whole genome sequencing (WGS) techniques has allowed us to better understand the genotypic characteristics of this pathogen and relate them to antibiotic resistance phenotypes. These techniques complement the data obtained from the phenotypic analysis of  isolates. The zoonotic transmission of  through the consumption of contaminated poultry supports approaching the study of this pathogen through &quot;One Health&quot; approach. In addition, due to the limited information on the genomic characteristics of  in Spain, this study provides important data and allows us to compare the results with those obtained in other countries.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Moura, David S</style></author><author><style face="normal" font="default" size="100%">Mondaza-Hernandez, Jose L</style></author><author><style face="normal" font="default" size="100%">Sanchez-Bustos, Paloma</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Cordero-Varela, Juan A</style></author><author><style face="normal" font="default" size="100%">Lopez-Alvarez, Maria</style></author><author><style face="normal" font="default" size="100%">Carrillo-Garcia, Jaime</style></author><author><style face="normal" font="default" size="100%">Martin-Ruiz, Marta</style></author><author><style face="normal" font="default" size="100%">Romero-Gonzalez, Pablo</style></author><author><style face="normal" font="default" size="100%">Renshaw-Calderon, Marta</style></author><author><style face="normal" font="default" size="100%">Ramos, Rafael</style></author><author><style face="normal" font="default" size="100%">Marcilla, David</style></author><author><style face="normal" font="default" size="100%">Alvarez-Alegret, Ramiro</style></author><author><style face="normal" font="default" size="100%">Agra-Pujol, Carolina</style></author><author><style face="normal" font="default" size="100%">Izquierdo, Francisco</style></author><author><style face="normal" font="default" size="100%">Ortega-Medina, Luis</style></author><author><style face="normal" font="default" size="100%">Martin-Davila, Francisco</style></author><author><style face="normal" font="default" size="100%">Hernandez-Leon, Carmen Nieves</style></author><author><style face="normal" font="default" size="100%">Romagosa, Cleofe</style></author><author><style face="normal" font="default" size="100%">Salgado, Maria Angeles Vaz</style></author><author><style face="normal" font="default" size="100%">Lavernia, Javier</style></author><author><style face="normal" font="default" size="100%">Bagué, Silvia</style></author><author><style face="normal" font="default" size="100%">Mayodormo-Aranda, Empar</style></author><author><style face="normal" font="default" size="100%">Alvarez, Rosa</style></author><author><style face="normal" font="default" size="100%">Valverde, Claudia</style></author><author><style face="normal" font="default" size="100%">Martinez-Trufero, Javier</style></author><author><style face="normal" font="default" size="100%">Castilla-Ramirez, Carolina</style></author><author><style face="normal" font="default" size="100%">Gutierrez, Antonio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Hindi, Nadia</style></author><author><style face="normal" font="default" size="100%">Garcia-Foncillas, Jesus</style></author><author><style face="normal" font="default" size="100%">Martin-Broto, Javier</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">HMGA1 regulates trabectedin sensitivity in advanced soft-tissue sarcoma (STS): A Spanish Group for Research on Sarcomas (GEIS) study.</style></title><secondary-title><style face="normal" font="default" size="100%">Cell Mol Life Sci</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cell Mol Life Sci</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Antineoplastic Agents, Alkylating</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug Resistance, Neoplasm</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">HMGA1a Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Leiomyosarcoma</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Prognosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Sarcoma</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">TOR Serine-Threonine Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Trabectedin</style></keyword><keyword><style  face="normal" font="default" size="100%">Xenograft Model Antitumor Assays</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 May 17</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">81</style></volume><pages><style face="normal" font="default" size="100%">219</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;HMGA1 is a structural epigenetic chromatin factor that has been associated with tumor progression and drug resistance. Here, we reported the prognostic/predictive value of HMGA1 for trabectedin in advanced soft-tissue sarcoma (STS) and the effect of inhibiting HMGA1 or the mTOR downstream pathway in trabectedin activity. The prognostic/predictive value of HMGA1 expression was assessed in a cohort of 301 STS patients at mRNA (n = 133) and protein level (n = 272), by HTG EdgeSeq transcriptomics and immunohistochemistry, respectively. The effect of HMGA1 silencing on trabectedin activity and gene expression profiling was measured in leiomyosarcoma cells. The effect of combining mTOR inhibitors with trabectedin was assessed on cell viability in vitro studies, whereas in vivo studies tested the activity of this combination. HMGA1 mRNA and protein expression were significantly associated with worse progression-free survival of trabectedin and worse overall survival in STS. HMGA1 silencing sensitized leiomyosarcoma cells for trabectedin treatment, reducing the spheroid area and increasing cell death. The downregulation of HGMA1 significantly decreased the enrichment of some specific gene sets, including the PI3K/AKT/mTOR pathway. The inhibition of mTOR, sensitized leiomyosarcoma cultures for trabectedin treatment, increasing cell death. In in vivo studies, the combination of rapamycin with trabectedin downregulated HMGA1 expression and stabilized tumor growth of 3-methylcholantrene-induced sarcoma-like models. HMGA1 is an adverse prognostic factor for trabectedin treatment in advanced STS. HMGA1 silencing increases trabectedin efficacy, in part by modulating the mTOR signaling pathway. Trabectedin plus mTOR inhibitors are active in preclinical models of sarcoma, downregulating HMGA1 expression levels and stabilizing tumor growth.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mavillard, Fabiola</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco M</style></author><author><style face="normal" font="default" size="100%">Valladares, Amador</style></author><author><style face="normal" font="default" size="100%">Álvarez-Villegas, Miren L</style></author><author><style face="normal" font="default" size="100%">Roldán, Gema</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Soriano, Manuel</style></author><author><style face="normal" font="default" size="100%">Susarte, Santiago</style></author><author><style face="normal" font="default" size="100%">Fuentes, Pilar</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Nuñez-Negrillo, Ana María</style></author><author><style face="normal" font="default" size="100%">Carvajal, Alejandra</style></author><author><style face="normal" font="default" size="100%">Morgado, Yolanda</style></author><author><style face="normal" font="default" size="100%">Arteaga, Daniel</style></author><author><style face="normal" font="default" size="100%">Ufano, Rosa</style></author><author><style face="normal" font="default" size="100%">Mir, Pablo</style></author><author><style face="normal" font="default" size="100%">Gamella, Juan F</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Paradas, Carmen</style></author><author><style face="normal" font="default" size="100%">Cabrera-Serrano, Macarena</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Iberian Roma Population Variant Server (IRPVS).</style></title><secondary-title><style face="normal" font="default" size="100%">J Genet Genomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Genet Genomics</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Mar 26</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Robles, Enrique A</style></author><author><style face="normal" font="default" size="100%">Lara, María</style></author><author><style face="normal" font="default" size="100%">Aguado, Andrea</style></author><author><style face="normal" font="default" size="100%">Rodríguez Iglesias, Manuel A</style></author><author><style face="normal" font="default" size="100%">Lepe, Jose A</style></author><author><style face="normal" font="default" size="100%">García, Federico</style></author><author><style face="normal" font="default" size="100%">Pérez-Alegre, Mónica</style></author><author><style face="normal" font="default" size="100%">Andújar, Eloísa</style></author><author><style face="normal" font="default" size="100%">Jiménez, Victoria E</style></author><author><style face="normal" font="default" size="100%">Camino, Lola P</style></author><author><style face="normal" font="default" size="100%">Loruso, Nicola</style></author><author><style face="normal" font="default" size="100%">Ameyugo, Ulises</style></author><author><style face="normal" font="default" size="100%">Vazquez, Isabel María</style></author><author><style face="normal" font="default" size="100%">Lozano, Carlota M</style></author><author><style face="normal" font="default" size="100%">Chaves, J Alberto</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The integrated genomic surveillance system of Andalusia (SIEGA) provides a One Health regional resource connected with the clinic.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug Resistance, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">One Health</style></keyword><keyword><style  face="normal" font="default" size="100%">Virulence Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Aug 19</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">19200</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The One Health approach, recognizing the interconnectedness of human, animal, and environmental health, has gained significance amid emerging zoonotic diseases and antibiotic resistance concerns. This paper aims to demonstrate the utility of a collaborative tool, the SIEGA, for monitoring infectious diseases across domains, fostering a comprehensive understanding of disease dynamics and risk factors, highlighting the pivotal role of One Health surveillance systems. Raw whole-genome sequencing is processed through different species-specific open software that additionally reports the presence of genes associated to anti-microbial resistances and virulence. The SIEGA application is a Laboratory Information Management System, that allows customizing reports, detect transmission chains, and promptly alert on alarming genetic similarities. The SIEGA initiative has successfully accumulated a comprehensive collection of more than 1900 bacterial genomes, including Salmonella enterica, Listeria monocytogenes, Campylobacter jejuni, Escherichia coli, Yersinia enterocolitica and Legionella pneumophila, showcasing its potential in monitoring pathogen transmission, resistance patterns, and virulence factors. SIEGA enables customizable reports and prompt detection of transmission chains, highlighting its contribution to enhancing vigilance and response capabilities. Here we show the potential of genomics in One Health surveillance when supported by an appropriate bioinformatic tool. By facilitating precise disease control strategies and antimicrobial resistance management, SIEGA enhances global health security and reduces the burden of infectious diseases. The integration of health data from humans, animals, and the environment, coupled with advanced genomics, underscores the importance of a holistic One Health approach in mitigating health threats.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Lara, María</style></author><author><style face="normal" font="default" size="100%">Camacho-Martinez, Pedro</style></author><author><style face="normal" font="default" size="100%">Merino-Diaz, Laura</style></author><author><style face="normal" font="default" size="100%">Pupo-Ledo, Inmaculada</style></author><author><style face="normal" font="default" size="100%">de Salazar, Adolfo</style></author><author><style face="normal" font="default" size="100%">Fuentes, Ana</style></author><author><style face="normal" font="default" size="100%">Viñuela, Laura</style></author><author><style face="normal" font="default" size="100%">Chueca, Natalia</style></author><author><style face="normal" font="default" size="100%">Martinez-Martinez, Luis</style></author><author><style face="normal" font="default" size="100%">Lorusso, Nicola</style></author><author><style face="normal" font="default" size="100%">Lepe, Jose A</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">García, Federico</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Molecular and phylogenetic characterization of the monkeypox outbreak in the South of Spain.</style></title><secondary-title><style face="normal" font="default" size="100%">Health Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Health Sci Rep</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Mar</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">e1965</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND AND AIM: &lt;/b&gt;Until the May 2022 Monkeypox (MPXV) outbreak, which spread rapidly to many non-endemic countries, the virus was considered a viral zoonosis limited to some African countries. The Andalusian circuit of genomic surveillance was rapidly applied to characterize the MPXV outbreak in the South of Spain.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Whole genome sequencing was used to obtain the genomic profiles of samples collected across the south of Spain, representative of all the provinces of Andalusia. Phylogenetic analysis was used to study the relationship of the isolates and the available sequences of the 2022 outbreak.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Whole genome sequencing of a total of 160 MPXV viruses from the different provinces that reported cases were obtained. Interestingly, we report the sequences of MPXV viruses obtained from two patients who died. While one of the isolates bore no noteworthy mutations that explain a potential heightened virulence, in another patient the second consecutive genome sequence, performed after the administration of tecovirimat, uncovered a mutation within the A0A7H0DN30 gene, known to be a prime target for tecovirimat in its Vaccinia counterpart. In general, a low number of mutations were observed in the sequences reported, which were very similar to the reference of the 2022 outbreak (OX044336), as expected from a DNA virus. The samples likely correspond to several introductions of the circulating MPXV viruses from the last outbreak. The virus sequenced from one of the two patients that died presented a mutation in a gene that bears potential connections to drug resistance. This mutation was absent in the initial sequencing before treatment.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pérez-Gutiérrez, Ana M</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Cervilla, Jorge A</style></author><author><style face="normal" font="default" size="100%">Gutiérrez, Blanca</style></author><author><style face="normal" font="default" size="100%">Molina, Esther</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Zarza-Rebollo, Juan Antonio</style></author><author><style face="normal" font="default" size="100%">López-Isac, Elena</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Martinez-Gonzalez, Luis Javier</style></author><author><style face="normal" font="default" size="100%">Rivera, Margarita</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Psychiatry</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Psychiatry</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 May 28</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Baz-Redón, Noelia</style></author><author><style face="normal" font="default" size="100%">Sánchez-Bellver, Laura</style></author><author><style face="normal" font="default" size="100%">Fernández-Cancio, Mónica</style></author><author><style face="normal" font="default" size="100%">Rovira-Amigo, Sandra</style></author><author><style face="normal" font="default" size="100%">Burgoyne, Thomas</style></author><author><style face="normal" font="default" size="100%">Ranjit, Rai</style></author><author><style face="normal" font="default" size="100%">Aquino, Virginia</style></author><author><style face="normal" font="default" size="100%">Toro-Barrios, Noemí</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Polverino, Eva</style></author><author><style face="normal" font="default" size="100%">Cols, Maria</style></author><author><style face="normal" font="default" size="100%">Moreno-Galdó, Antonio</style></author><author><style face="normal" font="default" size="100%">Camats-Tarruella, Núria</style></author><author><style face="normal" font="default" size="100%">Marfany, Gemma</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Primary Ciliary Dyskinesia and Retinitis Pigmentosa: Novel  Variant and Possible Modifier Gene.</style></title><secondary-title><style face="normal" font="default" size="100%">Cells</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cells</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Ciliary Motility Disorders</style></keyword><keyword><style  face="normal" font="default" size="100%">Eye Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Modifier</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Retinitis pigmentosa</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Mar 16</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We report a novel  missense variant co-segregated with a familial X-linked retinitis pigmentosa (XLRP) case. The brothers were hemizygous for this variant, but only the proband presented with primary ciliary dyskinesia (PCD). Thus, we aimed to elucidate the role of the  variant and other modifier genes in the phenotypic variability observed in the family and its impact on motile cilia. The pathogenicity of the variant on the RPGR protein was evaluated by in vitro studies transiently transfecting the mutated  gene, and immunofluorescence analysis on nasal brushing samples. Whole-exome sequencing was conducted to identify potential modifier variants. In vitro studies showed that the mutated RPGR protein could not localise to the cilium and impaired cilium formation. Accordingly, RPGR was abnormally distributed in the siblings' nasal brushing samples. In addition, a missense variant in  was identified. The concurrent  variant influenced ciliary mislocalisation of the protein. We provide a comprehensive characterisation of motile cilia in this XLRP family, with only the proband presenting PCD symptoms. The variant's pathogenicity was confirmed, although it alone does not explain the respiratory symptoms. Finally, the  gene may be a potential modifier for respiratory symptoms in patients with  mutations.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Blasco, Lucia</style></author><author><style face="normal" font="default" size="100%">López-Hernández, Inmaculada</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Fernández, Miguel</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Case report: Analysis of phage therapy failure in a patient with a Pseudomonas aeruginosa prosthetic vascular graft infection</style></title><secondary-title><style face="normal" font="default" size="100%">Front Med (Lausanne)</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Front Med (Lausanne)</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10235614/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">1199657</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Clinical case of a patient with a  multidrug-resistant prosthetic vascular graft infection which was treated with a cocktail of phages (PT07, 14/01, and PNM) in combination with ceftazidime-avibactam (CZA). After the application of the phage treatment and in absence of antimicrobial therapy, a new  bloodstream infection (BSI) with a septic residual limb metastasis occurred, now involving a wild-type strain being susceptible to ß-lactams and quinolones. Clinical strains were analyzed by microbiology and whole genome sequencing techniques. In relation with phage administration, the clinical isolates of  before phage therapy (HE2011471) and post phage therapy (HE2105886) showed a clonal relationship but with important genomic changes which could be involved in the resistance to this therapy. Finally, phenotypic studies showed a decrease in Minimum Inhibitory Concentration (MIC) to ß-lactams and quinolones as well as an increase of the biofilm production and phage resistant mutants in the clinical isolate of  post phage therapy.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Crosstalk between Metabolite Production and Signaling Activity in Breast Cancer.</style></title><secondary-title><style face="normal" font="default" size="100%">Int J Mol Sci</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Int J Mol Sci</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Apr 18</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">24</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The reprogramming of metabolism is a recognized cancer hallmark. It is well known that different signaling pathways regulate and orchestrate this reprogramming that contributes to cancer initiation and development. However, recent evidence is accumulating, suggesting that several metabolites could play a relevant role in regulating signaling pathways. To assess the potential role of metabolites in the regulation of signaling pathways, both metabolic and signaling pathway activities of Breast invasive Carcinoma (BRCA) have been modeled using mechanistic models. Gaussian Processes, powerful machine learning methods, were used in combination with SHapley Additive exPlanations (SHAP), a recent methodology that conveys causality, to obtain potential causal relationships between the production of metabolites and the regulation of signaling pathways. A total of 317 metabolites were found to have a strong impact on signaling circuits. The results presented here point to the existence of a complex crosstalk between signaling and metabolic pathways more complex than previously was thought.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Roldán, Gema</style></author><author><style face="normal" font="default" size="100%">Fernandez-Rueda, Jose L</style></author><author><style face="normal" font="default" size="100%">Bostelmann, Gerrit</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Aquino, Virginia</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco</style></author><author><style face="normal" font="default" size="100%">Pita, Guillermo</style></author><author><style face="normal" font="default" size="100%">Núñez-Torres, Rocío</style></author><author><style face="normal" font="default" size="100%">González-Neira, Anna</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">CSVS Crowdsourcing Group</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">A crowdsourcing database for the copy-number variation of the Spanish population.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Genomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Genomics</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Mar 09</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Despite being a very common type of genetic variation, the distribution of copy-number variations (CNVs) in the population is still poorly understood. The knowledge of the genetic variability, especially at the level of the local population, is a critical factor for distinguishing pathogenic from non-pathogenic variation in the discovery of new disease variants.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Here, we present the SPAnish Copy Number Alterations Collaborative Server (SPACNACS), which currently contains copy number variation profiles obtained from more than 400 genomes and exomes of unrelated Spanish individuals. By means of a collaborative crowdsourcing effort whole genome and whole exome sequencing data, produced by local genomic projects and for other purposes, is continuously collected. Once checked both, the Spanish ancestry and the lack of kinship with other individuals in the SPACNACS, the CNVs are inferred for these sequences and they are used to populate the database. A web interface allows querying the database with different filters that include ICD10 upper categories. This allows discarding samples from the disease under study and obtaining pseudo-control CNV profiles from the local population. We also show here additional studies on the local impact of CNVs in some phenotypes and on pharmacogenomic variants. SPACNACS can be accessed at: http://csvs.clinbioinfosspa.es/spacnacs/ .&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;SPACNACS facilitates disease gene discovery by providing detailed information of the local variability of the population and exemplifies how to reuse genomic data produced for other purposes to build a local reference database.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pellegrinelli, Vanessa</style></author><author><style face="normal" font="default" size="100%">Figueroa-Juárez, Elizabeth</style></author><author><style face="normal" font="default" size="100%">Samuelson, Isabella</style></author><author><style face="normal" font="default" size="100%">U-Din, Mueez</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Fdez, Sonia</style></author><author><style face="normal" font="default" size="100%">Virtue, Samuel</style></author><author><style face="normal" font="default" size="100%">Leggat, Jennifer</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Peirce, Vivian J</style></author><author><style face="normal" font="default" size="100%">Niemi, Tarja</style></author><author><style face="normal" font="default" size="100%">Campbell, Mark</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Cuenca, Sergio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Carobbio, Stefania</style></author><author><style face="normal" font="default" size="100%">Virtanen, Kirsi A</style></author><author><style face="normal" font="default" size="100%">Vidal-Puig, Antonio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Defective extracellular matrix remodeling in brown adipose tissue is associated with fibro-inflammation and reduced diet-induced thermogenesis.</style></title><secondary-title><style face="normal" font="default" size="100%">Cell Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cell Rep</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Jun 13</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">112640</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The relevance of extracellular matrix (ECM) remodeling is reported in white adipose tissue (AT) and obesity-related dysfunctions, but little is known about the importance of ECM remodeling in brown AT (BAT) function. Here, we show that a time course of high-fat diet (HFD) feeding progressively impairs diet-induced thermogenesis concomitantly with the development of fibro-inflammation in BAT. Higher markers of fibro-inflammation are associated with lower cold-induced BAT activity in humans. Similarly, when mice are housed at thermoneutrality, inactivated BAT features fibro-inflammation. We validate the pathophysiological relevance of BAT ECM remodeling in response to temperature challenges and HFD using a model of a primary defect in the collagen turnover mediated by partial ablation of the Pepd prolidase. Pepd-heterozygous mice display exacerbated dysfunction and BAT fibro-inflammation at thermoneutrality and in HFD. Our findings show the relevance of ECM remodeling in BAT activation and provide a mechanism for BAT dysfunction in obesity.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco</style></author><author><style face="normal" font="default" size="100%">Fernandez-Rueda, Jose L</style></author><author><style face="normal" font="default" size="100%">Aguado, Andrea</style></author><author><style face="normal" font="default" size="100%">Lara, María</style></author><author><style face="normal" font="default" size="100%">Riazzo, Cristina</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Iglesias, Manuel A</style></author><author><style face="normal" font="default" size="100%">Camacho-Martinez, Pedro</style></author><author><style face="normal" font="default" size="100%">Merino-Diaz, Laura</style></author><author><style face="normal" font="default" size="100%">Pupo-Ledo, Inmaculada</style></author><author><style face="normal" font="default" size="100%">de Salazar, Adolfo</style></author><author><style face="normal" font="default" size="100%">Viñuela, Laura</style></author><author><style face="normal" font="default" size="100%">Fuentes, Ana</style></author><author><style face="normal" font="default" size="100%">Chueca, Natalia</style></author><author><style face="normal" font="default" size="100%">García, Federico</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Lepe, Jose A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Detection of High Level of Co-Infection and the Emergence of Novel SARS CoV-2 Delta-Omicron and Omicron-Omicron Recombinants in the Epidemiological Surveillance of Andalusia.</style></title><secondary-title><style face="normal" font="default" size="100%">Int J Mol Sci</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Int J Mol Sci</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Jan 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">24</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Recombination is an evolutionary strategy to quickly acquire new viral properties inherited from the parental lineages. The systematic survey of the SARS-CoV-2 genome sequences of the Andalusian genomic surveillance strategy has allowed the detection of an unexpectedly high number of co-infections, which constitute the ideal scenario for the emergence of new recombinants. Whole genome sequence of SARS-CoV-2 has been carried out as part of the genomic surveillance programme. Sample sources included the main hospitals in the Andalusia region. In addition to the increase of co-infections and known recombinants, three novel SARS-CoV-2 delta-omicron and omicron-omicron recombinant variants with two break points have been detected. Our observations document an epidemiological scenario in which co-infection and recombination are detected more frequently. Finally, we describe a family case in which co-infection is followed by the detection of a recombinant made from the two co-infecting variants. This increased number of recombinants raises the risk of emergence of recombinant variants with increased transmissibility and pathogenicity.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chaves-Blanco, Lucía</style></author><author><style face="normal" font="default" size="100%">de Salazar, Adolfo</style></author><author><style face="normal" font="default" size="100%">Fuentes, Ana</style></author><author><style face="normal" font="default" size="100%">Viñuela, Laura</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">García, Federico</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluation of a combined detection of SARS-CoV-2 and its variants using real-time allele-specific PCR strategy: an advantage for clinical practice.</style></title><secondary-title><style face="normal" font="default" size="100%">Epidemiol Infect</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Epidemiol Infect</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alleles</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19 Testing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Real-Time Polymerase Chain Reaction</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity and Specificity</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Nov 24</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">151</style></volume><pages><style face="normal" font="default" size="100%">e201</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This study aimed to assess the ability of a real-time reverse transcription polymerase chain reaction (RT-PCR) with multiple targets to detect SARS-CoV-2 and its variants in a single test. Nasopharyngeal specimens were collected from patients in Granada, Spain, between January 2021 and December 2022. Five allele-specific RT-PCR kits were used sequentially, with each kit designed to detect a predominant variant at the time. When the Alpha variant was dominant, the kit included the HV69/70 deletion, E and N genes. When Delta replaced Alpha, the kit incorporated the L452R mutation in addition to E and N genes. When Omicron became dominant, L452R was replaced with the N679K mutation. Before incorporating each variant kit, a comparative analysis was carried out with SARS-CoV-2 whole genome sequencing (WGS). The results demonstrated that RT-PCR with multiple targets can provide rapid and effective detection of SARS-CoV-2 and its variants in a single test. A very high degree of agreement (96.2%) was obtained between the comparison of RT-PCR and WGS. Allele-specific RT-PCR assays make it easier to implement epidemiological surveillance systems for effective public health decision making.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Bostelmann, Gerrit</style></author><author><style face="normal" font="default" size="100%">Muñoyerro-Muñiz, Dolores</style></author><author><style face="normal" font="default" size="100%">Villegas, Román</style></author><author><style face="normal" font="default" size="100%">Gonzalez-Manzanares, Rafael</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Anguita, Manuel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evidence of the association between increased use of direct oral anticoagulants and a reduction in the rate of atrial fibrillation-related stroke and major bleeding at the population level (2012-2019).</style></title><secondary-title><style face="normal" font="default" size="100%">Med Clin (Barc)</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Med Clin (Barc)</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Nov 20</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;The introduction of direct-acting oral anticoagulants (DOACs) has shown to decrease atrial fibrillation (AF)-related stroke and bleeding rates in clinical studies, but there is no certain evidence about their effects at the population level. Our aim was to assess changes in AF-related stroke and major bleeding rates between 2012 and 2019 in Andalusia (Spain), and the association between DOACs use and events rates at the population level.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;All patients with an AF diagnosis from 2012 to 2019 were identified using the Andalusian Health Population Base, that provides clinical information on all Andalusian people. Annual ischemic and hemorrhagic stroke, major bleeding rates, and used antithrombotic treatments were determined. Marginal hazard ratios (HR) were calculated for each treatment.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;A total of 95,085 patients with an AF diagnosis were identified. Mean age was 76.1±10.2 years (49.7% women). An increase in the use of DOACs was observed throughout the study period in both males and females (p&lt;0.001). The annual rate of ischemic stroke decreased by one third, while that of hemorrhagic stroke and major bleeding decreased 2-3-fold from 2012 to 2019. Marginal HR was lower than 0.50 for DOACs compared to VKA for all ischemic or hemorrhagic events.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;In this contemporary population-based study using clinical and administrative databases in Andalusia, a significant reduction in the incidence of AF-related ischemic and hemorrhagic stroke and major bleeding was observed between 2012 and 2019. The increased use of DOACs seems to be associated with this reduction.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sola-García, Alejandro</style></author><author><style face="normal" font="default" size="100%">Cáliz-Molina, María Ángeles</style></author><author><style face="normal" font="default" size="100%">Espadas, Isabel</style></author><author><style face="normal" font="default" size="100%">Petr, Michael</style></author><author><style face="normal" font="default" size="100%">Panadero-Morón, Concepción</style></author><author><style face="normal" font="default" size="100%">González-Morán, Daniel</style></author><author><style face="normal" font="default" size="100%">Martín-Vázquez, María Eugenia</style></author><author><style face="normal" font="default" size="100%">Narbona-Pérez, Álvaro Jesús</style></author><author><style face="normal" font="default" size="100%">López-Noriega, Livia</style></author><author><style face="normal" font="default" size="100%">Martínez-Corrales, Guillermo</style></author><author><style face="normal" font="default" size="100%">López-Fernández-Sobrino, Raúl</style></author><author><style face="normal" font="default" size="100%">Carmona-Marin, Lina M</style></author><author><style face="normal" font="default" size="100%">Martínez-Force, Enrique</style></author><author><style face="normal" font="default" size="100%">Yanes, Oscar</style></author><author><style face="normal" font="default" size="100%">Vinaixa, Maria</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Reyes, José Carlos</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Martín, Franz</style></author><author><style face="normal" font="default" size="100%">Gauthier, Benoit R</style></author><author><style face="normal" font="default" size="100%">Scheibye-Knudsen, Morten</style></author><author><style face="normal" font="default" size="100%">Capilla-González, Vivian</style></author><author><style face="normal" font="default" size="100%">Martín-Montalvo, Alejandro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Metabolic reprogramming by Acly inhibition using SB-204990 alters glucoregulation and modulates molecular mechanisms associated with aging.</style></title><secondary-title><style face="normal" font="default" size="100%">Commun Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Commun Biol</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Mar 08</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">250</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;ATP-citrate lyase is a central integrator of cellular metabolism in the interface of protein, carbohydrate, and lipid metabolism. The physiological consequences as well as the molecular mechanisms orchestrating the response to long-term pharmacologically induced Acly inhibition are unknown. We report here that the Acly inhibitor SB-204990 improves metabolic health and physical strength in wild-type mice when fed with a high-fat diet, while in mice fed with healthy diet results in metabolic imbalance and moderated insulin resistance. By applying a multiomic approach using untargeted metabolomics, transcriptomics, and proteomics, we determined that, in vivo, SB-204990 plays a role in the regulation of molecular mechanisms associated with aging, such as energy metabolism, mitochondrial function, mTOR signaling, and folate cycle, while global alterations on histone acetylation are absent. Our findings indicate a mechanism for regulating molecular pathways of aging that prevents the development of metabolic abnormalities associated with unhealthy dieting. This strategy might be explored for devising therapeutic approaches to prevent metabolic diseases.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Corrales, Patricia</style></author><author><style face="normal" font="default" size="100%">Martin-Taboada, Marina</style></author><author><style face="normal" font="default" size="100%">Vivas-García, Yurena</style></author><author><style face="normal" font="default" size="100%">Torres, Lucia</style></author><author><style face="normal" font="default" size="100%">Ramirez-Jimenez, Laura</style></author><author><style face="normal" font="default" size="100%">Lopez, Yamila</style></author><author><style face="normal" font="default" size="100%">Horrillo, Daniel</style></author><author><style face="normal" font="default" size="100%">Vila-Bedmar, Rocio</style></author><author><style face="normal" font="default" size="100%">Barber-Cano, Eloisa</style></author><author><style face="normal" font="default" size="100%">Izquierdo-Lahuerta, Adriana</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Martínez, Carmen</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Ros, Manuel</style></author><author><style face="normal" font="default" size="100%">Medina-Gomez, Gema</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">microRNAs-mediated regulation of insulin signaling in white adipose tissue during aging: Role of caloric restriction.</style></title><secondary-title><style face="normal" font="default" size="100%">Aging Cell</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Aging Cell</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Jul 04</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">e13919</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Caloric restriction is a non-pharmacological intervention known to ameliorate the metabolic defects associated with aging, including insulin resistance. The levels of miRNA expression may represent a predictive tool for aging-related alterations. In order to investigate the role of miRNAs underlying insulin resistance in adipose tissue during the early stages of aging, 3- and 12-month-old male animals fed ad libitum, and 12-month-old male animals fed with a 20% caloric restricted diet were used. In this work we demonstrate that specific miRNAs may contribute to the impaired insulin-stimulated glucose metabolism specifically in the subcutaneous white adipose tissue, through the regulation of target genes implicated in the insulin signaling cascade. Moreover, the expression of these miRNAs is modified by caloric restriction in middle-aged animals, in accordance with the improvement of the metabolic state. Overall, our work demonstrates that alterations in posttranscriptional gene expression because of miRNAs dysregulation might represent an endogenous mechanism by which insulin response in the subcutaneous fat depot is already affected at middle age. Importantly, caloric restriction could prevent this modulation, demonstrating that certain miRNAs could constitute potential biomarkers of age-related metabolic alterations.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Bostelmann, Gerrit</style></author><author><style face="normal" font="default" size="100%">Muñoyerro-Muñiz, Dolores</style></author><author><style face="normal" font="default" size="100%">Villegas, Román</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Real-world evidence with a retrospective cohort of 15,968 COVID-19 hospitalized patients suggests 21 new effective treatments.</style></title><secondary-title><style face="normal" font="default" size="100%">Virol J</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Virol J</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Oct 06</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">226</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;PURPOSE: &lt;/b&gt;Despite the extensive vaccination campaigns in many countries, COVID-19 is still a major worldwide health problem because of its associated morbidity and mortality. Therefore, finding efficient treatments as fast as possible is a pressing need. Drug repurposing constitutes a convenient alternative when the need for new drugs in an unexpected medical scenario is urgent, as is the case with COVID-19.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Using data from a central registry of electronic health records (the Andalusian Population Health Database), the effect of prior consumption of drugs for other indications previous to the hospitalization with respect to patient outcomes, including survival and lymphocyte progression, was studied on a retrospective cohort of 15,968 individuals, comprising all COVID-19 patients hospitalized in Andalusia between January and November 2020.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Covariate-adjusted hazard ratios and analysis of lymphocyte progression curves support a significant association between consumption of 21 different drugs and better patient survival. Contrarily, one drug, furosemide, displayed a significant increase in patient mortality.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;In this study we have taken advantage of the availability of a regional clinical database to study the effect of drugs, which patients were taking for other indications, on their survival. The large size of the database allowed us to control covariates effectively.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><translated-authors><author><style face="normal" font="default" size="100%">COVID-19 Host Genetics Initiative</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">A second update on mapping the human genetic architecture of COVID-19.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nature</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Human Genetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Sep</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">621</style></volume><pages><style face="normal" font="default" size="100%">E7-E26</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">7977</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gawron, Piotr</style></author><author><style face="normal" font="default" size="100%">Hoksza, David</style></author><author><style face="normal" font="default" size="100%">Piñero, Janet</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Fernandez-Rueda, Jose Luis</style></author><author><style face="normal" font="default" size="100%">Colonna, Vincenza</style></author><author><style face="normal" font="default" size="100%">Smula, Ewa</style></author><author><style face="normal" font="default" size="100%">Heirendt, Laurent</style></author><author><style face="normal" font="default" size="100%">Ancien, François</style></author><author><style face="normal" font="default" size="100%">Grouès, Valentin</style></author><author><style face="normal" font="default" size="100%">Satagopam, Venkata P</style></author><author><style face="normal" font="default" size="100%">Schneider, Reinhard</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Furlong, Laura I</style></author><author><style face="normal" font="default" size="100%">Ostaszewski, Marek</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualization of automatically combined disease maps and pathway diagrams for rare diseases.</style></title><secondary-title><style face="normal" font="default" size="100%">Front Bioinform</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Front Bioinform</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">1101505</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt; Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower.  In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer.  We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets.  In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco M</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Bostelmann, Gerrit</style></author><author><style face="normal" font="default" size="100%">Martínez-González, L Javier</style></author><author><style face="normal" font="default" size="100%">Muñoyerro-Muñiz, Dolores</style></author><author><style face="normal" font="default" size="100%">Villegas, Román</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Baño, Jesús</style></author><author><style face="normal" font="default" size="100%">Romero-Gómez, Manuel</style></author><author><style face="normal" font="default" size="100%">Lorusso, Nicola</style></author><author><style face="normal" font="default" size="100%">Garcia-León, Javier</style></author><author><style face="normal" font="default" size="100%">Navarro-Marí, Jose M</style></author><author><style face="normal" font="default" size="100%">Camacho-Martinez, Pedro</style></author><author><style face="normal" font="default" size="100%">Merino-Diaz, Laura</style></author><author><style face="normal" font="default" size="100%">Salazar, Adolfo de</style></author><author><style face="normal" font="default" size="100%">Viñuela, Laura</style></author><author><style face="normal" font="default" size="100%">Lepe, Jose A</style></author><author><style face="normal" font="default" size="100%">García, Federico</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessing the Impact of SARS-CoV-2 Lineages and Mutations on Patient Survival.</style></title><secondary-title><style face="normal" font="default" size="100%">Viruses</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Viruses</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Viral</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Pandemics</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 Aug 27</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;OBJECTIVES: &lt;/b&gt;More than two years into the COVID-19 pandemic, SARS-CoV-2 still remains a global public health problem. Successive waves of infection have produced new SARS-CoV-2 variants with new mutations for which the impact on COVID-19 severity and patient survival is uncertain.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;A total of 764 SARS-CoV-2 genomes, sequenced from COVID-19 patients, hospitalized from 19th February 2020 to 30 April 2021, along with their clinical data, were used for survival analysis.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;A significant association of B.1.1.7, the alpha lineage, with patient mortality (log hazard ratio (LHR) = 0.51, C.I. = [0.14,0.88]) was found upon adjustment by all the covariates known to affect COVID-19 prognosis. Moreover, survival analysis of mutations in the SARS-CoV-2 genome revealed 27 of them were significantly associated with higher mortality of patients. Most of these mutations were located in the genes coding for the S, ORF8, and N proteins.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;This study illustrates how a combination of genomic and clinical data can provide solid evidence for the impact of viral lineage on patient survival.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luque, Juan</style></author><author><style face="normal" font="default" size="100%">Mendes, Ingrid</style></author><author><style face="normal" font="default" size="100%">Gómez, Beatriz</style></author><author><style face="normal" font="default" size="100%">Morte, Beatriz</style></author><author><style face="normal" font="default" size="100%">de Heredia, Miguel López</style></author><author><style face="normal" font="default" size="100%">Herreras, Enrique</style></author><author><style face="normal" font="default" size="100%">Corrochano, Virginia</style></author><author><style face="normal" font="default" size="100%">Bueren, Juan</style></author><author><style face="normal" font="default" size="100%">Gallano, Pia</style></author><author><style face="normal" font="default" size="100%">Artuch, Rafael</style></author><author><style face="normal" font="default" size="100%">Fillat, Cristina</style></author><author><style face="normal" font="default" size="100%">Pérez-Jurado, Luis A</style></author><author><style face="normal" font="default" size="100%">Montoliu, Lluis</style></author><author><style face="normal" font="default" size="100%">Carracedo, Ángel</style></author><author><style face="normal" font="default" size="100%">Millán, José M</style></author><author><style face="normal" font="default" size="100%">Webb, Susan M</style></author><author><style face="normal" font="default" size="100%">Palau, Francesc</style></author><author><style face="normal" font="default" size="100%">Lapunzina, Pablo</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">CIBERER Network</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">CIBERER: Spanish National Network for Research on Rare Diseases: a highly productive collaborative initiative.</style></title><secondary-title><style face="normal" font="default" size="100%">Clin Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Clin Genet</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 Jan 20</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on Rare Diseases currently consists of 75 research groups belonging to universities, research centers and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical and cellular research of rare diseases. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this paper, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions towards the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to rare disease research. This article is protected by copyright. All rights reserved.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Torrent-Vernetta, Alba</style></author><author><style face="normal" font="default" size="100%">Gaboli, Mirella</style></author><author><style face="normal" font="default" size="100%">Castillo-Corullón, Silvia</style></author><author><style face="normal" font="default" size="100%">Mondéjar-López, Pedro</style></author><author><style face="normal" font="default" size="100%">Sanz Santiago, Verónica</style></author><author><style face="normal" font="default" size="100%">Costa-Colomer, Jordi</style></author><author><style face="normal" font="default" size="100%">Osona, Borja</style></author><author><style face="normal" font="default" size="100%">Torres-Borrego, Javier</style></author><author><style face="normal" font="default" size="100%">de la Serna-Blázquez, Olga</style></author><author><style face="normal" font="default" size="100%">Bellón Alonso, Sara</style></author><author><style face="normal" font="default" size="100%">Caro Aguilera, Pilar</style></author><author><style face="normal" font="default" size="100%">Gimeno-Díaz de Atauri, Álvaro</style></author><author><style face="normal" font="default" size="100%">Valenzuela Soria, Alfredo</style></author><author><style face="normal" font="default" size="100%">Ayats, Roser</style></author><author><style face="normal" font="default" size="100%">Martin de Vicente, Carlos</style></author><author><style face="normal" font="default" size="100%">Velasco González, Valle</style></author><author><style face="normal" font="default" size="100%">Moure González, José Domingo</style></author><author><style face="normal" font="default" size="100%">Canino Calderín, Elisa María</style></author><author><style face="normal" font="default" size="100%">Pastor-Vivero, María Dolores</style></author><author><style face="normal" font="default" size="100%">Villar Álvarez, María Ángeles</style></author><author><style face="normal" font="default" size="100%">Rovira-Amigo, Sandra</style></author><author><style face="normal" font="default" size="100%">Iglesias Serrano, Ignacio</style></author><author><style face="normal" font="default" size="100%">Díez Izquierdo, Ana</style></author><author><style face="normal" font="default" size="100%">de Mir Messa, Inés</style></author><author><style face="normal" font="default" size="100%">Gartner, Silvia</style></author><author><style face="normal" font="default" size="100%">Navarro, Alexandra</style></author><author><style face="normal" font="default" size="100%">Baz-Redón, Noelia</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Camats-Tarruella, Núria</style></author><author><style face="normal" font="default" size="100%">Fernández-Cancio, Mónica</style></author><author><style face="normal" font="default" size="100%">Rapp, Christina</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Griese, Matthias</style></author><author><style face="normal" font="default" size="100%">Moreno-Galdó, Antonio</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">ChILD-Spain Group</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Incidence and Prevalence of Children's Diffuse Lung Disease in Spain.</style></title><secondary-title><style face="normal" font="default" size="100%">Arch Bronconeumol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Arch Bronconeumol</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">58</style></volume><pages><style face="normal" font="default" size="100%">22-29</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Children's diffuse lung disease, also known as children's Interstitial Lung Diseases (chILD), are a heterogeneous group of rare diseases with relevant morbidity and mortality, which diagnosis and classification are very complex. Epidemiological data are scarce. The aim of this study was to analyse incidence and prevalence of chILD in Spain.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Multicentre observational prospective study in patients from 0 to 18 years of age with chILD to analyse its incidence and prevalence in Spain, based on data reported in 2018 and 2019.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;A total of 381 cases with chILD were notified from 51 paediatric pulmonology units all over Spain, covering the 91.7% of the paediatric population. The average incidence of chILD was 8.18 (CI 95% 6.28-10.48) new cases/million of children per year. The average prevalence of chILD was 46.53 (CI 95% 41.81-51.62) cases/million of children. The age group with the highest prevalence were children under 1 year of age. Different types of disorders were seen in children 2-18 years of age compared with children 0-2 years of age. Most frequent cases were: primary pulmonary interstitial glycogenosis in neonates (17/65), neuroendocrine cell hyperplasia of infancy in infants from 1 to 12 months (44/144), idiopathic pulmonary haemosiderosis in children from 1 to 5 years old (13/74), hypersensitivity pneumonitis in children from 5 to 10 years old (9/51), and scleroderma in older than 10 years old (8/47).&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;We found a higher incidence and prevalence of chILD than previously described probably due to greater understanding and increased clinician awareness of these rare diseases.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cruz, Raquel</style></author><author><style face="normal" font="default" size="100%">Almeida, Silvia Diz-de</style></author><author><style face="normal" font="default" size="100%">Heredia, Miguel López</style></author><author><style face="normal" font="default" size="100%">Quintela, Inés</style></author><author><style face="normal" font="default" size="100%">Ceballos, Francisco C</style></author><author><style face="normal" font="default" size="100%">Pita, Guillermo</style></author><author><style face="normal" font="default" size="100%">Lorenzo-Salazar, José M</style></author><author><style face="normal" font="default" size="100%">González-Montelongo, Rafaela</style></author><author><style face="normal" font="default" size="100%">Gago-Domínguez, Manuela</style></author><author><style face="normal" font="default" size="100%">Porras, Marta Sevilla</style></author><author><style face="normal" font="default" size="100%">Castaño, Jair Antonio Tenorio</style></author><author><style face="normal" font="default" size="100%">Nevado, Julián</style></author><author><style face="normal" font="default" size="100%">Aguado, Jose María</style></author><author><style face="normal" font="default" size="100%">Aguilar, Carlos</style></author><author><style face="normal" font="default" size="100%">Aguilera-Albesa, Sergio</style></author><author><style face="normal" font="default" size="100%">Almadana, Virginia</style></author><author><style face="normal" font="default" size="100%">Almoguera, Berta</style></author><author><style face="normal" font="default" size="100%">Alvarez, Nuria</style></author><author><style face="normal" font="default" size="100%">Andreu-Bernabeu, Álvaro</style></author><author><style face="normal" font="default" size="100%">Arana-Arri, Eunate</style></author><author><style face="normal" font="default" size="100%">Arango, Celso</style></author><author><style face="normal" font="default" size="100%">Arranz, María J</style></author><author><style face="normal" font="default" size="100%">Artiga, Maria-Jesus</style></author><author><style face="normal" font="default" size="100%">Baptista-Rosas, Raúl C</style></author><author><style face="normal" font="default" size="100%">Barreda-Sánchez, María</style></author><author><style face="normal" font="default" size="100%">Belhassen-Garcia, Moncef</style></author><author><style face="normal" font="default" size="100%">Bezerra, Joao F</style></author><author><style face="normal" font="default" size="100%">Bezerra, Marcos A C</style></author><author><style face="normal" font="default" size="100%">Boix-Palop, Lucía</style></author><author><style face="normal" font="default" size="100%">Brión, Maria</style></author><author><style face="normal" font="default" size="100%">Brugada, Ramón</style></author><author><style face="normal" font="default" size="100%">Bustos, Matilde</style></author><author><style face="normal" font="default" size="100%">Calderón, Enrique J</style></author><author><style face="normal" font="default" size="100%">Carbonell, Cristina</style></author><author><style face="normal" font="default" size="100%">Castano, Luis</style></author><author><style face="normal" font="default" size="100%">Castelao, Jose E</style></author><author><style face="normal" font="default" size="100%">Conde-Vicente, Rosa</style></author><author><style face="normal" font="default" size="100%">Cordero-Lorenzana, M Lourdes</style></author><author><style face="normal" font="default" size="100%">Cortes-Sanchez, Jose L</style></author><author><style face="normal" font="default" size="100%">Corton, Marta</style></author><author><style face="normal" font="default" size="100%">Darnaude, M Teresa</style></author><author><style face="normal" font="default" size="100%">De Martino-Rodríguez, Alba</style></author><author><style face="normal" font="default" size="100%">Campo-Pérez, Victor</style></author><author><style face="normal" font="default" size="100%">Bustamante, Aranzazu Diaz</style></author><author><style face="normal" font="default" size="100%">Domínguez-Garrido, Elena</style></author><author><style face="normal" font="default" size="100%">Luchessi, André D</style></author><author><style face="normal" font="default" size="100%">Eirós, Rocío</style></author><author><style face="normal" font="default" size="100%">Sanabria, Gladys Mercedes Estigarribia</style></author><author><style face="normal" font="default" size="100%">Fariñas, María Carmen</style></author><author><style face="normal" font="default" size="100%">Fernández-Robelo, Uxía</style></author><author><style face="normal" font="default" size="100%">Fernández-Rodríguez, Amanda</style></author><author><style face="normal" font="default" size="100%">Fernández-Villa, Tania</style></author><author><style face="normal" font="default" size="100%">Gil-Fournier, Belén</style></author><author><style face="normal" font="default" size="100%">Gómez-Arrue, Javier</style></author><author><style face="normal" font="default" size="100%">Álvarez, Beatriz González</style></author><author><style face="normal" font="default" size="100%">Quirós, Fernan Gonzalez Bernaldo</style></author><author><style face="normal" font="default" size="100%">González-Peñas, Javier</style></author><author><style face="normal" font="default" size="100%">Gutiérrez-Bautista, Juan F</style></author><author><style face="normal" font="default" size="100%">Herrero, María José</style></author><author><style face="normal" font="default" size="100%">Herrero-Gonzalez, Antonio</style></author><author><style face="normal" font="default" size="100%">Jimenez-Sousa, María A</style></author><author><style face="normal" font="default" size="100%">Lattig, María Claudia</style></author><author><style face="normal" font="default" size="100%">Borja, Anabel Liger</style></author><author><style face="normal" font="default" size="100%">Lopez-Rodriguez, Rosario</style></author><author><style face="normal" font="default" size="100%">Mancebo, Esther</style></author><author><style face="normal" font="default" size="100%">Martín-López, Caridad</style></author><author><style face="normal" font="default" size="100%">Martín, Vicente</style></author><author><style face="normal" font="default" size="100%">Martinez-Nieto, Oscar</style></author><author><style face="normal" font="default" size="100%">Martinez-Lopez, Iciar</style></author><author><style face="normal" font="default" size="100%">Martinez-Resendez, Michel F</style></author><author><style face="normal" font="default" size="100%">Martinez-Perez, Ángel</style></author><author><style face="normal" font="default" size="100%">Mazzeu, Juliana A</style></author><author><style face="normal" font="default" size="100%">Macías, Eleuterio Merayo</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Cuerda, Victor Moreno</style></author><author><style face="normal" font="default" size="100%">Silbiger, Vivian N</style></author><author><style face="normal" font="default" size="100%">Oliveira, Silviene F</style></author><author><style face="normal" font="default" size="100%">Ortega-Paino, Eva</style></author><author><style face="normal" font="default" size="100%">Parellada, Mara</style></author><author><style face="normal" font="default" size="100%">Paz-Artal, Estela</style></author><author><style face="normal" font="default" size="100%">Santos, Ney P C</style></author><author><style face="normal" font="default" size="100%">Pérez-Matute, Patricia</style></author><author><style face="normal" font="default" size="100%">Perez, Patricia</style></author><author><style face="normal" font="default" size="100%">Pérez-Tomás, M Elena</style></author><author><style face="normal" font="default" size="100%">Perucho, Teresa</style></author><author><style face="normal" font="default" size="100%">Pinsach-Abuin, Mel Lina</style></author><author><style face="normal" font="default" size="100%">Pompa-Mera, Ericka N</style></author><author><style face="normal" font="default" size="100%">Porras-Hurtado, Gloria L</style></author><author><style face="normal" font="default" size="100%">Pujol, Aurora</style></author><author><style face="normal" font="default" size="100%">León, Soraya Ramiro</style></author><author><style face="normal" font="default" size="100%">Resino, Salvador</style></author><author><style face="normal" font="default" size="100%">Fernandes, Marianne R</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Ruiz, Emilio</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Artalejo, Fernando</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Garcia, José A</style></author><author><style face="normal" font="default" size="100%">Ruiz-Cabello, Francisco</style></author><author><style face="normal" font="default" size="100%">Ruiz-Hornillos, Javier</style></author><author><style face="normal" font="default" size="100%">Ryan, Pablo</style></author><author><style face="normal" font="default" size="100%">Soria, José Manuel</style></author><author><style face="normal" font="default" size="100%">Souto, Juan Carlos</style></author><author><style face="normal" font="default" size="100%">Tamayo, Eduardo</style></author><author><style face="normal" font="default" size="100%">Tamayo-Velasco, Alvaro</style></author><author><style face="normal" font="default" size="100%">Taracido-Fernandez, Juan Carlos</style></author><author><style face="normal" font="default" size="100%">Teper, Alejandro</style></author><author><style face="normal" font="default" size="100%">Torres-Tobar, Lilian</style></author><author><style face="normal" font="default" size="100%">Urioste, Miguel</style></author><author><style face="normal" font="default" size="100%">Valencia-Ramos, Juan</style></author><author><style face="normal" font="default" size="100%">Yáñez, Zuleima</style></author><author><style face="normal" font="default" size="100%">Zarate, Ruth</style></author><author><style face="normal" font="default" size="100%">Nakanishi, Tomoko</style></author><author><style face="normal" font="default" size="100%">Pigazzini, Sara</style></author><author><style face="normal" font="default" size="100%">Degenhardt, Frauke</style></author><author><style face="normal" font="default" size="100%">Butler-Laporte, Guillaume</style></author><author><style face="normal" font="default" size="100%">Maya-Miles, Douglas</style></author><author><style face="normal" font="default" size="100%">Bujanda, Luis</style></author><author><style face="normal" font="default" size="100%">Bouysran, Youssef</style></author><author><style face="normal" font="default" size="100%">Palom, Adriana</style></author><author><style face="normal" font="default" size="100%">Ellinghaus, David</style></author><author><style face="normal" font="default" size="100%">Martínez-Bueno, Manuel</style></author><author><style face="normal" font="default" size="100%">Rolker, Selina</style></author><author><style face="normal" font="default" size="100%">Amitrano, Sara</style></author><author><style face="normal" font="default" size="100%">Roade, Luisa</style></author><author><style face="normal" font="default" size="100%">Fava, Francesca</style></author><author><style face="normal" font="default" size="100%">Spinner, Christoph D</style></author><author><style face="normal" font="default" size="100%">Prati, Daniele</style></author><author><style face="normal" font="default" size="100%">Bernardo, David</style></author><author><style face="normal" font="default" size="100%">García, Federico</style></author><author><style face="normal" font="default" size="100%">Darcis, Gilles</style></author><author><style face="normal" font="default" size="100%">Fernández-Cadenas, Israel</style></author><author><style face="normal" font="default" size="100%">Holter, Jan Cato</style></author><author><style face="normal" font="default" size="100%">Banales, Jesus M</style></author><author><style face="normal" font="default" size="100%">Frithiof, Robert</style></author><author><style face="normal" font="default" size="100%">Duga, Stefano</style></author><author><style face="normal" font="default" size="100%">Asselta, Rosanna</style></author><author><style face="normal" font="default" size="100%">Pereira, Alexandre C</style></author><author><style face="normal" font="default" size="100%">Romero-Gómez, Manuel</style></author><author><style face="normal" font="default" size="100%">Nafría-Jiménez, Beatriz</style></author><author><style face="normal" font="default" size="100%">Hov, Johannes R</style></author><author><style face="normal" font="default" size="100%">Migeotte, Isabelle</style></author><author><style face="normal" font="default" size="100%">Renieri, Alessandra</style></author><author><style face="normal" font="default" size="100%">Planas, Anna M</style></author><author><style face="normal" font="default" size="100%">Ludwig, Kerstin U</style></author><author><style face="normal" font="default" size="100%">Buti, Maria</style></author><author><style face="normal" font="default" size="100%">Rahmouni, Souad</style></author><author><style face="normal" font="default" size="100%">Alarcón-Riquelme, Marta E</style></author><author><style face="normal" font="default" size="100%">Schulte, Eva C</style></author><author><style face="normal" font="default" size="100%">Franke, Andre</style></author><author><style face="normal" font="default" size="100%">Karlsen, Tom H</style></author><author><style face="normal" font="default" size="100%">Valenti, Luca</style></author><author><style face="normal" font="default" size="100%">Zeberg, Hugo</style></author><author><style face="normal" font="default" size="100%">Richards, Brent</style></author><author><style face="normal" font="default" size="100%">Ganna, Andrea</style></author><author><style face="normal" font="default" size="100%">Boada, Mercè</style></author><author><style face="normal" font="default" size="100%">Rojas, Itziar</style></author><author><style face="normal" font="default" size="100%">Ruiz, Agustín</style></author><author><style face="normal" font="default" size="100%">Sánchez, Pascual</style></author><author><style face="normal" font="default" size="100%">Real, Luis Miguel</style></author><author><style face="normal" font="default" size="100%">Guillén-Navarro, Encarna</style></author><author><style face="normal" font="default" size="100%">Ayuso, Carmen</style></author><author><style face="normal" font="default" size="100%">González-Neira, Anna</style></author><author><style face="normal" font="default" size="100%">Riancho, José A</style></author><author><style face="normal" font="default" size="100%">Rojas-Martinez, Augusto</style></author><author><style face="normal" font="default" size="100%">Flores, Carlos</style></author><author><style face="normal" font="default" size="100%">Lapunzina, Pablo</style></author><author><style face="normal" font="default" size="100%">Carracedo, Ángel</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">SCOURGE Cohort Group</style></author><author><style face="normal" font="default" size="100%">HOSTAGE Cohort Group</style></author><author><style face="normal" font="default" size="100%">GRA@CE Cohort Group</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel genes and sex differences in COVID-19 severity.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Mol Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Mol Genet</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 Jun 16</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Here we describe the results of a genome-wide study conducted in 11 939 COVID-19 positive cases with an extensive clinical information that were recruited from 34 hospitals across Spain (SCOURGE consortium). In sex-disaggregated genome-wide association studies for COVID-19 hospitalization, genome-wide significance (p &lt; 5x10-8) was crossed for variants in 3p21.31 and 21q22.11 loci only among males (p = 1.3x10-22 and p = 8.1x10-12, respectively), and for variants in 9q21.32 near TLE1 only among females (p = 4.4x10-8). In a second phase, results were combined with an independent Spanish cohort (1598 COVID-19 cases and 1068 population controls), revealing in the overall analysis two novel risk loci in 9p13.3 and 19q13.12, with fine-mapping prioritized variants functionally associated with AQP3 (p = 2.7x10-8) and ARHGAP33 (p = 1.3x10-8), respectively. The meta-analysis of both phases with four European studies stratified by sex from the Host Genetics Initiative confirmed the association of the 3p21.31 and 21q22.11 loci predominantly in males and replicated a recently reported variant in 11p13 (ELF5, p = 4.1x10-8). Six of the COVID-19 HGI discovered loci were replicated and an HGI-based genetic risk score predicted the severity strata in SCOURGE. We also found more SNP-heritability and larger heritability differences by age (&lt;60 or ≥ 60 years) among males than among females. Parallel genome-wide screening of inbreeding depression in SCOURGE also showed an effect of homozygosity in COVID-19 hospitalization and severity and this effect was stronger among older males. In summary, new candidate genes for COVID-19 severity and evidence supporting genetic disparities among sexes are provided.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ortiz-Aljaro, Pilar</style></author><author><style face="normal" font="default" size="100%">Montes-Cano, Marco Antonio</style></author><author><style face="normal" font="default" size="100%">García-Lozano, José-Raúl</style></author><author><style face="normal" font="default" size="100%">Aquino, Virginia</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">García-Hernández, Francisco José</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">González-Escribano, María Francisca</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Protein and functional isoform levels and genetic variants of the BAFF and APRIL pathway components in systemic lupus erythematosus.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 Jul 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">11219</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Systemic lupus erythematosus (SLE) is the prototype of an autoimmune disease. Belimumab, a monoclonal antibody targets BAFF, is the only biologic approved for SLE and active lupus nephritis. BAFF is a cytokine with a key-regulatory role in the B cell homeostasis, which acts by binding to three receptors: BAFF-R, TACI and BCMA. TACI and BCMA also bind APRIL. Many studies reported elevated soluble BAFF and APRIL levels in the sera of SLE patients, but other questions about the role of this system in the disease remain open. The study aimed to investigate the utility of the cytokine levels in serum and urine as biomarkers, the role of non-functional isoforms, and the association of gene variants with the disease. This case-control study includes a cohort (women, 18-60 years old) of 100 patients (48% with nephritis) and 100 healthy controls. We used ELISA assays to measure the cytokine concentrations in serum (sBAFF and sAPRIL) and urine (uBAFF and uAPRIL); TaqMan Gene Expression Assays to quantify the relative mRNA expression of ΔBAFF, βAPRIL, and εAPRIL, and next-generation sequencing to genotype the cytokine (TNFSF13 and TNFSF13B) and receptor (TNFRSF13B, TNFRSF17 and TNFRSF13C) genes. The statistical tests used were: Kruskal-Wallis (qualitative variables), the Spearman Rho coefficient (correlations), the Chi-square and SKAT (association of common and rare genetic variants, respectively). As expected, sBAFF and sAPRIL levels were higher in patients than in controls (p ≤ 0.001) but found differences between patient subgroups. sBAFF and sAPRIL significantly correlated only in patients with nephritis (r = 0.67, p ≤ 0.001) and βAPRIL levels were lower in patients with nephritis (p = 0.04), and ΔBAFF levels were lower in patients with dsDNA antibodies (p = 0.04). Rare variants of TNFSF13 and TNFRSF13B and TNFSF13 p.Gly67Arg and TNFRSF13B p.Val220Ala were associated with SLE. Our study supports differences among SLE patient subgroups with diverse clinical features in the BAFF/APRIL pathway. In addition, it suggests the involvement of genetic variants in the susceptibility to the disease.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 Jan 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">450</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 1042 fecal metagenomic samples from seven publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allow extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes undergone by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Functional profiles have demonstrated superior accuracy in predicting CRC and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martorell-Marugán, Jordi</style></author><author><style face="normal" font="default" size="100%">López-Domínguez, Raúl</style></author><author><style face="normal" font="default" size="100%">García-Moreno, Adrián</style></author><author><style face="normal" font="default" size="100%">Toro-Domínguez, Daniel</style></author><author><style face="normal" font="default" size="100%">Villatoro-García, Juan Antonio</style></author><author><style face="normal" font="default" size="100%">Barturen, Guillermo</style></author><author><style face="normal" font="default" size="100%">Martín-Gómez, Adoración</style></author><author><style face="normal" font="default" size="100%">Troule, Kevin</style></author><author><style face="normal" font="default" size="100%">Gómez-López, Gonzalo</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">González-Rumayor, Víctor</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Saez-Rodriguez, Julio</style></author><author><style face="normal" font="default" size="100%">Alarcón-Riquelme, Marta E</style></author><author><style face="normal" font="default" size="100%">Carmona-Sáez, Pedro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A comprehensive database for integrated analysis of omics data in autoimmune diseases.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Autoimmune Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 Jun 24</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">343</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34167460?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ostaszewski, Marek</style></author><author><style face="normal" font="default" size="100%">Niarakis, Anna</style></author><author><style face="normal" font="default" size="100%">Mazein, Alexander</style></author><author><style face="normal" font="default" size="100%">Kuperstein, Inna</style></author><author><style face="normal" font="default" size="100%">Phair, Robert</style></author><author><style face="normal" font="default" size="100%">Orta-Resendiz, Aurelio</style></author><author><style face="normal" font="default" size="100%">Singh, Vidisha</style></author><author><style face="normal" font="default" size="100%">Aghamiri, Sara Sadat</style></author><author><style face="normal" font="default" size="100%">Acencio, Marcio Luis</style></author><author><style face="normal" font="default" size="100%">Glaab, Enrico</style></author><author><style face="normal" font="default" size="100%">Ruepp, Andreas</style></author><author><style face="normal" font="default" size="100%">Fobo, Gisela</style></author><author><style face="normal" font="default" size="100%">Montrone, Corinna</style></author><author><style face="normal" font="default" size="100%">Brauner, Barbara</style></author><author><style face="normal" font="default" size="100%">Frishman, Goar</style></author><author><style face="normal" font="default" size="100%">Monraz Gómez, Luis Cristóbal</style></author><author><style face="normal" font="default" size="100%">Somers, Julia</style></author><author><style face="normal" font="default" size="100%">Hoch, Matti</style></author><author><style face="normal" font="default" size="100%">Kumar Gupta, Shailendra</style></author><author><style face="normal" font="default" size="100%">Scheel, Julia</style></author><author><style face="normal" font="default" size="100%">Borlinghaus, Hanna</style></author><author><style face="normal" font="default" size="100%">Czauderna, Tobias</style></author><author><style face="normal" font="default" size="100%">Schreiber, Falk</style></author><author><style face="normal" font="default" size="100%">Montagud, Arnau</style></author><author><style face="normal" font="default" size="100%">Ponce de Leon, Miguel</style></author><author><style face="normal" font="default" size="100%">Funahashi, Akira</style></author><author><style face="normal" font="default" size="100%">Hiki, Yusuke</style></author><author><style face="normal" font="default" size="100%">Hiroi, Noriko</style></author><author><style face="normal" font="default" size="100%">Yamada, Takahiro G</style></author><author><style face="normal" font="default" size="100%">Dräger, Andreas</style></author><author><style face="normal" font="default" size="100%">Renz, Alina</style></author><author><style face="normal" font="default" size="100%">Naveez, Muhammad</style></author><author><style face="normal" font="default" size="100%">Bocskei, Zsolt</style></author><author><style face="normal" font="default" size="100%">Messina, Francesco</style></author><author><style face="normal" font="default" size="100%">Börnigen, Daniela</style></author><author><style face="normal" font="default" size="100%">Fergusson, Liam</style></author><author><style face="normal" font="default" size="100%">Conti, Marta</style></author><author><style face="normal" font="default" size="100%">Rameil, Marius</style></author><author><style face="normal" font="default" size="100%">Nakonecnij, Vanessa</style></author><author><style face="normal" font="default" size="100%">Vanhoefer, Jakob</style></author><author><style face="normal" font="default" size="100%">Schmiester, Leonard</style></author><author><style face="normal" font="default" size="100%">Wang, Muying</style></author><author><style face="normal" font="default" size="100%">Ackerman, Emily E</style></author><author><style face="normal" font="default" size="100%">Shoemaker, Jason E</style></author><author><style face="normal" font="default" size="100%">Zucker, Jeremy</style></author><author><style face="normal" font="default" size="100%">Oxford, Kristie</style></author><author><style face="normal" font="default" size="100%">Teuton, Jeremy</style></author><author><style face="normal" font="default" size="100%">Kocakaya, Ebru</style></author><author><style face="normal" font="default" size="100%">Summak, Gökçe Yağmur</style></author><author><style face="normal" font="default" size="100%">Hanspers, Kristina</style></author><author><style face="normal" font="default" size="100%">Kutmon, Martina</style></author><author><style face="normal" font="default" size="100%">Coort, Susan</style></author><author><style face="normal" font="default" size="100%">Eijssen, Lars</style></author><author><style face="normal" font="default" size="100%">Ehrhart, Friederike</style></author><author><style face="normal" font="default" size="100%">Rex, Devasahayam Arokia Balaya</style></author><author><style face="normal" font="default" size="100%">Slenter, Denise</style></author><author><style face="normal" font="default" size="100%">Martens, Marvin</style></author><author><style face="normal" font="default" size="100%">Pham, Nhung</style></author><author><style face="normal" font="default" size="100%">Haw, Robin</style></author><author><style face="normal" font="default" size="100%">Jassal, Bijay</style></author><author><style face="normal" font="default" size="100%">Matthews, Lisa</style></author><author><style face="normal" font="default" size="100%">Orlic-Milacic, Marija</style></author><author><style face="normal" font="default" size="100%">Senff Ribeiro, Andrea</style></author><author><style face="normal" font="default" size="100%">Rothfels, Karen</style></author><author><style face="normal" font="default" size="100%">Shamovsky, Veronica</style></author><author><style face="normal" font="default" size="100%">Stephan, Ralf</style></author><author><style face="normal" font="default" size="100%">Sevilla, Cristoffer</style></author><author><style face="normal" font="default" size="100%">Varusai, Thawfeek</style></author><author><style face="normal" font="default" size="100%">Ravel, Jean-Marie</style></author><author><style face="normal" font="default" size="100%">Fraser, Rupsha</style></author><author><style face="normal" font="default" size="100%">Ortseifen, Vera</style></author><author><style face="normal" font="default" size="100%">Marchesi, Silvia</style></author><author><style face="normal" font="default" size="100%">Gawron, Piotr</style></author><author><style face="normal" font="default" size="100%">Smula, Ewa</style></author><author><style face="normal" font="default" size="100%">Heirendt, Laurent</style></author><author><style face="normal" font="default" size="100%">Satagopam, Venkata</style></author><author><style face="normal" font="default" size="100%">Wu, Guanming</style></author><author><style face="normal" font="default" size="100%">Riutta, Anders</style></author><author><style face="normal" font="default" size="100%">Golebiewski, Martin</style></author><author><style face="normal" font="default" size="100%">Owen, Stuart</style></author><author><style face="normal" font="default" size="100%">Goble, Carole</style></author><author><style face="normal" font="default" size="100%">Hu, Xiaoming</style></author><author><style face="normal" font="default" size="100%">Overall, Rupert W</style></author><author><style face="normal" font="default" size="100%">Maier, Dieter</style></author><author><style face="normal" font="default" size="100%">Bauch, Angela</style></author><author><style face="normal" font="default" size="100%">Gyori, Benjamin M</style></author><author><style face="normal" font="default" size="100%">Bachman, John A</style></author><author><style face="normal" font="default" size="100%">Vega, Carlos</style></author><author><style face="normal" font="default" size="100%">Grouès, Valentin</style></author><author><style face="normal" font="default" size="100%">Vazquez, Miguel</style></author><author><style face="normal" font="default" size="100%">Porras, Pablo</style></author><author><style face="normal" font="default" size="100%">Licata, Luana</style></author><author><style face="normal" font="default" size="100%">Iannuccelli, Marta</style></author><author><style face="normal" font="default" size="100%">Sacco, Francesca</style></author><author><style face="normal" font="default" size="100%">Nesterova, Anastasia</style></author><author><style face="normal" font="default" size="100%">Yuryev, Anton</style></author><author><style face="normal" font="default" size="100%">de Waard, Anita</style></author><author><style face="normal" font="default" size="100%">Turei, Denes</style></author><author><style face="normal" font="default" size="100%">Luna, Augustin</style></author><author><style face="normal" font="default" size="100%">Babur, Ozgun</style></author><author><style face="normal" font="default" size="100%">Soliman, Sylvain</style></author><author><style face="normal" font="default" size="100%">Valdeolivas, Alberto</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Helikar, Tomáš</style></author><author><style face="normal" font="default" size="100%">Puniya, Bhanwar Lal</style></author><author><style face="normal" font="default" size="100%">Modos, Dezso</style></author><author><style face="normal" font="default" size="100%">Treveil, Agatha</style></author><author><style face="normal" font="default" size="100%">Olbei, Marton</style></author><author><style face="normal" font="default" size="100%">De Meulder, Bertrand</style></author><author><style face="normal" font="default" size="100%">Ballereau, Stephane</style></author><author><style face="normal" font="default" size="100%">Dugourd, Aurélien</style></author><author><style face="normal" font="default" size="100%">Naldi, Aurélien</style></author><author><style face="normal" font="default" size="100%">Noël, Vincent</style></author><author><style face="normal" font="default" size="100%">Calzone, Laurence</style></author><author><style face="normal" font="default" size="100%">Sander, Chris</style></author><author><style face="normal" font="default" size="100%">Demir, Emek</style></author><author><style face="normal" font="default" size="100%">Korcsmaros, Tamas</style></author><author><style face="normal" font="default" size="100%">Freeman, Tom C</style></author><author><style face="normal" font="default" size="100%">Augé, Franck</style></author><author><style face="normal" font="default" size="100%">Beckmann, Jacques S</style></author><author><style face="normal" font="default" size="100%">Hasenauer, Jan</style></author><author><style face="normal" font="default" size="100%">Wolkenhauer, Olaf</style></author><author><style face="normal" font="default" size="100%">Wilighagen, Egon L</style></author><author><style face="normal" font="default" size="100%">Pico, Alexander R</style></author><author><style face="normal" font="default" size="100%">Evelo, Chris T</style></author><author><style face="normal" font="default" size="100%">Gillespie, Marc E</style></author><author><style face="normal" font="default" size="100%">Stein, Lincoln D</style></author><author><style face="normal" font="default" size="100%">Hermjakob, Henning</style></author><author><style face="normal" font="default" size="100%">D'Eustachio, Peter</style></author><author><style face="normal" font="default" size="100%">Saez-Rodriguez, Julio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Valencia, Alfonso</style></author><author><style face="normal" font="default" size="100%">Kitano, Hiroaki</style></author><author><style face="normal" font="default" size="100%">Barillot, Emmanuel</style></author><author><style face="normal" font="default" size="100%">Auffray, Charles</style></author><author><style face="normal" font="default" size="100%">Balling, Rudi</style></author><author><style face="normal" font="default" size="100%">Schneider, Reinhard</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">COVID-19 Disease Map Community</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Syst Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Syst Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Antiviral Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Graphics</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Cytokines</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Host Microbial Interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunity, Cellular</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunity, Humoral</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunity, Innate</style></keyword><keyword><style  face="normal" font="default" size="100%">Lymphocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolic Networks and Pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">Myeloid Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Viral Proteins</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">e10387</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34664389?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Roldán, Gema</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco M</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Aquino, Virginia</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Fernandez-Rueda, Jose L</style></author><author><style face="normal" font="default" size="100%">Gallego, Asunción</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">González-Neira, Anna</style></author><author><style face="normal" font="default" size="100%">Pita, Guillermo</style></author><author><style face="normal" font="default" size="100%">Núñez-Torres, Rocío</style></author><author><style face="normal" font="default" size="100%">Santoyo-López, Javier</style></author><author><style face="normal" font="default" size="100%">Ayuso, Carmen</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Avila-Fernandez, Almudena</style></author><author><style face="normal" font="default" size="100%">Corton, Marta</style></author><author><style face="normal" font="default" size="100%">Moreno-Pelayo, Miguel Ángel</style></author><author><style face="normal" font="default" size="100%">Morin, Matías</style></author><author><style face="normal" font="default" size="100%">Gallego-Martinez, Alvaro</style></author><author><style face="normal" font="default" size="100%">Lopez-Escamez, Jose A</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author><author><style face="normal" font="default" size="100%">Amigo, Jorge</style></author><author><style face="normal" font="default" size="100%">Salgado-Garrido, Josefa</style></author><author><style face="normal" font="default" size="100%">Pasalodos-Sanchez, Sara</style></author><author><style face="normal" font="default" size="100%">Morte, Beatriz</style></author><author><style face="normal" font="default" size="100%">Carracedo, Ángel</style></author><author><style face="normal" font="default" size="100%">Alonso, Ángel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">Spanish Exome Crowdsourcing Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">CSVS, a crowdsourcing database of the Spanish population genetic variability.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alleles</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosome Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Crowdsourcing</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetics, Population</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Precision Medicine</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Spain</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 01 08</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">49</style></volume><pages><style face="normal" font="default" size="100%">D1130-D1137</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The knowledge of the genetic variability of the local population is of utmost importance in personalized medicine and has been revealed as a critical factor for the discovery of new disease variants. Here, we present the Collaborative Spanish Variability Server (CSVS), which currently contains more than 2000 genomes and exomes of unrelated Spanish individuals. This database has been generated in a collaborative crowdsourcing effort collecting sequencing data produced by local genomic projects and for other purposes. Sequences have been grouped by ICD10 upper categories. A web interface allows querying the database removing one or more ICD10 categories. In this way, aggregated counts of allele frequencies of the pseudo-control Spanish population can be obtained for diseases belonging to the category removed. Interestingly, in addition to pseudo-control studies, some population studies can be made, as, for example, prevalence of pharmacogenomic variants, etc. In addition, this genomic data has been used to define the first Spanish Genome Reference Panel (SGRP1.0) for imputation. This is the first local repository of variability entirely produced by a crowdsourcing effort and constitutes an example for future initiatives to characterize local variability worldwide. CSVS is also part of the GA4GH Beacon network. CSVS can be accessed at: http://csvs.babelomics.org/.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">D1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32990755?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martinez-Delgado, Beatriz</style></author><author><style face="normal" font="default" size="100%">Lopez-Martin, Estrella</style></author><author><style face="normal" font="default" size="100%">Lara-Herguedas, Julián</style></author><author><style face="normal" font="default" size="100%">Monzon, Sara</style></author><author><style face="normal" font="default" size="100%">Cuesta, Isabel</style></author><author><style face="normal" font="default" size="100%">Juliá, Miguel</style></author><author><style face="normal" font="default" size="100%">Aquino, Virginia</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Martin, Carlos</style></author><author><style face="normal" font="default" size="100%">Damian, Alejandra</style></author><author><style face="normal" font="default" size="100%">Gonzalo, Irene</style></author><author><style face="normal" font="default" size="100%">Gomez-Mariano, Gema</style></author><author><style face="normal" font="default" size="100%">Baladron, Beatriz</style></author><author><style face="normal" font="default" size="100%">Cazorla, Rosario</style></author><author><style face="normal" font="default" size="100%">Iglesias, Gema</style></author><author><style face="normal" font="default" size="100%">Roman, Enriqueta</style></author><author><style face="normal" font="default" size="100%">Ros, Purificacion</style></author><author><style face="normal" font="default" size="100%">Tutor, Pablo</style></author><author><style face="normal" font="default" size="100%">Mellor, Susana</style></author><author><style face="normal" font="default" size="100%">Jimenez, Carlos</style></author><author><style face="normal" font="default" size="100%">Cabrejas, Maria Jose</style></author><author><style face="normal" font="default" size="100%">Gonzalez-Vioque, Emiliano</style></author><author><style face="normal" font="default" size="100%">Alonso, Javier</style></author><author><style face="normal" font="default" size="100%">Bermejo-Sánchez, Eva</style></author><author><style face="normal" font="default" size="100%">Posada, Manuel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">De novo small deletion affecting transcription start site of short isoform of AUTS2 gene in a patient with syndromic neurodevelopmental defects.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Med Genet A</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am J Med Genet A</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Child, Preschool</style></keyword><keyword><style  face="normal" font="default" size="100%">Cytoskeletal Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Dwarfism</style></keyword><keyword><style  face="normal" font="default" size="100%">Exons</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Association Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurodevelopmental Disorders</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Isoforms</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Deletion</style></keyword><keyword><style  face="normal" font="default" size="100%">Syndrome</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Initiation Site</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription, Genetic</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 03</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">185</style></volume><pages><style face="normal" font="default" size="100%">877-883</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Disruption of the autism susceptibility candidate 2 (AUTS2) gene through genomic rearrangements, copy number variations (CNVs), and intragenic deletions and mutations, has been recurrently involved in syndromic forms of developmental delay and intellectual disability, known as AUTS2 syndrome. The AUTS2 gene plays an important role in regulation of neuronal migration, and when altered, associates with a variable phenotype from severely to mildly affected patients. The more severe phenotypes significantly correlate with the presence of defects affecting the C-terminus part of the gene. This article reports a new patient with a syndromic neurodevelopmental disorder, who presents a deletion of 30 nucleotides in the exon 9 of the AUTS2 gene. Importantly, this deletion includes the transcription start site for the AUTS2 short transcript isoform, which has an important role in brain development. Gene expression analysis of AUTS2 full-length and short isoforms revealed that the deletion found in this patient causes a remarkable reduction in the expression level, not only of the short isoform, but also of the full AUTS2 transcripts. This report adds more evidence for the role of mutated AUTS2 short transcripts in the development of a severe phenotype in the AUTS2 syndrome.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">López-Quílez, Antonio</style></author><author><style face="normal" font="default" size="100%">Conesa, David</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematics</style></secondary-title><short-title><style face="normal" font="default" size="100%">Mathematics</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-11-2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/2227-7390/9/21/2833https://www.mdpi.com/2227-7390/9/21/2833/pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">2833</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">21</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Moura, David S</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Cordero Varela, Juan Antonio</style></author><author><style face="normal" font="default" size="100%">Alvarez-Alegret, Ramiro</style></author><author><style face="normal" font="default" size="100%">Agra-Pujol, Carolina</style></author><author><style face="normal" font="default" size="100%">Izquierdo, Francisco</style></author><author><style face="normal" font="default" size="100%">Ramos, Rafael</style></author><author><style face="normal" font="default" size="100%">Ortega-Medina, Luis</style></author><author><style face="normal" font="default" size="100%">Martin-Davila, Francisco</style></author><author><style face="normal" font="default" size="100%">Castilla-Ramirez, Carolina</style></author><author><style face="normal" font="default" size="100%">Hernandez-Leon, Carmen Nieves</style></author><author><style face="normal" font="default" size="100%">Romagosa, Cleofe</style></author><author><style face="normal" font="default" size="100%">Vaz Salgado, Maria Angeles</style></author><author><style face="normal" font="default" size="100%">Lavernia, Javier</style></author><author><style face="normal" font="default" size="100%">Bagué, Silvia</style></author><author><style face="normal" font="default" size="100%">Mayodormo-Aranda, Empar</style></author><author><style face="normal" font="default" size="100%">Vicioso, Luis</style></author><author><style face="normal" font="default" size="100%">Hernández Barceló, Jose Emilio</style></author><author><style face="normal" font="default" size="100%">Rubio-Casadevall, Jordi</style></author><author><style face="normal" font="default" size="100%">de Juan, Ana</style></author><author><style face="normal" font="default" size="100%">Fiaño-Valverde, Maria Concepcion</style></author><author><style face="normal" font="default" size="100%">Hindi, Nadia</style></author><author><style face="normal" font="default" size="100%">Lopez-Alvarez, Maria</style></author><author><style face="normal" font="default" size="100%">Lacerenza, Serena</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Gutierrez, Antonio</style></author><author><style face="normal" font="default" size="100%">Alvarez, Rosa</style></author><author><style face="normal" font="default" size="100%">Valverde, Claudia</style></author><author><style face="normal" font="default" size="100%">Martinez-Trufero, Javier</style></author><author><style face="normal" font="default" size="100%">Martin-Broto, Javier</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A DNA damage repair gene-associated signature predicts responses of patients with advanced soft-tissue sarcoma to treatment with trabectedin.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Oncol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Oncol</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 12</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">3691-3705</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Predictive biomarkers of trabectedin represent an unmet need in advanced soft-tissue sarcomas (STS). DNA damage repair (DDR) genes, involved in homologous recombination or nucleotide excision repair, had been previously described as biomarkers of trabectedin resistance or sensitivity, respectively. The majority of these studies only focused on specific factors (ERCC1, ERCC5, and BRCA1) and did not evaluate several other DDR-related genes that could have a relevant role for trabectedin efficacy. In this retrospective translational study, 118 genes involved in DDR were evaluated to determine, by transcriptomics, a predictive gene signature of trabectedin efficacy. A six-gene predictive signature of trabectedin efficacy was built in a series of 139 tumor samples from patients with advanced STS. Patients in the high-risk gene signature group showed a significantly worse progression-free survival compared with patients in the low-risk group (2.1 vs 6.0 months, respectively). Differential gene expression analysis defined new potential predictive biomarkers of trabectedin sensitivity (PARP3 and CCNH) or resistance (DNAJB11 and PARP1). Our study identified a new gene signature that significantly predicts patients with higher probability to respond to treatment with trabectedin. Targeting some genes of this signature emerges as a potential strategy to enhance trabectedin efficacy.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33983674?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R.</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Falco, Matias M.</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Alamo-Alvarez, Inmaculada</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data</style></title><secondary-title><style face="normal" font="default" size="100%">Computational and Structural Biotechnology Journal</style></secondary-title><short-title><style face="normal" font="default" size="100%">Computational and Structural Biotechnology Journal</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-01-2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S2001037021002038</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">2968 - 2978</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Villalba-Benito, Leticia</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Torroglosa, Ana</style></author><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S.</style></author><author><style face="normal" font="default" size="100%">Luzón-Toro, Berta</style></author><author><style face="normal" font="default" size="100%">Fernández, Raquel María</style></author><author><style face="normal" font="default" size="100%">Moya-Jiménez, María José</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genome-wide analysis of DNA methylation in Hirschsprung enteric precursor cells: unraveling the epigenetic landscape of enteric nervous system developmentAbstractBackgroundResultsConclusionsGraphic abstract</style></title><secondary-title><style face="normal" font="default" size="100%">Clinical Epigenetics</style></secondary-title><short-title><style face="normal" font="default" size="100%">Clin Epigenet</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-12-2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/article/10.1186/s13148-021-01040-6/fulltext.html</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">13</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ortuno, Francisco M</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Lepe, Jose A</style></author><author><style face="normal" font="default" size="100%">Camacho Martinez, Pedro</style></author><author><style face="normal" font="default" size="100%">Merino Diaz, Laura</style></author><author><style face="normal" font="default" size="100%">de Salazar, Adolfo</style></author><author><style face="normal" font="default" size="100%">Chueca, Natalia</style></author><author><style face="normal" font="default" size="100%">García, Federico</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Highly accurate whole-genome imputation of SARS-CoV-2 from partial or low-quality sequences.</style></title><secondary-title><style face="normal" font="default" size="100%">Gigascience</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Gigascience</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Genome, Viral</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 12 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;The current SARS-CoV-2 pandemic has emphasized the utility of viral whole-genome sequencing in the surveillance and control of the pathogen. An unprecedented ongoing global initiative is producing hundreds of thousands of sequences worldwide. However, the complex circumstances in which viruses are sequenced, along with the demand of urgent results, causes a high rate of incomplete and, therefore, useless sequences. Viral sequences evolve in the context of a complex phylogeny and different positions along the genome are in linkage disequilibrium. Therefore, an imputation method would be able to predict missing positions from the available sequencing data.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We have developed the impuSARS application, which takes advantage of the enormous number of SARS-CoV-2 genomes available, using a reference panel containing 239,301 sequences, to produce missing data imputation in viral genomes. ImpuSARS was tested in a wide range of conditions (continuous fragments, amplicons or sparse individual positions missing), showing great fidelity when reconstructing the original sequences, recovering the lineage with a 100% precision for almost all the lineages, even in very poorly covered genomes (&lt;20%).&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Imputation can improve the pace of SARS-CoV-2 sequencing production by recovering many incomplete or low-quality sequences that would be otherwise discarded. ImpuSARS can be incorporated in any primary data processing pipeline for SARS-CoV-2 whole-genome sequencing.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34865008?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Maya-Miles, Douglas</style></author><author><style face="normal" font="default" size="100%">García, Federico</style></author><author><style face="normal" font="default" size="100%">Lorusso, Nicola</style></author><author><style face="normal" font="default" size="100%">Calleja, Miguel Ángel</style></author><author><style face="normal" font="default" size="100%">Pareja, María Jesús</style></author><author><style face="normal" font="default" size="100%">López-Miranda, José</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Baño, Jesús</style></author><author><style face="normal" font="default" size="100%">Padillo, Javier</style></author><author><style face="normal" font="default" size="100%">Túnez, Isaac</style></author><author><style face="normal" font="default" size="100%">Romero-Gómez, Manuel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Implementing Personalized Medicine in COVID-19 in Andalusia: An Opportunity to Transform the Healthcare System.</style></title><secondary-title><style face="normal" font="default" size="100%">J Pers Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Pers Med</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 May 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The COVID-19 pandemic represents an unprecedented opportunity to exploit the advantages of personalized medicine for the prevention, diagnosis, treatment, surveillance and management of a new challenge in public health. COVID-19 infection is highly variable, ranging from asymptomatic infections to severe, life-threatening manifestations. Personalized medicine can play a key role in elucidating individual susceptibility to the infection as well as inter-individual variability in clinical course, prognosis and response to treatment. Integrating personalized medicine into clinical practice can also transform health care by enabling the design of preventive and therapeutic strategies tailored to individual profiles, improving the detection of outbreaks or defining transmission patterns at an increasingly local level. SARS-CoV2 genome sequencing, together with the assessment of specific patient genetic variants, will support clinical decision-makers and ultimately better ways to fight this disease. Additionally, it would facilitate a better stratification and selection of patients for clinical trials, thus increasing the likelihood of obtaining positive results. Lastly, defining a national strategy to implement in clinical practice all available tools of personalized medicine in COVID-19 could be challenging but linked to a positive transformation of the health care system. In this review, we provide an update of the achievements, promises, and challenges of personalized medicine in the fight against COVID-19 from susceptibility to natural history and response to therapy, as well as from surveillance to control measures and vaccination. We also discuss strategies to facilitate the adoption of this new paradigm for medical and public health measures during and after the pandemic in health care systems.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34073493?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><translated-authors><author><style face="normal" font="default" size="100%">COVID-19 Host Genetics Initiative</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Mapping the human genetic architecture of COVID-19.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nature</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 Dec</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">600</style></volume><pages><style face="normal" font="default" size="100%">472-477</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7889</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34237774?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Falco, Matias M</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Gunyel, Devrim</style></author><author><style face="normal" font="default" size="100%">Ostaszewski, Marek</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mechanistic modeling of the SARS-CoV-2 disease map.</style></title><secondary-title><style face="normal" font="default" size="100%">BioData Min</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BioData Min</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 Jan 21</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map. In this framework, the detailed activity of the human signaling circuits related to the viral infection, covering from the entry and replication mechanisms to the downstream consequences as inflammation and antigenic response, can be inferred from gene expression experiments. Moreover, the effect of potential interventions, such as knock-downs, or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied. This freely available tool not only provides an unprecedentedly detailed view of the mechanisms of viral invasion and the consequences in the cell but has also the potential of becoming an invaluable asset in the search for efficient antiviral treatments.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33478554?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heath, Allison P.</style></author><author><style face="normal" font="default" size="100%">Ferretti, Vincent</style></author><author><style face="normal" font="default" size="100%">Agrawal, Stuti</style></author><author><style face="normal" font="default" size="100%">An, Maksim</style></author><author><style face="normal" font="default" size="100%">Angelakos, James C.</style></author><author><style face="normal" font="default" size="100%">Arya, Renuka</style></author><author><style face="normal" font="default" size="100%">Bajari, Rosita</style></author><author><style face="normal" font="default" size="100%">Baqar, Bilal</style></author><author><style face="normal" font="default" size="100%">Barnowski, Justin H. 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Rob</style></author><author><style face="normal" font="default" size="100%">Knights, Dan</style></author><author><style face="normal" font="default" size="100%">Koren, Omry</style></author><author><style face="normal" font="default" size="100%">Kwon, Douglas S</style></author><author><style face="normal" font="default" size="100%">Langille, Morgan</style></author><author><style face="normal" font="default" size="100%">Lindsay, Brianna</style></author><author><style face="normal" font="default" size="100%">McGovern, Dermot</style></author><author><style face="normal" font="default" size="100%">McHardy, Alice C</style></author><author><style face="normal" font="default" size="100%">McWeeney, Shannon</style></author><author><style face="normal" font="default" size="100%">Mueller, Noel T</style></author><author><style face="normal" font="default" size="100%">Nezi, Luigi</style></author><author><style face="normal" font="default" size="100%">Olm, Matthew</style></author><author><style face="normal" font="default" size="100%">Palm, Noah</style></author><author><style face="normal" font="default" size="100%">Pasolli, Edoardo</style></author><author><style face="normal" font="default" size="100%">Raes, Jeroen</style></author><author><style face="normal" font="default" size="100%">Redinbo, Matthew R</style></author><author><style face="normal" font="default" size="100%">Rühlemann, Malte</style></author><author><style face="normal" font="default" size="100%">Balfour Sartor, R</style></author><author><style face="normal" font="default" size="100%">Schloss, Patrick D</style></author><author><style face="normal" font="default" size="100%">Schriml, Lynn</style></author><author><style face="normal" font="default" size="100%">Segal, Eran</style></author><author><style face="normal" font="default" size="100%">Shardell, Michelle</style></author><author><style face="normal" font="default" size="100%">Sharpton, Thomas</style></author><author><style face="normal" font="default" size="100%">Smirnova, Ekaterina</style></author><author><style face="normal" font="default" size="100%">Sokol, Harry</style></author><author><style face="normal" font="default" size="100%">Sonnenburg, Justin L</style></author><author><style face="normal" font="default" size="100%">Srinivasan, Sujatha</style></author><author><style face="normal" font="default" size="100%">Thingholm, Louise B</style></author><author><style face="normal" font="default" size="100%">Turnbaugh, Peter J</style></author><author><style face="normal" font="default" size="100%">Upadhyay, Vaibhav</style></author><author><style face="normal" font="default" size="100%">Walls, Ramona L</style></author><author><style face="normal" font="default" size="100%">Wilmes, Paul</style></author><author><style face="normal" font="default" size="100%">Yamada, Takuji</style></author><author><style face="normal" font="default" size="100%">Zeller, Georg</style></author><author><style face="normal" font="default" size="100%">Zhang, Mingyu</style></author><author><style face="normal" font="default" size="100%">Zhao, Ni</style></author><author><style face="normal" font="default" size="100%">Zhao, Liping</style></author><author><style face="normal" font="default" size="100%">Bao, Wenjun</style></author><author><style face="normal" font="default" size="100%">Culhane, Aedin</style></author><author><style face="normal" font="default" size="100%">Devanarayan, Viswanath</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Fan, Xiaohui</style></author><author><style face="normal" font="default" size="100%">Fischer, Matthias</style></author><author><style face="normal" font="default" size="100%">Jones, Wendell</style></author><author><style face="normal" font="default" size="100%">Kusko, Rebecca</style></author><author><style face="normal" font="default" size="100%">Mason, Christopher E</style></author><author><style face="normal" font="default" size="100%">Mercer, Tim R</style></author><author><style face="normal" font="default" size="100%">Sansone, Susanna-Assunta</style></author><author><style face="normal" font="default" size="100%">Scherer, Andreas</style></author><author><style face="normal" font="default" size="100%">Shi, Leming</style></author><author><style face="normal" font="default" size="100%">Thakkar, Shraddha</style></author><author><style face="normal" font="default" size="100%">Tong, Weida</style></author><author><style face="normal" font="default" size="100%">Wolfinger, Russ</style></author><author><style face="normal" font="default" size="100%">Hunter, Christopher</style></author><author><style face="normal" font="default" size="100%">Segata, Nicola</style></author><author><style face="normal" font="default" size="100%">Huttenhower, Curtis</style></author><author><style face="normal" font="default" size="100%">Dowd, Jennifer B</style></author><author><style face="normal" font="default" size="100%">Jones, Heidi E</style></author><author><style face="normal" font="default" size="100%">Waldron, Levi</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">Genomic Standards Consortium</style></author><author><style face="normal" font="default" size="100%">Massive Analysis and Quality Control Society</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Reporting guidelines for human microbiome research: the STORMS checklist.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Dysbiosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Microbiota</style></keyword><keyword><style  face="normal" font="default" size="100%">Observational Studies as Topic</style></keyword><keyword><style  face="normal" font="default" size="100%">Research Design</style></keyword><keyword><style  face="normal" font="default" size="100%">Translational Science, Biomedical</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">1885-1892</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34789871?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tenorio-Castaño, Jair</style></author><author><style face="normal" font="default" size="100%">Morte, Beatriz</style></author><author><style face="normal" font="default" size="100%">Nevado, Julián</style></author><author><style face="normal" font="default" size="100%">Martínez-Glez, Víctor</style></author><author><style face="normal" font="default" size="100%">Santos-Simarro, Fernando</style></author><author><style face="normal" font="default" size="100%">García-Miñaur, Sixto</style></author><author><style face="normal" font="default" size="100%">Palomares-Bralo, María</style></author><author><style face="normal" font="default" size="100%">Pacio-Míguez, Marta</style></author><author><style face="normal" font="default" size="100%">Gómez, Beatriz</style></author><author><style face="normal" font="default" size="100%">Arias, Pedro</style></author><author><style face="normal" font="default" size="100%">Alcochea, Alba</style></author><author><style face="normal" font="default" size="100%">Carrión, Juan</style></author><author><style face="normal" font="default" size="100%">Arias, Patricia</style></author><author><style face="normal" font="default" size="100%">Almoguera, Berta</style></author><author><style face="normal" font="default" size="100%">López-Grondona, Fermina</style></author><author><style face="normal" font="default" size="100%">Lorda-Sanchez, Isabel</style></author><author><style face="normal" font="default" size="100%">Galán-Gómez, Enrique</style></author><author><style face="normal" font="default" size="100%">Valenzuela, Irene</style></author><author><style face="normal" font="default" size="100%">Méndez Perez, María</style></author><author><style face="normal" font="default" size="100%">Cuscó, Ivón</style></author><author><style face="normal" font="default" size="100%">Barros, Francisco</style></author><author><style face="normal" font="default" size="100%">Pié, Juan</style></author><author><style face="normal" font="default" size="100%">Ramos, Sergio</style></author><author><style face="normal" font="default" size="100%">Ramos, Feliciano</style></author><author><style face="normal" font="default" size="100%">Kuechler, Alma</style></author><author><style face="normal" font="default" size="100%">Tizzano, Eduardo</style></author><author><style face="normal" font="default" size="100%">Ayuso, Carmen</style></author><author><style face="normal" font="default" size="100%">Kaiser, Frank</style></author><author><style face="normal" font="default" size="100%">Pérez-Jurado, Luis</style></author><author><style face="normal" font="default" size="100%">Carracedo, Ángel</style></author><author><style face="normal" font="default" size="100%">Lapunzina, Pablo</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">The ENoD-CIBERER Consortium</style></author><author><style face="normal" font="default" size="100%">The SIDE Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Schuurs–Hoeijmakers Syndrome (PACS1 Neurodevelopmental Disorder): Seven Novel Patients and a Review</style></title><secondary-title><style face="normal" font="default" size="100%">Genes</style></secondary-title><short-title><style face="normal" font="default" size="100%">Genes</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-05-2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/2073-4425/12/5/738https://www.mdpi.com/2073-4425/12/5/738/pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">738</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Méndez-Salazar, Eder Orlando</style></author><author><style face="normal" font="default" size="100%">Vázquez-Mellado, Janitzia</style></author><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Zamudio-Cuevas, Yessica</style></author><author><style face="normal" font="default" size="100%">Francisco-Balderas, Adriana</style></author><author><style face="normal" font="default" size="100%">Martínez-Flores, Karina</style></author><author><style face="normal" font="default" size="100%">Fernández-Torres, Javier</style></author><author><style face="normal" font="default" size="100%">Lozada-Pérez, Carlos</style></author><author><style face="normal" font="default" size="100%">Pineda, Carlos</style></author><author><style face="normal" font="default" size="100%">Sánchez-González, Austreberto</style></author><author><style face="normal" font="default" size="100%">Silveira, Luis H</style></author><author><style face="normal" font="default" size="100%">Burguete-García, Ana I</style></author><author><style face="normal" font="default" size="100%">Orbe-Orihuela, Citlalli</style></author><author><style face="normal" font="default" size="100%">Lagunas-Martínez, Alfredo</style></author><author><style face="normal" font="default" size="100%">Vazquez-Gomez, Alonso</style></author><author><style face="normal" font="default" size="100%">López-Reyes, Alberto</style></author><author><style face="normal" font="default" size="100%">Palacios-González, Berenice</style></author><author><style face="normal" font="default" size="100%">Martínez-Nava, Gabriela Angélica</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biodiversity</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Dysbiosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Gastrointestinal Microbiome</style></keyword><keyword><style  face="normal" font="default" size="100%">Gout</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Metagenome</style></keyword><keyword><style  face="normal" font="default" size="100%">metagenomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Maps</style></keyword><keyword><style  face="normal" font="default" size="100%">Uric Acid</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 05 24</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">50</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;OBJECTIVE: &lt;/b&gt;To evaluate the taxonomic composition of the gut microbiome in gout patients with and without tophi formation, and predict bacterial functions that might have an impact on urate metabolism.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Hypervariable V3-V4 regions of the bacterial 16S rRNA gene from fecal samples of gout patients with and without tophi (n = 33 and n = 25, respectively) were sequenced and compared to fecal samples from 53 healthy controls. We explored predictive functional profiles using bioinformatics in order to identify differences in taxonomy and metabolic pathways.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We identified a microbiome characterized by the lowest richness and a higher abundance of Phascolarctobacterium, Bacteroides, Akkermansia, and Ruminococcus_gnavus_group genera in patients with gout without tophi when compared to controls. The Proteobacteria phylum and the Escherichia-Shigella genus were more abundant in patients with tophaceous gout than in controls. Fold change analysis detected nine genera enriched in healthy controls compared to gout groups (Bifidobacterium, Butyricicoccus, Oscillobacter, Ruminococcaceae_UCG_010, Lachnospiraceae_ND2007_group, Haemophilus, Ruminococcus_1, Clostridium_sensu_stricto_1, and Ruminococcaceae_UGC_013). We found that the core microbiota of both gout groups shared Bacteroides caccae, Bacteroides stercoris ATCC 43183, and Bacteroides coprocola DSM 17136. These bacteria might perform functions linked to one-carbon metabolism, nucleotide binding, amino acid biosynthesis, and purine biosynthesis. Finally, we observed differences in key bacterial enzymes involved in urate synthesis, degradation, and elimination.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Our findings revealed that taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34030623?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Garrido-Rodriguez, Martín</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco M</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Muñoz, Eduardo</style></author><author><style face="normal" font="default" size="100%">Calzado, Marco A</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Theoretical</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-seq</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">whole exome sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Workflow</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">e1008748</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33571195?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Golubnitschaja, Olga</style></author><author><style face="normal" font="default" size="100%">Topolcan, Ondrej</style></author><author><style face="normal" font="default" size="100%">Kucera, Radek</style></author><author><style face="normal" font="default" size="100%">Costigliola, Vincenzo</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">EPMA</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">10th Anniversary of the European Association for Predictive, Preventive and Personalised (3P) Medicine - EPMA World Congress Supplement 2020.</style></title><secondary-title><style face="normal" font="default" size="100%">EPMA J</style></secondary-title><alt-title><style face="normal" font="default" size="100%">EPMA J</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 Aug 19</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">1-133</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In 2019, the EPMA celebrated its 10th anniversary at the 5th World Congress in Pilsen, Czech Republic. The history of the International Professional Network dedicated to Predictive, Preventive and Personalised Medicine (PPPM / 3PM) is rich in achievements. Facing the coronavirus COVID-19 pandemic it is getting evident globally that the predictive approach, targeted prevention and personalisation of medical services is the optimal paradigm in healthcare demonstrating the high potential to save lives and to benefit the society as a whole. The EPMA World Congress Supplement 2020 highlights advances in 3P medicine.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Díez-Fuertes, F</style></author><author><style face="normal" font="default" size="100%">De La Torre-Tarazona, H E</style></author><author><style face="normal" font="default" size="100%">Calonge, E</style></author><author><style face="normal" font="default" size="100%">Pernas, M</style></author><author><style face="normal" font="default" size="100%">Bermejo, M</style></author><author><style face="normal" font="default" size="100%">García-Pérez, J</style></author><author><style face="normal" font="default" size="100%">Álvarez, A</style></author><author><style face="normal" font="default" size="100%">Capa, L</style></author><author><style face="normal" font="default" size="100%">García-García, F</style></author><author><style face="normal" font="default" size="100%">Saumoy, M</style></author><author><style face="normal" font="default" size="100%">Riera, M</style></author><author><style face="normal" font="default" size="100%">Boland-Auge, A</style></author><author><style face="normal" font="default" size="100%">López-Galíndez, C</style></author><author><style face="normal" font="default" size="100%">Lathrop, M</style></author><author><style face="normal" font="default" size="100%">Dopazo, J</style></author><author><style face="normal" font="default" size="100%">Sakuntabhai, A</style></author><author><style face="normal" font="default" size="100%">Alcamí, J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Association of a single nucleotide polymorphism in the ubxn6 gene with long-term non-progression phenotype in HIV-positive individuals.</style></title><secondary-title><style face="normal" font="default" size="100%">Clin Microbiol Infect</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Clin Microbiol Infect</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adaptor Proteins, Vesicular Transport</style></keyword><keyword><style  face="normal" font="default" size="100%">Autophagy-Related Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Caveolin 1</style></keyword><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Dendritic Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease Progression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Knockdown Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Association Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">HeLa Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">HIV Infections</style></keyword><keyword><style  face="normal" font="default" size="100%">HIV Long-Term Survivors</style></keyword><keyword><style  face="normal" font="default" size="100%">HIV-1</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Macrophages</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">whole exome sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">107-114</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;OBJECTIVES: &lt;/b&gt;The long-term non-progressors (LTNPs) are a heterogeneous group of HIV-positive individuals characterized by their ability to maintain high CD4 T-cell counts and partially control viral replication for years in the absence of antiretroviral therapy. The present study aims to identify host single nucleotide polymorphisms (SNPs) associated with non-progression in a cohort of 352 individuals.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;DNA microarrays and exome sequencing were used for genotyping about 240 000 functional polymorphisms throughout more than 20 000 human genes. The allele frequencies of 85 LTNPs were compared with a control population. SNPs associated with LTNPs were confirmed in a population of typical progressors. Functional analyses in the affected gene were carried out through knockdown experiments in HeLa-P4, macrophages and dendritic cells.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Several SNPs located within the major histocompatibility complex region previously related to LTNPs were confirmed in this new cohort. The SNP rs1127888 (UBXN6) surpassed the statistical significance of these markers after Bonferroni correction (q = 2.11 × 10). An uncommon allelic frequency of rs1127888 among LTNPs was confirmed by comparison with typical progressors and other publicly available populations. UBXN6 knockdown experiments caused an increase in CAV1 expression and its accumulation in the plasma membrane. In vitro infection of different cell types with HIV-1 replication-competent recombinant viruses caused a reduction of the viral replication capacity compared with their corresponding wild-type cells expressing UBXN6.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;A higher prevalence of Ala31Thr in UBXN6 was found among LTNPs within its N-terminal region, which is crucial for UBXN6/VCP protein complex formation. UBXN6 knockdown affected CAV1 turnover and HIV-1 replication capacity.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31158522?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yang, Mi</style></author><author><style face="normal" font="default" size="100%">Petralia, Francesca</style></author><author><style face="normal" font="default" size="100%">Li, Zhi</style></author><author><style face="normal" font="default" size="100%">Li, Hongyang</style></author><author><style face="normal" font="default" size="100%">Ma, Weiping</style></author><author><style face="normal" font="default" size="100%">Song, Xiaoyu</style></author><author><style face="normal" font="default" size="100%">Kim, Sunkyu</style></author><author><style face="normal" font="default" size="100%">Lee, Heewon</style></author><author><style face="normal" font="default" size="100%">Yu, Han</style></author><author><style face="normal" font="default" size="100%">Lee, Bora</style></author><author><style face="normal" font="default" size="100%">Bae, Seohui</style></author><author><style face="normal" font="default" size="100%">Heo, Eunji</style></author><author><style face="normal" font="default" size="100%">Kaczmarczyk, Jan</style></author><author><style face="normal" font="default" size="100%">Stępniak, Piotr</style></author><author><style face="normal" font="default" size="100%">Warchoł, Michał</style></author><author><style face="normal" font="default" size="100%">Yu, Thomas</style></author><author><style face="normal" font="default" size="100%">Calinawan, Anna P</style></author><author><style face="normal" font="default" size="100%">Boutros, Paul C</style></author><author><style face="normal" font="default" size="100%">Payne, Samuel H</style></author><author><style face="normal" font="default" size="100%">Reva, Boris</style></author><author><style face="normal" font="default" size="100%">Boja, Emily</style></author><author><style face="normal" font="default" size="100%">Rodriguez, Henry</style></author><author><style face="normal" font="default" size="100%">Stolovitzky, Gustavo</style></author><author><style face="normal" font="default" size="100%">Guan, Yuanfang</style></author><author><style face="normal" font="default" size="100%">Kang, Jaewoo</style></author><author><style face="normal" font="default" size="100%">Wang, Pei</style></author><author><style face="normal" font="default" size="100%">Fenyö, David</style></author><author><style face="normal" font="default" size="100%">Saez-Rodriguez, Julio</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">NCI-CPTAC-DREAM Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics.</style></title><secondary-title><style face="normal" font="default" size="100%">Cell Syst</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cell Syst</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Crowdsourcing</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Phosphoproteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 08 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">186-195.e9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper's transparent peer review process is included in the Supplemental Information.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32710834?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Salgado, David</style></author><author><style face="normal" font="default" size="100%">Armean, Irina M</style></author><author><style face="normal" font="default" size="100%">Baudis, Michael</style></author><author><style face="normal" font="default" size="100%">Beltran, Sergi</style></author><author><style face="normal" font="default" size="100%">Capella-Gutíerrez, Salvador</style></author><author><style face="normal" font="default" size="100%">Carvalho-Silva, Denise</style></author><author><style face="normal" font="default" size="100%">Dominguez Del Angel, Victoria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Furlong, Laura I</style></author><author><style face="normal" font="default" size="100%">Gao, Bo</style></author><author><style face="normal" font="default" size="100%">Garcia, Leyla</style></author><author><style face="normal" font="default" size="100%">Gerloff, Dietlind</style></author><author><style face="normal" font="default" size="100%">Gut, Ivo</style></author><author><style face="normal" font="default" size="100%">Gyenesei, Attila</style></author><author><style face="normal" font="default" size="100%">Habermann, Nina</style></author><author><style face="normal" font="default" size="100%">Hancock, John M</style></author><author><style face="normal" font="default" size="100%">Hanauer, Marc</style></author><author><style face="normal" font="default" size="100%">Hovig, Eivind</style></author><author><style face="normal" font="default" size="100%">Johansson, Lennart F</style></author><author><style face="normal" font="default" size="100%">Keane, Thomas</style></author><author><style face="normal" font="default" size="100%">Korbel, Jan</style></author><author><style face="normal" font="default" size="100%">Lauer, Katharina B</style></author><author><style face="normal" font="default" size="100%">Laurie, Steve</style></author><author><style face="normal" font="default" size="100%">Leskošek, Brane</style></author><author><style face="normal" font="default" size="100%">Lloyd, David</style></author><author><style face="normal" font="default" size="100%">Marqués-Bonet, Tomás</style></author><author><style face="normal" font="default" size="100%">Mei, Hailiang</style></author><author><style face="normal" font="default" size="100%">Monostory, Katalin</style></author><author><style face="normal" font="default" size="100%">Piñero, Janet</style></author><author><style face="normal" font="default" size="100%">Poterlowicz, Krzysztof</style></author><author><style face="normal" font="default" size="100%">Rath, Ana</style></author><author><style face="normal" font="default" size="100%">Samarakoon, Pubudu</style></author><author><style face="normal" font="default" size="100%">Sanz, Ferran</style></author><author><style face="normal" font="default" size="100%">Saunders, Gary</style></author><author><style face="normal" font="default" size="100%">Sie, Daoud</style></author><author><style face="normal" font="default" size="100%">Swertz, Morris A</style></author><author><style face="normal" font="default" size="100%">Tsukanov, Kirill</style></author><author><style face="normal" font="default" size="100%">Valencia, Alfonso</style></author><author><style face="normal" font="default" size="100%">Vidak, Marko</style></author><author><style face="normal" font="default" size="100%">Yenyxe González, Cristina</style></author><author><style face="normal" font="default" size="100%">Ylstra, Bauke</style></author><author><style face="normal" font="default" size="100%">Béroud, Christophe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The ELIXIR Human Copy Number Variations Community: building bioinformatics infrastructure for research.</style></title><secondary-title><style face="normal" font="default" size="100%">F1000Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">F1000Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Copy Number Variations</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Copy number variations (CNVs) are major causative contributors both in the genesis of genetic diseases and human neoplasias. While &quot;High-Throughput&quot; sequencing technologies are increasingly becoming the primary choice for genomic screening analysis, their ability to efficiently detect CNVs is still heterogeneous and remains to be developed. The aim of this white paper is to provide a guiding framework for the future contributions of ELIXIR's recently established   with implications beyond human disease diagnostics and population genomics. This white paper is the direct result of a strategy meeting that took place in September 2018 in Hinxton (UK) and involved representatives of 11 ELIXIR Nodes. The meeting led to the definition of priority objectives and tasks, to address a wide range of CNV-related challenges ranging from detection and interpretation to sharing and training. Here, we provide suggestions on how to align these tasks within the ELIXIR Platforms strategy, and on how to frame the activities of this new ELIXIR Community in the international context.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34367618?dopt=Abstract</style></custom1><section><style face="normal" font="default" size="100%">1229</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Palomero, Luis</style></author><author><style face="normal" font="default" size="100%">Galván-Femenía, Ivan</style></author><author><style face="normal" font="default" size="100%">de Cid, Rafael</style></author><author><style face="normal" font="default" size="100%">Espín, Roderic</style></author><author><style face="normal" font="default" size="100%">Barnes, Daniel R</style></author><author><style face="normal" font="default" size="100%">Blommaert, Eline</style></author><author><style face="normal" font="default" size="100%">Gil-Gil, Miguel</style></author><author><style face="normal" font="default" size="100%">Falo, Catalina</style></author><author><style face="normal" font="default" size="100%">Stradella, Agostina</style></author><author><style face="normal" font="default" size="100%">Ouchi, Dan</style></author><author><style face="normal" font="default" size="100%">Roso-Llorach, Albert</style></author><author><style face="normal" font="default" size="100%">Violan, Concepció</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Extremera, Ana Isabel</style></author><author><style face="normal" font="default" size="100%">García-Valero, Mar</style></author><author><style face="normal" font="default" size="100%">Herranz, Carmen</style></author><author><style face="normal" font="default" size="100%">Mateo, Francesca</style></author><author><style face="normal" font="default" size="100%">Mereu, Elisabetta</style></author><author><style face="normal" font="default" size="100%">Beesley, Jonathan</style></author><author><style face="normal" font="default" size="100%">Chenevix-Trench, Georgia</style></author><author><style face="normal" font="default" size="100%">Roux, Cecilia</style></author><author><style face="normal" font="default" size="100%">Mak, Tak</style></author><author><style face="normal" font="default" size="100%">Brunet, Joan</style></author><author><style face="normal" font="default" size="100%">Hakem, Razq</style></author><author><style face="normal" font="default" size="100%">Gorrini, Chiara</style></author><author><style face="normal" font="default" size="100%">Antoniou, Antonis C</style></author><author><style face="normal" font="default" size="100%">Lázaro, Conxi</style></author><author><style face="normal" font="default" size="100%">Pujana, Miquel Angel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Immune Cell Associations with Cancer Risk.</style></title><secondary-title><style face="normal" font="default" size="100%">iScience</style></secondary-title><alt-title><style face="normal" font="default" size="100%">iScience</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 Jul 24</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">101296</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Proper immune system function hinders cancer development, but little is known about whether genetic variants linked to cancer risk alter immune cells. Here, we report 57 cancer risk loci associated with differences in immune and/or stromal cell contents in the corresponding tissue. Predicted target genes show expression and regulatory associations with immune features. Polygenic risk scores also reveal associations with immune and/or stromal cell contents, and breast cancer scores show consistent results in normal and tumor tissue. SH2B3 links peripheral alterations of several immune cell types to the risk of this malignancy. Pleiotropic SH2B3 variants are associated with breast cancer risk in BRCA1/2 mutation carriers. A retrospective case-cohort study indicates a positive association between blood counts of basophils, leukocytes, and monocytes and age at breast cancer diagnosis. These findings broaden our knowledge of the role of the immune system in cancer and highlight promising prevention strategies for individuals at high risk.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32622267?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Can, Fatma E</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments.</style></title><secondary-title><style face="normal" font="default" size="100%">Cells</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cells</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 06 29</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Despite the existence of differences in gene expression across numerous genes between males and females having been known for a long time, these have been mostly ignored in many studies, including drug development and its therapeutic use. In fact, the consequences of such differences over the disease mechanisms or the drug action mechanisms are completely unknown. Here we applied mechanistic mathematical models of signaling activity to reveal the ultimate functional consequences that gender-specific gene expression activities have over cell functionality and fate. Moreover, we also used the mechanistic modeling framework to simulate the drug interventions and unravel how drug action mechanisms are affected by gender-specific differential gene expression. Interestingly, some cancers have many biological processes significantly affected by these gender-specific differences (e.g., bladder or head and neck carcinomas), while others (e.g., glioblastoma or rectum cancer) are almost insensitive to them. We found that many of these gender-specific differences affect cancer-specific pathways or in physiological signaling pathways, also involved in cancer origin and development. Finally, mechanistic models have the potential to be used for finding alternative therapeutic interventions on the pathways targeted by the drug, which lead to similar results compensating the downstream consequences of gender-specific differences in gene expression.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32610626?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin-Broto, Javier</style></author><author><style face="normal" font="default" size="100%">Hindi, Nadia</style></author><author><style face="normal" font="default" size="100%">Grignani, Giovanni</style></author><author><style face="normal" font="default" size="100%">Martinez-Trufero, Javier</style></author><author><style face="normal" font="default" size="100%">Redondo, Andres</style></author><author><style face="normal" font="default" size="100%">Valverde, Claudia</style></author><author><style face="normal" font="default" size="100%">Stacchiotti, Silvia</style></author><author><style face="normal" font="default" size="100%">Lopez-Pousa, Antonio</style></author><author><style face="normal" font="default" size="100%">D'Ambrosio, Lorenzo</style></author><author><style face="normal" font="default" size="100%">Gutierrez, Antonio</style></author><author><style face="normal" font="default" size="100%">Perez-Vega, Herminia</style></author><author><style face="normal" font="default" size="100%">Encinas-Tobajas, Victor</style></author><author><style face="normal" font="default" size="100%">de Alava, Enrique</style></author><author><style face="normal" font="default" size="100%">Collini, Paola</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Carrasco-Garcia, Irene</style></author><author><style face="normal" font="default" size="100%">Lopez-Alvarez, Maria</style></author><author><style face="normal" font="default" size="100%">Moura, David S</style></author><author><style face="normal" font="default" size="100%">Lopez-Martin, Jose A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Nivolumab and sunitinib combination in advanced soft tissue sarcomas: a multicenter, single-arm, phase Ib/II trial.</style></title><secondary-title><style face="normal" font="default" size="100%">J Immunother Cancer</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Immunother Cancer</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Antineoplastic Agents, Immunological</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Nivolumab</style></keyword><keyword><style  face="normal" font="default" size="100%">Sarcoma</style></keyword><keyword><style  face="normal" font="default" size="100%">Sunitinib</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Sarcomas exhibit low expression of factors related to immune response, which could explain the modest activity of PD-1 inhibitors. A potential strategy to convert a cold into an inflamed microenvironment lies on a combination therapy. As tumor angiogenesis promotes immunosuppression, we designed a phase Ib/II trial to test the double inhibition of angiogenesis (sunitinib) and PD-1/PD-L1 axis (nivolumab).&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;This single-arm, phase Ib/II trial enrolled adult patients with selected subtypes of sarcoma. Phase Ib established two dose levels: level 0 with sunitinib 37.5 mg daily from day 1, plus nivolumab 3 mg/kg intravenously on day 15, and then every 2 weeks; and level -1 with sunitinib 37.5 mg on the first 14 days (induction) and then 25 mg per day plus nivolumab on the same schedule. The primary endpoint was to determine the recommended dose for phase II (phase I) and the 6-month progression-free survival rate, according to Response Evaluation Criteria in Solid Tumors 1.1 (phase II).&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;From May 2017 to April 2019, 68 patients were enrolled: 16 in phase Ib and 52 in phase II. The recommended dose of sunitinib for phase II was 37.5 mg as induction and then 25 mg in combination with nivolumab. After a median follow-up of 17 months (4-26), the 6-month progression-free survival rate was 48% (95% CI 41% to 55%). The most common grade 3-4 adverse events included transaminitis (17.3%) and neutropenia (11.5%).&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Sunitinib plus nivolumab is an active scheme with manageable toxicity in the treatment of selected patients with advanced soft tissue sarcoma, with almost half of patients free of progression at 6 months. NCT03277924.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33203665?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bogliolo, Massimo</style></author><author><style face="normal" font="default" size="100%">Pujol, Roser</style></author><author><style face="normal" font="default" size="100%">Aza-Carmona, Miriam</style></author><author><style face="normal" font="default" size="100%">Muñoz-Subirana, Núria</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Santiago, Benjamin</style></author><author><style face="normal" font="default" size="100%">Casado, José Antonio</style></author><author><style face="normal" font="default" size="100%">Rio, Paula</style></author><author><style face="normal" font="default" size="100%">Bauser, Christopher</style></author><author><style face="normal" font="default" size="100%">Reina-Castillón, Judith</style></author><author><style face="normal" font="default" size="100%">Lopez-Sanchez, Marcos</style></author><author><style face="normal" font="default" size="100%">Gonzalez-Quereda, Lidia</style></author><author><style face="normal" font="default" size="100%">Gallano, Pia</style></author><author><style face="normal" font="default" size="100%">Catalá, Albert</style></author><author><style face="normal" font="default" size="100%">Ruiz-Llobet, Ana</style></author><author><style face="normal" font="default" size="100%">Badell, Isabel</style></author><author><style face="normal" font="default" size="100%">Diaz-Heredia, Cristina</style></author><author><style face="normal" font="default" size="100%">Hladun, Raquel</style></author><author><style face="normal" font="default" size="100%">Senent, Leonort</style></author><author><style face="normal" font="default" size="100%">Argiles, Bienvenida</style></author><author><style face="normal" font="default" size="100%">Bergua Burgues, Juan Miguel</style></author><author><style face="normal" font="default" size="100%">Bañez, Fatima</style></author><author><style face="normal" font="default" size="100%">Arrizabalaga, Beatriz</style></author><author><style face="normal" font="default" size="100%">López Almaraz, Ricardo</style></author><author><style face="normal" font="default" size="100%">Lopez, Monica</style></author><author><style face="normal" font="default" size="100%">Figuera, Ángela</style></author><author><style face="normal" font="default" size="100%">Molinés, Antonio</style></author><author><style face="normal" font="default" size="100%">Pérez de Soto, Inmaculada</style></author><author><style face="normal" font="default" size="100%">Hernando, Inés</style></author><author><style face="normal" font="default" size="100%">Muñoz, Juan Antonio</style></author><author><style face="normal" font="default" size="100%">Del Rosario Marin, Maria</style></author><author><style face="normal" font="default" size="100%">Balmaña, Judith</style></author><author><style face="normal" font="default" size="100%">Stjepanovic, Neda</style></author><author><style face="normal" font="default" size="100%">Carrasco, Estela</style></author><author><style face="normal" font="default" size="100%">Cuesta, Isabel</style></author><author><style face="normal" font="default" size="100%">Cosuelo, José Miguel</style></author><author><style face="normal" font="default" size="100%">Regueiro, Alexandra</style></author><author><style face="normal" font="default" size="100%">Moraleda Jimenez, José</style></author><author><style face="normal" font="default" size="100%">Galera-Miñarro, Ana Maria</style></author><author><style face="normal" font="default" size="100%">Rosiñol, Laura</style></author><author><style face="normal" font="default" size="100%">Carrió, Anna</style></author><author><style face="normal" font="default" size="100%">Beléndez-Bieler, Cristina</style></author><author><style face="normal" font="default" size="100%">Escudero Soto, Antonio</style></author><author><style face="normal" font="default" size="100%">Cela, Elena</style></author><author><style face="normal" font="default" size="100%">de la Mata, Gregorio</style></author><author><style face="normal" font="default" size="100%">Fernández-Delgado, Rafael</style></author><author><style face="normal" font="default" size="100%">Garcia-Pardos, Maria Carmen</style></author><author><style face="normal" font="default" size="100%">Sáez-Villaverde, Raquel</style></author><author><style face="normal" font="default" size="100%">Barragaño, Marta</style></author><author><style face="normal" font="default" size="100%">Portugal, Raquel</style></author><author><style face="normal" font="default" size="100%">Lendinez, Francisco</style></author><author><style face="normal" font="default" size="100%">Hernadez, Ines</style></author><author><style face="normal" font="default" size="100%">Vagace, José Manue</style></author><author><style face="normal" font="default" size="100%">Tapia, Maria</style></author><author><style face="normal" font="default" size="100%">Nieto, José</style></author><author><style face="normal" font="default" size="100%">Garcia, Marta</style></author><author><style face="normal" font="default" size="100%">Gonzalez, Macarena</style></author><author><style face="normal" font="default" size="100%">Vicho, Cristina</style></author><author><style face="normal" font="default" size="100%">Galvez, Eva</style></author><author><style face="normal" font="default" size="100%">Valiente, Alberto</style></author><author><style face="normal" font="default" size="100%">Antelo, Maria Luisa</style></author><author><style face="normal" font="default" size="100%">Ancliff, Phil</style></author><author><style face="normal" font="default" size="100%">García, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Sevilla, Julian</style></author><author><style face="normal" font="default" size="100%">Paprotka, Tobias</style></author><author><style face="normal" font="default" size="100%">Pérez-Jurado, Luis Alberto</style></author><author><style face="normal" font="default" size="100%">Bueren, Juan</style></author><author><style face="normal" font="default" size="100%">Surralles, Jordi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimised molecular genetic diagnostics of Fanconi anaemia by whole exome sequencing and functional studies.</style></title><secondary-title><style face="normal" font="default" size="100%">J Med Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Med Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cell Line</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Copy Number Variations</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Repair</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA-Binding Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Fanconi Anemia</style></keyword><keyword><style  face="normal" font="default" size="100%">Fanconi Anemia Complementation Group A Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Knockout Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mutation, Missense</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">whole exome sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 04</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">57</style></volume><pages><style face="normal" font="default" size="100%">258-268</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;PURPOSE: &lt;/b&gt;Patients with Fanconi anaemia (FA), a rare DNA repair genetic disease, exhibit chromosome fragility, bone marrow failure, malformations and cancer susceptibility. FA molecular diagnosis is challenging since FA is caused by point mutations and large deletions in 22 genes following three heritability patterns. To optimise FA patients' characterisation, we developed a simplified but effective methodology based on whole exome sequencing (WES) and functional studies.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;68 patients with FA were analysed by commercial WES services. Copy number variations were evaluated by sequencing data analysis with RStudio. To test  missense variants, wt FANCA cDNA was cloned and variants were introduced by site-directed mutagenesis. Vectors were then tested for their ability to complement DNA repair defects of a FANCA-KO human cell line generated by TALEN technologies.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We identified 93.3% of mutated alleles including large deletions. We determined the pathogenicity of three FANCA missense variants and demonstrated that two  variants reported in mutations databases as 'affecting functions' are SNPs. Deep analysis of sequencing data revealed patients' true mutations, highlighting the importance of functional analysis. In one patient, no pathogenic variant could be identified in any of the 22 known FA genes, and in seven patients, only one deleterious variant could be identified (three patients each with FANCA and FANCD2 and one patient with FANCE mutations) CONCLUSION: WES and proper bioinformatics analysis are sufficient to effectively characterise patients with FA regardless of the rarity of their complementation group, type of mutations, mosaic condition and DNA source.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31586946?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin-Broto, Javier</style></author><author><style face="normal" font="default" size="100%">Cruz, Josefina</style></author><author><style face="normal" font="default" size="100%">Penel, Nicolas</style></author><author><style face="normal" font="default" size="100%">Le Cesne, Axel</style></author><author><style face="normal" font="default" size="100%">Hindi, Nadia</style></author><author><style face="normal" font="default" size="100%">Luna, Pablo</style></author><author><style face="normal" font="default" size="100%">Moura, David S</style></author><author><style face="normal" font="default" size="100%">Bernabeu, Daniel</style></author><author><style face="normal" font="default" size="100%">de Alava, Enrique</style></author><author><style face="normal" font="default" size="100%">Lopez-Guerrero, Jose Antonio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Gutierrez, Antonio</style></author><author><style face="normal" font="default" size="100%">Collini, Paola</style></author><author><style face="normal" font="default" size="100%">Karanian, Marie</style></author><author><style face="normal" font="default" size="100%">Redondo, Andres</style></author><author><style face="normal" font="default" size="100%">Lopez-Pousa, Antonio</style></author><author><style face="normal" font="default" size="100%">Grignani, Giovanni</style></author><author><style face="normal" font="default" size="100%">Diaz-Martin, Juan</style></author><author><style face="normal" font="default" size="100%">Marcilla, David</style></author><author><style face="normal" font="default" size="100%">Fernandez-Serra, Antonio</style></author><author><style face="normal" font="default" size="100%">Gonzalez-Aguilera, Cristina</style></author><author><style face="normal" font="default" size="100%">Casali, Paolo G</style></author><author><style face="normal" font="default" size="100%">Blay, Jean-Yves</style></author><author><style face="normal" font="default" size="100%">Stacchiotti, Silvia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pazopanib for treatment of typical solitary fibrous tumours: a multicentre, single-arm, phase 2 trial.</style></title><secondary-title><style face="normal" font="default" size="100%">Lancet Oncol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Lancet Oncol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Follow-Up Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Indazoles</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasm Metastasis</style></keyword><keyword><style  face="normal" font="default" size="100%">Prognosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Prospective Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Kinase Inhibitors</style></keyword><keyword><style  face="normal" font="default" size="100%">Pyrimidines</style></keyword><keyword><style  face="normal" font="default" size="100%">Response Evaluation Criteria in Solid Tumors</style></keyword><keyword><style  face="normal" font="default" size="100%">Solitary Fibrous Tumors</style></keyword><keyword><style  face="normal" font="default" size="100%">Sulfonamides</style></keyword><keyword><style  face="normal" font="default" size="100%">Survival Rate</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 03</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">456-466</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Solitary fibrous tumour is an ultra-rare sarcoma, which encompasses different clinicopathological subgroups. The dedifferentiated subgroup shows an aggressive course with resistance to pazopanib, whereas in the malignant subgroup, pazopanib shows higher activity than in previous studies with chemotherapy. We designed a trial to test pazopanib activity in two different cohorts of solitary fibrous tumour: the malignant-dedifferentiated cohort, which was previously published, and the typical cohort, which is presented here.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;In this single-arm, phase 2 trial, adult patients (aged ≥18 years) diagnosed with confirmed metastatic or unresectable typical solitary fibrous tumour of any location, who had progressed in the previous 6 months (by Choi criteria or Response Evaluation Criteria in Solid Tumors [RECIST]) and an Eastern Cooperative Oncology Group (ECOG) performance status of 0-2 were enrolled at 11 tertiary hospitals in Italy, France, and Spain. Patients received pazopanib 800 mg once daily, taken orally, until progression, unacceptable toxicity, withdrawal of consent, non-compliance, or a delay in pazopanib administration of longer than 3 weeks. The primary endpoint was proportion of patients achieving an overall response measured by Choi criteria in patients who received at least 1 month of treatment with at least one radiological assessment. All patients who received at least one dose of the study drug were included in the safety analyses. This study is registered in ClinicalTrials.gov, NCT02066285, and with the European Clinical Trials Database, EudraCT 2013-005456-15.&lt;/p&gt;&lt;p&gt;&lt;b&gt;FINDINGS: &lt;/b&gt;From June 26, 2014, to Dec 13, 2018, of 40 patients who were assessed, 34 patients were enrolled and 31 patients were included in the response analysis. Median follow-up was 18 months (IQR 14-34), and 18 (58%) of 31 patients had a partial response, 12 (39%) had stable disease, and one (3%) showed progressive disease according to Choi criteria and central review. The proportion of overall response based on Choi criteria was 58% (95% CI 34-69). There were no deaths caused by toxicity, and the most frequent adverse events were diarrhoea (18 [53%] of 34 patients), fatigue (17 [50%]), and hypertension (17 [50%]).&lt;/p&gt;&lt;p&gt;&lt;b&gt;INTERPRETATION: &lt;/b&gt;To our knowledge, this is the first prospective trial of pazopanib for advanced typical solitary fibrous tumour. The manageable toxicity and activity shown by pazopanib in this cohort suggest that this drug could be considered as first-line treatment for advanced typical solitary fibrous tumour.&lt;/p&gt;&lt;p&gt;&lt;b&gt;FUNDING: &lt;/b&gt;Spanish Group for Research on Sarcomas (GEIS), Italian Sarcoma Group (ISG), French Sarcoma Group (FSG), GlaxoSmithKline, and Novartis.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32066540?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Aquino, Virginia</style></author><author><style face="normal" font="default" size="100%">Salgado, Josefa</style></author><author><style face="normal" font="default" size="100%">Pasalodos, Sara</style></author><author><style face="normal" font="default" size="100%">Miranda, María</style></author><author><style face="normal" font="default" size="100%">Alonso, Ángel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SMN1 copy-number and sequence variant analysis from next-generation sequencing data.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Mutat</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Mutat</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Base Sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Copy Number Variations</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Survival of Motor Neuron 1 Protein</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 12</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">41</style></volume><pages><style face="normal" font="default" size="100%">2073-2077</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Spinal muscular atrophy (SMA) is a severe neuromuscular autosomal recessive disorder affecting 1/10,000 live births. Most SMA patients present homozygous deletion of SMN1, while the vast majority of SMA carriers present only a single SMN1 copy. The sequence similarity between SMN1 and SMN2, and the complexity of the SMN locus makes the estimation of the SMN1 copy-number by next-generation sequencing (NGS) very difficult. Here, we present SMAca, the first python tool to detect SMA carriers and estimate the absolute SMN1 copy-number using NGS data. Moreover, SMAca takes advantage of the knowledge of certain variants specific to SMN1 duplication to also identify silent carriers. This tool has been validated with a cohort of 326 samples from the Navarra 1000 Genomes Project (NAGEN1000). SMAca was developed with a focus on execution speed and easy installation. This combination makes it especially suitable to be integrated into production NGS pipelines. Source code and documentation are available at https://www.github.com/babelomics/SMAca.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33058415?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Galvez, Juan M</style></author><author><style face="normal" font="default" size="100%">Castillo-Secilla, Daniel</style></author><author><style face="normal" font="default" size="100%">Herrera, Luis J</style></author><author><style face="normal" font="default" size="100%">Valenzuela, Olga</style></author><author><style face="normal" font="default" size="100%">Caba, Octavio</style></author><author><style face="normal" font="default" size="100%">Prados, Jose C</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco M</style></author><author><style face="normal" font="default" size="100%">Rojas, Ignacio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets.</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE J Biomed Health Inform</style></secondary-title><alt-title><style face="normal" font="default" size="100%">IEEE J Biomed Health Inform</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biomarkers, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Diagnosis, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-seq</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin Neoplasms</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 07</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">2119-2130</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligent clinical decision support systems. In this sense, gene expression analysis can help find differentially expressed genes (DEGs) simultaneously discerning multiple skin pathological states in a single test. The integration of multiple heterogeneous transcriptomic datasets requires different pipeline stages to be properly designed: from suitable batch merging and efficient biomarker selection to automated classification assessment. This article presents a novel approach addressing all these technical issues, with the intention of providing new sights about skin cancer diagnosis. Although new future efforts will have to be made in the search for better biomarkers recognizing specific skin pathological states, our study found a panel of 8 highly relevant multiclass DEGs for discerning up to 10 skin pathological states: 2 healthy skin conditions a priori, 2 cataloged precancerous skin diseases and 6 cancerous skin states. Their power of diagnosis over new samples was widely tested by previously well-trained classification models. Robust performance metrics such as overall and mean multiclass F1-score outperformed recognition rates of 94% and 80%, respectively. Clinicians should give special attention to highlighted multiclass DEGs that have high gene expression changes present among them, and understand their biological relationship to different skin pathological states.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31871000?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">León, Carlos</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Llames, Sara</style></author><author><style face="normal" font="default" size="100%">García-Pérez, Eva</style></author><author><style face="normal" font="default" size="100%">Carretero, Marta</style></author><author><style face="normal" font="default" size="100%">Arriba, María Del Carmen</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Del Rio, Marcela</style></author><author><style face="normal" font="default" size="100%">Escamez, Maria José</style></author><author><style face="normal" font="default" size="100%">Martínez-Santamaría, Lucía</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transcriptomic Analysis of a Diabetic Skin-Humanized Mouse Model Dissects Molecular Pathways Underlying the Delayed Wound Healing Response.</style></title><secondary-title><style face="normal" font="default" size="100%">Genes (Basel)</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genes (Basel)</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Diabetes Mellitus, Experimental</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolic Networks and Pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice, Nude</style></keyword><keyword><style  face="normal" font="default" size="100%">Microarray Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Annotation</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal Component Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin Transplantation</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin Ulcer</style></keyword><keyword><style  face="normal" font="default" size="100%">Streptozocin</style></keyword><keyword><style  face="normal" font="default" size="100%">Tissue Engineering</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">Transplantation, Heterologous</style></keyword><keyword><style  face="normal" font="default" size="100%">Wound Healing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 12 31</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Defective healing leading to cutaneous ulcer formation is one of the most feared complications of diabetes due to its consequences on patients' quality of life and on the healthcare system. A more in-depth analysis of the underlying molecular pathophysiology is required to develop effective healing-promoting therapies for those patients. Major architectural and functional differences with human epidermis limit extrapolation of results coming from rodents and other small mammal-healing models. Therefore, the search for reliable humanized models has become mandatory. Previously, we developed a diabetes-induced delayed humanized wound healing model that faithfully recapitulated the major histological features of such skin repair-deficient condition. Herein, we present the results of a transcriptomic and functional enrichment analysis followed by a mechanistic analysis performed in such humanized wound healing model. The deregulation of genes implicated in functions such as angiogenesis, apoptosis, and inflammatory signaling processes were evidenced, confirming published data in diabetic patients that in fact might also underlie some of the histological features previously reported in the delayed skin-humanized healing model. Altogether, these molecular findings support the utility of such preclinical model as a valuable tool to gain insight into the molecular basis of the delayed diabetic healing with potential impact in the translational medicine field.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33396192?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, C S</style></author><author><style face="normal" font="default" size="100%">Rigual, M M</style></author><author><style face="normal" font="default" size="100%">Brokate-Llanos, A M</style></author><author><style face="normal" font="default" size="100%">Muñoz, M J</style></author><author><style face="normal" font="default" size="100%">Garzón, A</style></author><author><style face="normal" font="default" size="100%">Pérez-Pulido, A J</style></author><author><style face="normal" font="default" size="100%">Jimenez, J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using AnABlast for intergenic sORF prediction in the Caenorhabditis elegans genome.</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Caenorhabditis elegans</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome</style></keyword><keyword><style  face="normal" font="default" size="100%">Open Reading Frames</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 12 08</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">4827-4832</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;MOTIVATION: &lt;/b&gt;Short bioactive peptides encoded by small open reading frames (sORFs) play important roles in eukaryotes. Bioinformatics prediction of ORFs is an early step in a genome sequence analysis, but sORFs encoding short peptides, often using non-AUG initiation codons, are not easily discriminated from false ORFs occurring by chance.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;AnABlast is a computational tool designed to highlight putative protein-coding regions in genomic DNA sequences. This protein-coding finder is independent of ORF length and reading frame shifts, thus making of AnABlast a potentially useful tool to predict sORFs. Using this algorithm, here, we report the identification of 82 putative new intergenic sORFs in the Caenorhabditis elegans genome. Sequence similarity, motif presence, expression data and RNA interference experiments support that the underlined sORFs likely encode functional peptides, encouraging the use of AnABlast as a new approach for the accurate prediction of intergenic sORFs in annotated eukaryotic genomes.&lt;/p&gt;&lt;p&gt;&lt;b&gt;AVAILABILITY AND IMPLEMENTATION: &lt;/b&gt;AnABlast is freely available at http://www.bioinfocabd.upo.es/ab/. The C.elegans genome browser with AnABlast results, annotated genes and all data used in this study is available at http://www.bioinfocabd.upo.es/celegans.&lt;/p&gt;&lt;p&gt;&lt;b&gt;SUPPLEMENTARY INFORMATION: &lt;/b&gt;Supplementary data are available at Bioinformatics online.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">19</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32614398?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Perez Florido, Javier</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples.</style></title><secondary-title><style face="normal" font="default" size="100%">Biol Direct</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Biol Direct</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">biomarkers</style></keyword><keyword><style  face="normal" font="default" size="100%">Cities</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug Resistance, Microbial</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolome</style></keyword><keyword><style  face="normal" font="default" size="100%">Metagenome</style></keyword><keyword><style  face="normal" font="default" size="100%">metagenomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Microbiota</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 08 20</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">15</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;The availability of hundreds of city microbiome profiles allows the development of increasingly accurate predictors of the origin of a sample based on its microbiota composition. Typical microbiome studies involve the analysis of bacterial abundance profiles.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Here we use a transformation of the conventional bacterial strain or gene abundance profiles to functional profiles that account for bacterial metabolism and other cell functionalities. These profiles are used as features for city classification in a machine learning algorithm that allows the extraction of the most relevant features for the classification.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;We demonstrate here that the use of functional profiles not only predict accurately the most likely origin of a sample but also to provide an interesting functional point of view of the biogeography of the microbiota. Interestingly, we show how cities can be classified based on the observed profile of antibiotic resistances.&lt;/p&gt;&lt;p&gt;&lt;b&gt;REVIEWERS: &lt;/b&gt;Open peer review: Reviewed by Jin Zhuang Dou, Jing Zhou, Torsten Semmler and Eran Elhaik.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31429791?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A comparison of mechanistic signaling pathway activity analysis methods.</style></title><secondary-title><style face="normal" font="default" size="100%">Brief Bioinform</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Brief Bioinform</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Postmortem Changes</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Sep 27</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">1655-1668</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29868818?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">Pujana, Miguel A</style></author><author><style face="normal" font="default" size="100%">Mateo, Francesca</style></author><author><style face="normal" font="default" size="100%">Herranz, Carmen</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.</style></title><secondary-title><style face="normal" font="default" size="100%">NPJ Syst Biol Appl</style></secondary-title><alt-title><style face="normal" font="default" size="100%">NPJ Syst Biol Appl</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug discovery</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolic Networks and Pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">7</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30854222?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chacón-Solano, E</style></author><author><style face="normal" font="default" size="100%">León, C</style></author><author><style face="normal" font="default" size="100%">Díaz, F</style></author><author><style face="normal" font="default" size="100%">García-García, F</style></author><author><style face="normal" font="default" size="100%">García, M</style></author><author><style face="normal" font="default" size="100%">Escámez, M J</style></author><author><style face="normal" font="default" size="100%">Guerrero-Aspizua, S</style></author><author><style face="normal" font="default" size="100%">Conti, C J</style></author><author><style face="normal" font="default" size="100%">Mencía, Á</style></author><author><style face="normal" font="default" size="100%">Martínez-Santamaría, L</style></author><author><style face="normal" font="default" size="100%">Llames, S</style></author><author><style face="normal" font="default" size="100%">Pévida, M</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, J</style></author><author><style face="normal" font="default" size="100%">Puig-Butillé, J A</style></author><author><style face="normal" font="default" size="100%">Maseda, R</style></author><author><style face="normal" font="default" size="100%">Puig, S</style></author><author><style face="normal" font="default" size="100%">de Lucas, R</style></author><author><style face="normal" font="default" size="100%">Baselga, E</style></author><author><style face="normal" font="default" size="100%">Larcher, F</style></author><author><style face="normal" font="default" size="100%">Dopazo, J</style></author><author><style face="normal" font="default" size="100%">Del Rio, M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fibroblast activation and abnormal extracellular matrix remodelling as common hallmarks in three cancer-prone genodermatoses.</style></title><secondary-title><style face="normal" font="default" size="100%">Br J Dermatol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Br J Dermatol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Biopsy</style></keyword><keyword><style  face="normal" font="default" size="100%">Blister</style></keyword><keyword><style  face="normal" font="default" size="100%">Case-Control Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Cells, Cultured</style></keyword><keyword><style  face="normal" font="default" size="100%">Child</style></keyword><keyword><style  face="normal" font="default" size="100%">Child, Preschool</style></keyword><keyword><style  face="normal" font="default" size="100%">Epidermolysis Bullosa</style></keyword><keyword><style  face="normal" font="default" size="100%">Epidermolysis Bullosa Dystrophica</style></keyword><keyword><style  face="normal" font="default" size="100%">Extracellular Matrix</style></keyword><keyword><style  face="normal" font="default" size="100%">Extracellular Matrix Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Fibroblasts</style></keyword><keyword><style  face="normal" font="default" size="100%">Fibrosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Healthy Volunteers</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Infant</style></keyword><keyword><style  face="normal" font="default" size="100%">Infant, Newborn</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Periodontal Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Photosensitivity Disorders</style></keyword><keyword><style  face="normal" font="default" size="100%">Primary Cell Culture</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-seq</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin</style></keyword><keyword><style  face="normal" font="default" size="100%">Xeroderma Pigmentosum</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 09</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">181</style></volume><pages><style face="normal" font="default" size="100%">512-522</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Recessive dystrophic epidermolysis bullosa (RDEB), Kindler syndrome (KS) and xeroderma pigmentosum complementation group C (XPC) are three cancer-prone genodermatoses whose causal genetic mutations cannot fully explain, on their own, the array of associated phenotypic manifestations. Recent evidence highlights the role of the stromal microenvironment in the pathology of these disorders.&lt;/p&gt;&lt;p&gt;&lt;b&gt;OBJECTIVES: &lt;/b&gt;To investigate, by means of comparative gene expression analysis, the role played by dermal fibroblasts in the pathogenesis of RDEB, KS and XPC.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We conducted RNA-Seq analysis, which included a thorough examination of the differentially expressed genes, a functional enrichment analysis and a description of affected signalling circuits. Transcriptomic data were validated at the protein level in cell cultures, serum samples and skin biopsies.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Interdisease comparisons against control fibroblasts revealed a unifying signature of 186 differentially expressed genes and four signalling pathways in the three genodermatoses. Remarkably, some of the uncovered expression changes suggest a synthetic fibroblast phenotype characterized by the aberrant expression of extracellular matrix (ECM) proteins. Western blot and immunofluorescence in situ analyses validated the RNA-Seq data. In addition, enzyme-linked immunosorbent assay revealed increased circulating levels of periostin in patients with RDEB.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Our results suggest that the different causal genetic defects converge into common changes in gene expression, possibly due to injury-sensitive events. These, in turn, trigger a cascade of reactions involving abnormal ECM deposition and underexpression of antioxidant enzymes. The elucidated expression signature provides new potential biomarkers and common therapeutic targets in RDEB, XPC and KS. What's already known about this topic? Recessive dystrophic epidermolysis bullosa (RDEB), Kindler syndrome (KS) and xeroderma pigmentosum complementation group C (XPC) are three genodermatoses with high predisposition to cancer development. Although their causal genetic mutations mainly affect epithelia, the dermal microenvironment likely contributes to the physiopathology of these disorders. What does this study add? We disclose a large overlapping transcription profile between XPC, KS and RDEB fibroblasts that points towards an activated phenotype with high matrix-synthetic capacity. This common signature seems to be independent of the primary causal deficiency, but reflects an underlying derangement of the extracellular matrix via transforming growth factor-β signalling activation and oxidative state imbalance. What is the translational message? This study broadens the current knowledge about the pathology of these diseases and highlights new targets and biomarkers for effective therapeutic intervention. It is suggested that high levels of circulating periostin could represent a potential biomarker in RDEB.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30693469?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin-Broto, Javier</style></author><author><style face="normal" font="default" size="100%">Stacchiotti, Silvia</style></author><author><style face="normal" font="default" size="100%">Lopez-Pousa, Antonio</style></author><author><style face="normal" font="default" size="100%">Redondo, Andres</style></author><author><style face="normal" font="default" size="100%">Bernabeu, Daniel</style></author><author><style face="normal" font="default" size="100%">de Alava, Enrique</style></author><author><style face="normal" font="default" size="100%">Casali, Paolo G</style></author><author><style face="normal" font="default" size="100%">Italiano, Antoine</style></author><author><style face="normal" font="default" size="100%">Gutierrez, Antonio</style></author><author><style face="normal" font="default" size="100%">Moura, David S</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Diaz-Martin, Juan</style></author><author><style face="normal" font="default" size="100%">Biscuola, Michele</style></author><author><style face="normal" font="default" size="100%">Taron, Miguel</style></author><author><style face="normal" font="default" size="100%">Collini, Paola</style></author><author><style face="normal" font="default" size="100%">Ranchere-Vince, Dominique</style></author><author><style face="normal" font="default" size="100%">Garcia Del Muro, Xavier</style></author><author><style face="normal" font="default" size="100%">Grignani, Giovanni</style></author><author><style face="normal" font="default" size="100%">Dumont, Sarah</style></author><author><style face="normal" font="default" size="100%">Martinez-Trufero, Javier</style></author><author><style face="normal" font="default" size="100%">Palmerini, Emanuela</style></author><author><style face="normal" font="default" size="100%">Hindi, Nadia</style></author><author><style face="normal" font="default" size="100%">Sebio, Ana</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Dei Tos, Angelo Paolo</style></author><author><style face="normal" font="default" size="100%">LeCesne, Axel</style></author><author><style face="normal" font="default" size="100%">Blay, Jean-Yves</style></author><author><style face="normal" font="default" size="100%">Cruz, Josefina</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pazopanib for treatment of advanced malignant and dedifferentiated solitary fibrous tumour: a multicentre, single-arm, phase 2 trial.</style></title><secondary-title><style face="normal" font="default" size="100%">Lancet Oncol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Lancet Oncol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Angiogenesis Inhibitors</style></keyword><keyword><style  face="normal" font="default" size="100%">Antineoplastic Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Indazoles</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Multivariate Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Pyrimidines</style></keyword><keyword><style  face="normal" font="default" size="100%">Response Evaluation Criteria in Solid Tumors</style></keyword><keyword><style  face="normal" font="default" size="100%">Soft Tissue Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Solitary Fibrous Tumors</style></keyword><keyword><style  face="normal" font="default" size="100%">Sulfonamides</style></keyword><keyword><style  face="normal" font="default" size="100%">Survival Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">134-144</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;A solitary fibrous tumour is a rare soft-tissue tumour with three clinicopathological variants: typical, malignant, and dedifferentiated. Preclinical experiments and retrospective studies have shown different sensitivities of solitary fibrous tumour to chemotherapy and antiangiogenics. We therefore designed a trial to assess the activity of pazopanib in a cohort of patients with malignant or dedifferentiated solitary fibrous tumour. The clinical and translational results are presented here.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;In this single-arm, phase 2 trial, adult patients (aged ≥ 18 years) with histologically confirmed metastatic or unresectable malignant or dedifferentiated solitary fibrous tumour at any location, who had progressed (by RECIST and Choi criteria) in the previous 6 months and had an ECOG performance status of 0-2, were enrolled at 16 third-level hospitals with expertise in sarcoma care in Spain, Italy, and France. Patients received pazopanib 800 mg once daily, taken orally without food, at least 1 h before or 2 h after a meal, until progression or intolerance. The primary endpoint of the study was overall response measured by Choi criteria in the subset of the intention-to-treat population (patients who received at least 1 month of treatment with at least one radiological assessment). All patients who received at least one dose of the study drug were included in the safety analyses. This study is registered with ClinicalTrials.gov, number NCT02066285, and with the European Clinical Trials Database, EudraCT number 2013-005456-15.&lt;/p&gt;&lt;p&gt;&lt;b&gt;FINDINGS: &lt;/b&gt;From June 26, 2014, to Nov 24, 2016, of 40 patients assessed, 36 were enrolled (34 with malignant solitary fibrous tumour and two with dedifferentiated solitary fibrous tumour). Median follow-up was 27 months (IQR 16-31). Based on central radiology review, 18 (51%) of 35 evaluable patients had partial responses, nine (26%) had stable disease, and eight (23%) had progressive disease according to Choi criteria. Further enrolment of patients with dedifferentiated solitary fibrous tumour was stopped after detection of early and fast progressions in a planned interim analysis. 51% (95% CI 34-69) of 35 patients achieved an overall response according to Choi criteria. Ten (29%) of 35 patients died. There were no deaths related to adverse events and the most frequent grade 3 or higher adverse events were hypertension (11 [31%] of 36 patients), neutropenia (four [11%]), increased concentrations of alanine aminotransferase (four [11%]), and increased concentrations of bilirubin (three [8%]).&lt;/p&gt;&lt;p&gt;&lt;b&gt;INTERPRETATION: &lt;/b&gt;To our knowledge, this is the first trial of pazopanib for treatment of malignant solitary fibrous tumour showing activity in this patient group. The manageable toxicity profile and the activity shown by pazopanib suggests that this drug could be an option for systemic treatment of advanced malignant solitary fibrous tumour, and provides a benchmark for future trials.&lt;/p&gt;&lt;p&gt;&lt;b&gt;FUNDING: &lt;/b&gt;Spanish Group for Research on Sarcomas (GEIS), Italian Sarcoma Group (ISG), French Sarcoma Group (FSG), GlaxoSmithKline, and Novartis.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30578023?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gómez-López, Gonzalo</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Cigudosa, Juan C</style></author><author><style face="normal" font="default" size="100%">Valencia, Alfonso</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Precision medicine needs pioneering clinical bioinformaticians.</style></title><secondary-title><style face="normal" font="default" size="100%">Brief Bioinform</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Brief Bioinform</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Precision Medicine</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 May 21</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">752-766</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Success in precision medicine depends on accessing high-quality genetic and molecular data from large, well-annotated patient cohorts that couple biological samples to comprehensive clinical data, which in conjunction can lead to effective therapies. From such a scenario emerges the need for a new professional profile, an expert bioinformatician with training in clinical areas who can make sense of multi-omics data to improve therapeutic interventions in patients, and the design of optimized basket trials. In this review, we first describe the main policies and international initiatives that focus on precision medicine. Secondly, we review the currently ongoing clinical trials in precision medicine, introducing the concept of 'precision bioinformatics', and we describe current pioneering bioinformatics efforts aimed at implementing tools and computational infrastructures for precision medicine in health institutions around the world. Thirdly, we discuss the challenges related to the clinical training of bioinformaticians, and the urgent need for computational specialists capable of assimilating medical terminologies and protocols to address real clinical questions. We also propose some skills required to carry out common tasks in clinical bioinformatics and some tips for emergent groups. Finally, we explore the future perspectives and the challenges faced by precision medicine bioinformatics.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29077790?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fourati, Slim</style></author><author><style face="normal" font="default" size="100%">Talla, Aarthi</style></author><author><style face="normal" font="default" size="100%">Mahmoudian, Mehrad</style></author><author><style face="normal" font="default" size="100%">Burkhart, Joshua G.</style></author><author><style face="normal" font="default" size="100%">Klén, Riku</style></author><author><style face="normal" font="default" size="100%">Henao, Ricardo</style></author><author><style face="normal" font="default" size="100%">Yu, Thomas</style></author><author><style face="normal" font="default" size="100%">Aydın, Zafer</style></author><author><style face="normal" font="default" size="100%">Yeung, Ka Yee</style></author><author><style face="normal" font="default" size="100%">Ahsen, Mehmet Eren</style></author><author><style face="normal" font="default" size="100%">Almugbel, Reem</style></author><author><style face="normal" font="default" size="100%">Jahandideh, Samad</style></author><author><style face="normal" font="default" size="100%">Liang, Xiao</style></author><author><style face="normal" font="default" size="100%">Nordling, Torbjörn E. M.</style></author><author><style face="normal" font="default" size="100%">Shiga, Motoki</style></author><author><style face="normal" font="default" size="100%">Stanescu, Ana</style></author><author><style face="normal" font="default" size="100%">Vogel, Robert</style></author><author><style face="normal" font="default" size="100%">Pandey, Gaurav</style></author><author><style face="normal" font="default" size="100%">Chiu, Christopher</style></author><author><style face="normal" font="default" size="100%">McClain, Micah T.</style></author><author><style face="normal" font="default" size="100%">Woods, Christopher W.</style></author><author><style face="normal" font="default" size="100%">Ginsburg, Geoffrey S.</style></author><author><style face="normal" font="default" size="100%">Elo, Laura L.</style></author><author><style face="normal" font="default" size="100%">Tsalik, Ephraim L.</style></author><author><style face="normal" font="default" size="100%">Mangravite, Lara M.</style></author><author><style face="normal" font="default" size="100%">Sieberts, Solveig K.</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">The Respiratory Viral DREAM Challenge Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection</style></title><secondary-title><style face="normal" font="default" size="100%">Nature Communications</style></secondary-title><short-title><style face="normal" font="default" size="100%">Nat Commun</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-12-2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/articles/s41467-018-06735-8http://www.nature.com/articles/s41467-018-06735-8.pdfhttp://www.nature.com/articles/s41467-018-06735-8.pdfhttp://www.nature.com/articles/s41467-018-06735-8</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ferreira, Pedro G</style></author><author><style face="normal" font="default" size="100%">Muñoz-Aguirre, Manuel</style></author><author><style face="normal" font="default" size="100%">Reverter, Ferran</style></author><author><style face="normal" font="default" size="100%">Sá Godinho, Caio P</style></author><author><style face="normal" font="default" size="100%">Sousa, Abel</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Sodaei, Reza</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Pervouchine, Dmitri</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Nurtdinov, Ramil</style></author><author><style face="normal" font="default" size="100%">Breschi, Alessandra</style></author><author><style face="normal" font="default" size="100%">Amador, Raziel</style></author><author><style face="normal" font="default" size="100%">Oliveira, Patrícia</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Curado, João</style></author><author><style face="normal" font="default" size="100%">Aguet, François</style></author><author><style face="normal" font="default" size="100%">Oliveira, Carla</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Sammeth, Michael</style></author><author><style face="normal" font="default" size="100%">Ardlie, Kristin G</style></author><author><style face="normal" font="default" size="100%">Guigó, Roderic</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The effects of death and post-mortem cold ischemia on human tissue transcriptomes.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Commun</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Commun</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Blood</style></keyword><keyword><style  face="normal" font="default" size="100%">Cold Ischemia</style></keyword><keyword><style  face="normal" font="default" size="100%">Death</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">gene expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Postmortem Changes</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Stochastic Processes</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Feb 13</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">490</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29440659?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rodríguez Hernáez, Javier</style></author><author><style face="normal" font="default" size="100%">Cerón Cucchi, Maria Esperanza</style></author><author><style face="normal" font="default" size="100%">Cravero, Silvio</style></author><author><style face="normal" font="default" size="100%">Martinez, Maria Carolina</style></author><author><style face="normal" font="default" size="100%">Gonzalez, Sergio</style></author><author><style face="normal" font="default" size="100%">Puebla, Andrea</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Farber, Marisa</style></author><author><style face="normal" font="default" size="100%">Paniego, Norma</style></author><author><style face="normal" font="default" size="100%">Rivarola, Máximo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The first complete genomic structure of Butyrivibrio fibrisolvens and its chromid.</style></title><secondary-title><style face="normal" font="default" size="100%">Microb Genom</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Microb Genom</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Butyrivibrio fibrisolvens</style></keyword><keyword><style  face="normal" font="default" size="100%">Cattle</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Milk</style></keyword><keyword><style  face="normal" font="default" size="100%">Rumen</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Oct</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">4</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Butyrivibrio fibrisolvens forms part of the gastrointestinal microbiome of ruminants and other mammals, including humans. Indeed, it is one of the most common bacteria found in the rumen and plays an important role in ruminal fermentation of polysaccharides, yet, to date, there is no closed reference genome published for this species in any ruminant animal. We successfully assembled the nearly complete genome sequence of B. fibrisolvens strain INBov1 isolated from cow rumen using Illumina paired-end reads, 454 Roche single-end and mate pair sequencing technology. Additionally, we constructed an optical restriction map of this strain to aid in scaffold ordering and positioning, and completed the first genomic structure of this species. Moreover, we identified and assembled the first chromid of this species (pINBov266). The INBov1 genome encodes a large set of genes involved in the cellulolytic process but lacks key genes. This seems to indicate that B. fibrisolvens plays an important role in ruminal cellulolytic processes, but does not have autonomous cellulolytic capacity. When searching for genes involved in the biohydrogenation of unsaturated fatty acids, no linoleate isomerase gene was found in this strain. INBov1 does encode oleate hydratase genes known to participate in the hydrogenation of oleic acids. Furthermore, INBov1 contains an enolase gene, which has been recently determined to participate in the synthesis of conjugated linoleic acids. This work confirms the presence of a novel chromid in B. fibrisolvens and provides a new potential reference genome sequence for this species, providing new insight into its role in biohydrogenation and carbohydrate degradation.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30216146?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Pujana, Miguel A</style></author><author><style face="normal" font="default" size="100%">Mateo, Francesca</style></author><author><style face="normal" font="default" size="100%">Herranz, Carmen</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.</style></title><secondary-title><style face="normal" font="default" size="100%">Cancer Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cancer Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Cluster Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease Progression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Kaplan-Meier Estimate</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolome</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Oncogenes</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Prognosis</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Small Interfering</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">Treatment Outcome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Nov 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">6059-6072</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies. Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. .&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">21</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30135189?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wu, Guohong Albert</style></author><author><style face="normal" font="default" size="100%">Terol, Javier</style></author><author><style face="normal" font="default" size="100%">Ibañez, Victoria</style></author><author><style face="normal" font="default" size="100%">López-García, Antonio</style></author><author><style face="normal" font="default" size="100%">Pérez-Román, Estela</style></author><author><style face="normal" font="default" size="100%">Borredá, Carles</style></author><author><style face="normal" font="default" size="100%">Domingo, Concha</style></author><author><style face="normal" font="default" size="100%">Tadeo, Francisco R</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Alonso, Roberto</style></author><author><style face="normal" font="default" size="100%">Curk, Franck</style></author><author><style face="normal" font="default" size="100%">Du, Dongliang</style></author><author><style face="normal" font="default" size="100%">Ollitrault, Patrick</style></author><author><style face="normal" font="default" size="100%">Roose, Mikeal L</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Gmitter, Frederick G</style></author><author><style face="normal" font="default" size="100%">Rokhsar, Daniel S</style></author><author><style face="normal" font="default" size="100%">Talon, Manuel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genomics of the origin and evolution of Citrus.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nature</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Asia, Southeastern</style></keyword><keyword><style  face="normal" font="default" size="100%">Biodiversity</style></keyword><keyword><style  face="normal" font="default" size="100%">citrus</style></keyword><keyword><style  face="normal" font="default" size="100%">Crop Production</style></keyword><keyword><style  face="normal" font="default" size="100%">Evolution, Molecular</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Speciation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Haplotypes</style></keyword><keyword><style  face="normal" font="default" size="100%">Heterozygote</style></keyword><keyword><style  face="normal" font="default" size="100%">History, Ancient</style></keyword><keyword><style  face="normal" font="default" size="100%">Human Migration</style></keyword><keyword><style  face="normal" font="default" size="100%">Hybridization, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Feb 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">554</style></volume><pages><style face="normal" font="default" size="100%">311-316</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The genus Citrus, comprising some of the most widely cultivated fruit crops worldwide, includes an uncertain number of species. Here we describe ten natural citrus species, using genomic, phylogenetic and biogeographic analyses of 60 accessions representing diverse citrus germ plasms, and propose that citrus diversified during the late Miocene epoch through a rapid southeast Asian radiation that correlates with a marked weakening of the monsoons. A second radiation enabled by migration across the Wallace line gave rise to the Australian limes in the early Pliocene epoch. Further identification and analyses of hybrids and admixed genomes provides insights into the genealogy of major commercial cultivars of citrus. Among mandarins and sweet orange, we find an extensive network of relatedness that illuminates the domestication of these groups. Widespread pummelo admixture among these mandarins and its correlation with fruit size and acidity suggests a plausible role of pummelo introgression in the selection of palatable mandarins. This work provides a new evolutionary framework for the genus Citrus.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7692</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29414943?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cobo-Vuilleumier, Nadia</style></author><author><style face="normal" font="default" size="100%">Lorenzo, Petra I</style></author><author><style face="normal" font="default" size="100%">Rodríguez, Noelia García</style></author><author><style face="normal" font="default" size="100%">Herrera Gómez, Irene de Gracia</style></author><author><style face="normal" font="default" size="100%">Fuente-Martin, Esther</style></author><author><style face="normal" font="default" size="100%">López-Noriega, Livia</style></author><author><style face="normal" font="default" size="100%">Mellado-Gil, José Manuel</style></author><author><style face="normal" font="default" size="100%">Romero-Zerbo, Silvana-Yanina</style></author><author><style face="normal" font="default" size="100%">Baquié, Mathurin</style></author><author><style face="normal" font="default" size="100%">Lachaud, Christian Claude</style></author><author><style face="normal" font="default" size="100%">Stifter, Katja</style></author><author><style face="normal" font="default" size="100%">Perdomo, German</style></author><author><style face="normal" font="default" size="100%">Bugliani, Marco</style></author><author><style face="normal" font="default" size="100%">De Tata, Vincenzo</style></author><author><style face="normal" font="default" size="100%">Bosco, Domenico</style></author><author><style face="normal" font="default" size="100%">Parnaud, Geraldine</style></author><author><style face="normal" font="default" size="100%">Pozo, David</style></author><author><style face="normal" font="default" size="100%">Hmadcha, Abdelkrim</style></author><author><style face="normal" font="default" size="100%">Florido, Javier P</style></author><author><style face="normal" font="default" size="100%">Toscano, Miguel G</style></author><author><style face="normal" font="default" size="100%">de Haan, Peter</style></author><author><style face="normal" font="default" size="100%">Schoonjans, Kristina</style></author><author><style face="normal" font="default" size="100%">Sánchez Palazón, Luis</style></author><author><style face="normal" font="default" size="100%">Marchetti, Piero</style></author><author><style face="normal" font="default" size="100%">Schirmbeck, Reinhold</style></author><author><style face="normal" font="default" size="100%">Martín-Montalvo, Alejandro</style></author><author><style face="normal" font="default" size="100%">Meda, Paolo</style></author><author><style face="normal" font="default" size="100%">Soria, Bernat</style></author><author><style face="normal" font="default" size="100%">Bermúdez-Silva, Francisco-Javier</style></author><author><style face="normal" font="default" size="100%">St-Onge, Luc</style></author><author><style face="normal" font="default" size="100%">Gauthier, Benoit R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">LRH-1 agonism favours an immune-islet dialogue which protects against diabetes mellitus.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Commun</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Commun</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Apoptosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Communication</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Survival</style></keyword><keyword><style  face="normal" font="default" size="100%">Diabetes Mellitus, Experimental</style></keyword><keyword><style  face="normal" font="default" size="100%">Diabetes Mellitus, Type 2</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Hypoglycemic Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunity, Innate</style></keyword><keyword><style  face="normal" font="default" size="100%">insulin</style></keyword><keyword><style  face="normal" font="default" size="100%">Insulin-Secreting Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Islets of Langerhans</style></keyword><keyword><style  face="normal" font="default" size="100%">Islets of Langerhans Transplantation</style></keyword><keyword><style  face="normal" font="default" size="100%">Macrophages</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice, Inbred C57BL</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenalenes</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptors, Cytoplasmic and Nuclear</style></keyword><keyword><style  face="normal" font="default" size="100%">Streptozocin</style></keyword><keyword><style  face="normal" font="default" size="100%">T-Lymphocytes, Regulatory</style></keyword><keyword><style  face="normal" font="default" size="100%">Transplantation, Heterologous</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Apr 16</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">1488</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Type 1 diabetes mellitus (T1DM) is due to the selective destruction of islet beta cells by immune cells. Current therapies focused on repressing the immune attack or stimulating beta cell regeneration still have limited clinical efficacy. Therefore, it is timely to identify innovative targets to dampen the immune process, while promoting beta cell survival and function. Liver receptor homologue-1 (LRH-1) is a nuclear receptor that represses inflammation in digestive organs, and protects pancreatic islets against apoptosis. Here, we show that BL001, a small LRH-1 agonist, impedes hyperglycemia progression and the immune-dependent inflammation of pancreas in murine models of T1DM, and beta cell apoptosis in islets of type 2 diabetic patients, while increasing beta cell mass and insulin secretion. Thus, we suggest that LRH-1 agonism favors a dialogue between immune and islet cells, which could be druggable to protect against diabetes mellitus.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29662071?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome.</style></title><secondary-title><style face="normal" font="default" size="100%">Biol Direct</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Biol Direct</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">JNK Mitogen-Activated Protein Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Theoretical</style></keyword><keyword><style  face="normal" font="default" size="100%">Neuroblastoma</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Aug 22</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.&lt;/p&gt;&lt;p&gt;&lt;b&gt;REVIEWERS: &lt;/b&gt;This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30134948?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ibáñez, Mariam</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Such, Esperanza</style></author><author><style face="normal" font="default" size="100%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Liquori, Alessandro</style></author><author><style face="normal" font="default" size="100%">López-Pavía, María</style></author><author><style face="normal" font="default" size="100%">LLop, Marta</style></author><author><style face="normal" font="default" size="100%">Alonso, Carmen</style></author><author><style face="normal" font="default" size="100%">Barragán, Eva</style></author><author><style face="normal" font="default" size="100%">Gómez-Seguí, Inés</style></author><author><style face="normal" font="default" size="100%">Neef, Alexander</style></author><author><style face="normal" font="default" size="100%">Hervás, David</style></author><author><style face="normal" font="default" size="100%">Montesinos, Pau</style></author><author><style face="normal" font="default" size="100%">Sanz, Guillermo</style></author><author><style face="normal" font="default" size="100%">Sanz, Miguel Angel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Cervera, José</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The modular network structure of the mutational landscape of Acute Myeloid Leukemia.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Cytodiagnosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Association Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Heterogeneity</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Karyotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Leukemia, Myeloid, Acute</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasm Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Nucleophosmin</style></keyword><keyword><style  face="normal" font="default" size="100%">Prognosis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">e0202926</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Acute myeloid leukemia (AML) is associated with the sequential accumulation of acquired genetic alterations. Although at diagnosis cytogenetic alterations are frequent in AML, roughly 50% of patients present an apparently normal karyotype (NK), leading to a highly heterogeneous prognosis. Due to this significant heterogeneity, it has been suggested that different molecular mechanisms may trigger the disease with diverse prognostic implications. We performed whole-exome sequencing (WES) of tumor-normal matched samples of de novo AML-NK patients lacking mutations in NPM1, CEBPA or FLT3-ITD to identify new gene mutations with potential prognostic and therapeutic relevance to patients with AML. Novel candidate-genes, together with others previously described, were targeted resequenced in an independent cohort of 100 de novo AML patients classified in the cytogenetic intermediate-risk (IR) category. A mean of 4.89 mutations per sample were detected in 73 genes, 35 of which were mutated in more than one patient. After a network enrichment analysis, we defined a single in silico model and established a set of seed-genes that may trigger leukemogenesis in patients with normal karyotype. The high heterogeneity of gene mutations observed in AML patients suggested that a specific alteration could not be as essential as the interaction of deregulated pathways.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30303964?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gonzalez, Sergio</style></author><author><style face="normal" font="default" size="100%">Clavijo, Bernardo</style></author><author><style face="normal" font="default" size="100%">Rivarola, Máximo</style></author><author><style face="normal" font="default" size="100%">Moreno, Patricio</style></author><author><style face="normal" font="default" size="100%">Fernandez, Paula</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Paniego, Norma</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Feb 22</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">121</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;In the last years, applications based on massively parallelized RNA sequencing (RNA-seq) have become valuable approaches for studying non-model species, e.g., without a fully sequenced genome. RNA-seq is a useful tool for detecting novel transcripts and genetic variations and for evaluating differential gene expression by digital measurements. The large and complex datasets resulting from functional genomic experiments represent a challenge in data processing, management, and analysis. This problem is especially significant for small research groups working with non-model species.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We developed a web-based application, called ATGC transcriptomics, with a flexible and adaptable interface that allows users to work with new generation sequencing (NGS) transcriptomic analysis results using an ontology-driven database. This new application simplifies data exploration, visualization, and integration for a better comprehension of the results.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;ATGC transcriptomics provides access to non-expert computer users and small research groups to a scalable storage option and simple data integration, including database administration and management. The software is freely available under the terms of GNU public license at http://atgcinta.sourceforge.net .&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28222698?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lopez, Javier</style></author><author><style face="normal" font="default" size="100%">Coll, Jacobo</style></author><author><style face="normal" font="default" size="100%">Haimel, Matthias</style></author><author><style face="normal" font="default" size="100%">Kandasamy, Swaathi</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Furio-Tari, Pedro</style></author><author><style face="normal" font="default" size="100%">Bari, Wasim</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Rueda, Antonio</style></author><author><style face="normal" font="default" size="100%">Gräf, Stefan</style></author><author><style face="normal" font="default" size="100%">Rendon, Augusto</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">HGVA: the Human Genome Variation Archive.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Jul 03</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx445</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">45</style></volume><pages><style face="normal" font="default" size="100%">W189-W194</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;High-profile genomic variation projects like the 1000 Genomes project or the Exome Aggregation Consortium, are generating a wealth of human genomic variation knowledge which can be used as an essential reference for identifying disease-causing genotypes. However, accessing these data, contrasting the various studies and integrating those data in downstream analyses remains cumbersome. The Human Genome Variation Archive (HGVA) tackles these challenges and facilitates access to genomic data for key reference projects in a clean, fast and integrated fashion. HGVA provides an efficient and intuitive web-interface for easy data mining, a comprehensive RESTful API and client libraries in Python, Java and JavaScript for fast programmatic access to its knowledge base. HGVA calculates population frequencies for these projects and enriches their data with variant annotation provided by CellBase, a rich and fast annotation solution. HGVA serves as a proof-of-concept of the genome analysis developments being carried out by the University of Cambridge together with UK's 100 000 genomes project and the National Institute for Health Research BioResource Rare-Diseases, in particular, deploying open-source for Computational Biology (OpenCB) software platform for storing and analyzing massive genomic datasets.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">W1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28535294?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.</style></title><secondary-title><style face="normal" font="default" size="100%">Oncotarget</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Oncotarget</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">gene expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Precision Medicine</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Jan 17</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">5160-5178</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28042959?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Alonso, Roberto</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Conesa, David</style></author><author><style face="normal" font="default" size="100%">López-Quílez, Antonio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reference genome assessment from a population scale perspective: an accurate profile of variability and noise.</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Statistical</style></keyword><keyword><style  face="normal" font="default" size="100%">Quality Control</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Nov 15</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx482</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">3511-3517</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;MOTIVATION: &lt;/b&gt;Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;The reliability of our protocol has been extensively tested through different experiments and organisms with accurate results, improving state-of-the-art methods. Our analysis demonstrates synergistic relations between quality control scores and allelic variability estimators, that improve the detection of misassembled regions, and is able to find strong artifact signals even within the human reference assembly. Furthermore, we demonstrated how our model can be trained to properly rank the confidence of a set of candidate variants obtained from new independent samples.&lt;/p&gt;&lt;p&gt;&lt;b&gt;AVAILABILITY AND IMPLEMENTATION: &lt;/b&gt;This tool is freely available at http://gitlab.com/carbonell/ces.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONTACT: &lt;/b&gt;jcarbonell.cipf@gmail.com or joaquin.dopazo@juntadeandalucia.es.&lt;/p&gt;&lt;p&gt;&lt;b&gt;SUPPLEMENTARY INFORMATION: &lt;/b&gt;Supplementary data are available at Bioinformatics online.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">22</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28961772?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Juanes, José M</style></author><author><style face="normal" font="default" size="100%">Gallego, Asunción</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Chaves, Felipe J</style></author><author><style face="normal" font="default" size="100%">Marin-Garcia, Pablo</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Arnau, Vicente</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">VISMapper: ultra-fast exhaustive cartography of viral insertion sites for gene therapy.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Base Sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Therapy</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Vectors</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword><keyword><style  face="normal" font="default" size="100%">Virus Integration</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Sep 20</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">421</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;The possibility of integrating viral vectors to become a persistent part of the host genome makes them a crucial element of clinical gene therapy. However, viral integration has associated risks, such as the unintentional activation of oncogenes that can result in cancer. Therefore, the analysis of integration sites of retroviral vectors is a crucial step in developing safer vectors for therapeutic use.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Here we present VISMapper, a vector integration site analysis web server, to analyze next-generation sequencing data for retroviral vector integration sites. VISMapper can be found at: http://vismapper.babelomics.org .&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Because it uses novel mapping algorithms VISMapper is remarkably faster than previous available programs. It also provides a useful graphical interface to analyze the integration sites found in the genomic context.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28931371?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gui, Hongsheng</style></author><author><style face="normal" font="default" size="100%">Schriemer, Duco</style></author><author><style face="normal" font="default" size="100%">Cheng, William W</style></author><author><style face="normal" font="default" size="100%">Chauhan, Rajendra K</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author><author><style face="normal" font="default" size="100%">Berrios, Courtney</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Brooks, Alice S</style></author><author><style face="normal" font="default" size="100%">Brouwer, Rutger W W</style></author><author><style face="normal" font="default" size="100%">Burns, Alan J</style></author><author><style face="normal" font="default" size="100%">Cherny, Stacey S</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Eggen, Bart J L</style></author><author><style face="normal" font="default" size="100%">Griseri, Paola</style></author><author><style face="normal" font="default" size="100%">Jalloh, Binta</style></author><author><style face="normal" font="default" size="100%">Le, Thuy-Linh</style></author><author><style face="normal" font="default" size="100%">Lui, Vincent C H</style></author><author><style face="normal" font="default" size="100%">Luzón-Toro, Berta</style></author><author><style face="normal" font="default" size="100%">Matera, Ivana</style></author><author><style face="normal" font="default" size="100%">Ngan, Elly S W</style></author><author><style face="normal" font="default" size="100%">Pelet, Anna</style></author><author><style face="normal" font="default" size="100%">Ruiz-Ferrer, Macarena</style></author><author><style face="normal" font="default" size="100%">Sham, Pak C</style></author><author><style face="normal" font="default" size="100%">Shepherd, Iain T</style></author><author><style face="normal" font="default" size="100%">So, Man-Ting</style></author><author><style face="normal" font="default" size="100%">Sribudiani, Yunia</style></author><author><style face="normal" font="default" size="100%">Tang, Clara S M</style></author><author><style face="normal" font="default" size="100%">van den Hout, Mirjam C G N</style></author><author><style face="normal" font="default" size="100%">van der Linde, Herma C</style></author><author><style face="normal" font="default" size="100%">van Ham, Tjakko J</style></author><author><style face="normal" font="default" size="100%">van IJcken, Wilfred F J</style></author><author><style face="normal" font="default" size="100%">Verheij, Joke B G M</style></author><author><style face="normal" font="default" size="100%">Amiel, Jeanne</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author><author><style face="normal" font="default" size="100%">Ceccherini, Isabella</style></author><author><style face="normal" font="default" size="100%">Chakravarti, Aravinda</style></author><author><style face="normal" font="default" size="100%">Lyonnet, Stanislas</style></author><author><style face="normal" font="default" size="100%">Tam, Paul K H</style></author><author><style face="normal" font="default" size="100%">Garcia-Barceló, Maria-Mercè</style></author><author><style face="normal" font="default" size="100%">Hofstra, Robert Mw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Whole exome sequencing coupled with unbiased functional analysis reveals new Hirschsprung disease genes.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome biology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Hirschprung</style></keyword><keyword><style  face="normal" font="default" size="100%">Rare Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">WES</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Mar 08</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1174-6</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">48</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BACKGROUND: Hirschsprung disease (HSCR), which is congenital obstruction of the bowel, results from a failure of enteric nervous system (ENS) progenitors to migrate, proliferate, differentiate, or survive within the distal intestine. Previous studies that have searched for genes underlying HSCR have focused on ENS-related pathways and genes not fitting the current knowledge have thus often been ignored. We identify and validate novel HSCR genes using whole exome sequencing (WES), burden tests, in silico prediction, unbiased in vivo analyses of the mutated genes in zebrafish, and expression analyses in zebrafish, mouse, and human. RESULTS: We performed de novo mutation (DNM) screening on 24 HSCR trios. We identify 28 DNMs in 21 different genes. Eight of the DNMs we identified occur in RET, the main HSCR gene, and the remaining 20 DNMs reside in genes not reported in the ENS. Knockdown of all 12 genes with missense or loss-of-function DNMs showed that the orthologs of four genes (DENND3, NCLN, NUP98, and TBATA) are indispensable for ENS development in zebrafish, and these results were confirmed by CRISPR knockout. These genes are also expressed in human and mouse gut and/or ENS progenitors. Importantly, the encoded proteins are linked to neuronal processes shared by the central nervous system and the ENS. CONCLUSIONS: Our data open new fields of investigation into HSCR pathology and provide novel insights into the development of the ENS. Moreover, the study demonstrates that functional analyses of genes carrying DNMs are warranted to delineate the full genetic architecture of rare complex diseases.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gui, Hongsheng</style></author><author><style face="normal" font="default" size="100%">Schriemer, Duco</style></author><author><style face="normal" font="default" size="100%">Cheng, William W.</style></author><author><style face="normal" font="default" size="100%">Chauhan, Rajendra K.</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author><author><style face="normal" font="default" size="100%">Berrios, Courtney</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Brooks, Alice S.</style></author><author><style face="normal" font="default" size="100%">Brouwer, Rutger W. W.</style></author><author><style face="normal" font="default" size="100%">Burns, Alan J.</style></author><author><style face="normal" font="default" size="100%">Cherny, Stacey S.</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Eggen, Bart J. L.</style></author><author><style face="normal" font="default" size="100%">Griseri, Paola</style></author><author><style face="normal" font="default" size="100%">Jalloh, Binta</style></author><author><style face="normal" font="default" size="100%">Le, Thuy-Linh</style></author><author><style face="normal" font="default" size="100%">Lui, Vincent C. H.</style></author><author><style face="normal" font="default" size="100%">Luzón-Toro, Berta</style></author><author><style face="normal" font="default" size="100%">Matera, Ivana</style></author><author><style face="normal" font="default" size="100%">Ngan, Elly S. W.</style></author><author><style face="normal" font="default" size="100%">Pelet, Anna</style></author><author><style face="normal" font="default" size="100%">Ruiz-Ferrer, Macarena</style></author><author><style face="normal" font="default" size="100%">Sham, Pak C.</style></author><author><style face="normal" font="default" size="100%">Shepherd, Iain T.</style></author><author><style face="normal" font="default" size="100%">So, Man-Ting</style></author><author><style face="normal" font="default" size="100%">Sribudiani, Yunia</style></author><author><style face="normal" font="default" size="100%">Tang, Clara S. M.</style></author><author><style face="normal" font="default" size="100%">van den Hout, Mirjam C. G. N.</style></author><author><style face="normal" font="default" size="100%">van der Linde, Herma C.</style></author><author><style face="normal" font="default" size="100%">van Ham, Tjakko J.</style></author><author><style face="normal" font="default" size="100%">van IJcken, Wilfred F. J.</style></author><author><style face="normal" font="default" size="100%">Verheij, Joke B. G. M.</style></author><author><style face="normal" font="default" size="100%">Amiel, Jeanne</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author><author><style face="normal" font="default" size="100%">Ceccherini, Isabella</style></author><author><style face="normal" font="default" size="100%">Chakravarti, Aravinda</style></author><author><style face="normal" font="default" size="100%">Lyonnet, Stanislas</style></author><author><style face="normal" font="default" size="100%">Tam, Paul K. H.</style></author><author><style face="normal" font="default" size="100%">Garcia-Barceló, Maria-Mercè</style></author><author><style face="normal" font="default" size="100%">Hofstra, Robert M. W.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Whole exome sequencing coupled with unbiased functional analysis reveals new Hirschsprung disease genes</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Biology</style></secondary-title><short-title><style face="normal" font="default" size="100%">Genome Biol</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-12-2017</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1174-6http://link.springer.com/content/pdf/10.1186/s13059-017-1174-6.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">18</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">Hidago, Marta R</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Crespo, Daniel</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Actionable pathways: interactive discovery of therapeutic targets using signaling pathway models.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic acids research</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">actionable genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease mechanism</style></keyword><keyword><style  face="normal" font="default" size="100%">drug action mechanism</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug discovery</style></keyword><keyword><style  face="normal" font="default" size="100%">pathway analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">personalized medicine</style></keyword><keyword><style  face="normal" font="default" size="100%">signalling</style></keyword><keyword><style  face="normal" font="default" size="100%">therapeutic targets</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 May 2</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/content/early/2016/05/02/nar.gkw369.full</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The discovery of actionable targets is crucial for targeted therapies and is also a constituent part of the drug discovery process. The success of an intervention over a target depends critically on its contribution, within the complex network of gene interactions, to the cellular processes responsible for disease progression or therapeutic response. Here we present PathAct, a web server that predicts the effect that interventions over genes (inhibitions or activations that simulate knock-outs, drug treatments or over-expressions) can have over signal transmission within signaling pathways and, ultimately, over the cell functionalities triggered by them. PathAct implements an advanced graphical interface that provides a unique interactive working environment in which the suitability of potentially actionable genes, that could eventually become drug targets for personalized or individualized therapies, can be easily tested. The PathAct tool can be found at: http://pathact.babelomics.org.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lupo, Vincenzo</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Sancho, Paula</style></author><author><style face="normal" font="default" size="100%">Tello, Cristina</style></author><author><style face="normal" font="default" size="100%">García-Romero, Mar</style></author><author><style face="normal" font="default" size="100%">Villarreal, Liliana</style></author><author><style face="normal" font="default" size="100%">Alberti, Antonia</style></author><author><style face="normal" font="default" size="100%">Sivera, Rafael</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Pascual-Pascual, Samuel I</style></author><author><style face="normal" font="default" size="100%">Márquez-Infante, Celedonio</style></author><author><style face="normal" font="default" size="100%">Casasnovas, Carlos</style></author><author><style face="normal" font="default" size="100%">Sevilla, Teresa</style></author><author><style face="normal" font="default" size="100%">Espinós, Carmen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessment of Targeted Next-Generation Sequencing as a Tool for the Diagnosis of Charcot-Marie-Tooth Disease and Hereditary Motor Neuropathy.</style></title><secondary-title><style face="normal" font="default" size="100%">The Journal of molecular diagnostics : JMD</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Charcot-Marie-Tooth</style></keyword><keyword><style  face="normal" font="default" size="100%">CMT</style></keyword><keyword><style  face="normal" font="default" size="100%">Diagnostic</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">Panels</style></keyword><keyword><style  face="normal" font="default" size="100%">rare diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Targeted resequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Jan 2</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S1525157815002615</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Charcot-Marie-Tooth disease is characterized by broad genetic heterogeneity with &gt;50 known disease-associated genes. Mutations in some of these genes can cause a pure motor form of hereditary motor neuropathy, the genetics of which are poorly characterized. We designed a panel comprising 56 genes associated with Charcot-Marie-Tooth disease/hereditary motor neuropathy. We validated this diagnostic tool by first testing 11 patients with pathological mutations. A cohort of 33 affected subjects was selected for this study. The DNAJB2 c.352+1G&gt;A mutation was detected in two cases; novel changes and/or variants with low frequency (&lt;1%) were found in 12 cases. There were no candidate variants in 18 cases, and amplification failed for one sample. The DNAJB2 c.352+1G&gt;A mutation was also detected in three additional families. On haplotype analysis, all of the patients from these five families shared the same haplotype; therefore, the DNAJB2 c.352+1G&gt;A mutation may be a founder event. Our gene panel allowed us to perform a very rapid and cost-effective screening of genes involved in Charcot-Marie-Tooth disease/hereditary motor neuropathy. Our diagnostic strategy was robust in terms of both coverage and read depth for all of the genes and patient samples. These findings demonstrate the difficulty in achieving a definitive molecular diagnosis because of the complexity of interpreting new variants and the genetic heterogeneity that is associated with these neuropathies.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanghez, Valentina</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Sebastián-Leon, Patricia</style></author><author><style face="normal" font="default" size="100%">Carobbio, Stefania</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Vidal-Puig, Antonio</style></author><author><style face="normal" font="default" size="100%">Bartolomucci, Alessandro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Chronic subordination stress selectively downregulates the insulin signaling pathway in liver and skeletal muscle but not in adipose tissue of male mice.</style></title><secondary-title><style face="normal" font="default" size="100%">Stress (Amsterdam, Netherlands)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adipose tissue</style></keyword><keyword><style  face="normal" font="default" size="100%">insulin</style></keyword><keyword><style  face="normal" font="default" size="100%">IRS1</style></keyword><keyword><style  face="normal" font="default" size="100%">IRS2</style></keyword><keyword><style  face="normal" font="default" size="100%">metabolic syndrome</style></keyword><keyword><style  face="normal" font="default" size="100%">obesity</style></keyword><keyword><style  face="normal" font="default" size="100%">pathway analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Mar 7</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.tandfonline.com/doi/abs/10.3109/10253890.2016.1151491?journalCode=ists20</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">1-11</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Chronic stress has been associated with obesity, glucose intolerance, and insulin resistance. We developed a model of chronic psychosocial stress (CPS) in which subordinate mice are vulnerable to obesity and the metabolic-like syndrome while dominant mice exhibit a healthy metabolic phenotype. Here we tested the hypothesis that the metabolic difference between subordinate and dominant mice is associated with changes in functional pathways relevant for insulin sensitivity, glucose and lipid homeostasis. Male mice were exposed to CPS for four weeks and fed either a standard diet or a high-fat diet (HFD). We first measured, by real-time PCR candidate genes, in the liver, skeletal muscle, and the perigonadal white adipose tissue (pWAT). Subsequently, we used a probabilistic analysis approach to analyze different ways in which signals can be transmitted across the pathways in each tissue. Results showed that subordinate mice displayed a drastic downregulation of the insulin pathway in liver and muscle, indicative of insulin resistance, already on standard diet. Conversely, pWAT showed molecular changes suggestive of facilitated fat deposition in an otherwise insulin-sensitive tissue. The molecular changes in subordinate mice fed a standard diet were greater compared to HFD-fed controls. Finally, dominant mice maintained a substantially normal metabolic and molecular phenotype even when fed a HFD. Overall, our data demonstrate that subordination stress is a potent stimulus for the downregulation of the insulin signaling pathway in liver and muscle and a major risk factor for the development of obesity, insulin resistance, and type 2 diabetes mellitus.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lagarde, Julien</style></author><author><style face="normal" font="default" size="100%">Uszczynska-Ratajczak, Barbara</style></author><author><style face="normal" font="default" size="100%">Santoyo-López, Javier</style></author><author><style face="normal" font="default" size="100%">Gonzalez, Jose Manuel</style></author><author><style face="normal" font="default" size="100%">Tapanari, Electra</style></author><author><style face="normal" font="default" size="100%">Mudge, Jonathan M.</style></author><author><style face="normal" font="default" size="100%">Steward, Charles A.</style></author><author><style face="normal" font="default" size="100%">Wilming, Laurens</style></author><author><style face="normal" font="default" size="100%">Tanzer, Andrea</style></author><author><style face="normal" font="default" size="100%">Howald, Cédric</style></author><author><style face="normal" font="default" size="100%">Chrast, Jacqueline</style></author><author><style face="normal" font="default" size="100%">Vela-Boza, Alicia</style></author><author><style face="normal" font="default" size="100%">Rueda, Antonio</style></author><author><style face="normal" font="default" size="100%">Lopez-Domingo, Francisco J.</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Reymond, Alexandre</style></author><author><style face="normal" font="default" size="100%">Guigó, Roderic</style></author><author><style face="normal" font="default" size="100%">Harrow, Jennifer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extension of human lncRNA transcripts by RACE coupled with long-read high-throughput sequencing (RACE-Seq)</style></title><secondary-title><style face="normal" font="default" size="100%">Nature Communications</style></secondary-title><short-title><style face="normal" font="default" size="100%">Nat Commun</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-11-2016</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/articles/ncomms12339http://www.nature.com/articles/ncomms12339.pdfhttp://www.nature.com/articles/ncomms12339.pdfhttp://www.nature.com/articles/ncomms12339</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">7</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lagarde, Julien</style></author><author><style face="normal" font="default" size="100%">Uszczynska-Ratajczak, Barbara</style></author><author><style face="normal" font="default" size="100%">Santoyo-López, Javier</style></author><author><style face="normal" font="default" size="100%">Gonzalez, Jose Manuel</style></author><author><style face="normal" font="default" size="100%">Tapanari, Electra</style></author><author><style face="normal" font="default" size="100%">Mudge, Jonathan M</style></author><author><style face="normal" font="default" size="100%">Steward, Charles A</style></author><author><style face="normal" font="default" size="100%">Wilming, Laurens</style></author><author><style face="normal" font="default" size="100%">Tanzer, Andrea</style></author><author><style face="normal" font="default" size="100%">Howald, Cédric</style></author><author><style face="normal" font="default" size="100%">Chrast, Jacqueline</style></author><author><style face="normal" font="default" size="100%">Vela-Boza, Alicia</style></author><author><style face="normal" font="default" size="100%">Antonio Rueda</style></author><author><style face="normal" font="default" size="100%">López-Domingo, Francisco J</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Reymond, Alexandre</style></author><author><style face="normal" font="default" size="100%">Guigó, Roderic</style></author><author><style face="normal" font="default" size="100%">Harrow, Jennifer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extension of human lncRNA transcripts by RACE coupled with long-read high-throughput sequencing (RACE-Seq).</style></title><secondary-title><style face="normal" font="default" size="100%">Nature communications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/articles/ncomms12339</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">12339</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Long non-coding RNAs (lncRNAs) constitute a large, yet mostly uncharacterized fraction of the mammalian transcriptome. Such characterization requires a comprehensive, high-quality annotation of their gene structure and boundaries, which is currently lacking. Here we describe RACE-Seq, an experimental workflow designed to address this based on RACE (rapid amplification of cDNA ends) and long-read RNA sequencing. We apply RACE-Seq to 398 human lncRNA genes in seven tissues, leading to the discovery of 2,556 on-target, novel transcripts. About 60% of the targeted loci are extended in either 5’ or 3’, often reaching genomic hallmarks of gene boundaries. Analysis of the novel transcripts suggests that lncRNAs are as long, have as many exons and undergo as much alternative splicing as protein-coding genes, contrary to current assumptions. Overall, we show that RACE-Seq is an effective tool to annotate an organism’s deep transcriptome, and compares favourably to other targeted sequencing techniques.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Medina, I</style></author><author><style face="normal" font="default" size="100%">Tárraga, J</style></author><author><style face="normal" font="default" size="100%">Martínez, H</style></author><author><style face="normal" font="default" size="100%">Barrachina, S</style></author><author><style face="normal" font="default" size="100%">Castillo, M I</style></author><author><style face="normal" font="default" size="100%">Paschall, J</style></author><author><style face="normal" font="default" size="100%">Salavert-Torres, J</style></author><author><style face="normal" font="default" size="100%">Blanquer-Espert, I</style></author><author><style face="normal" font="default" size="100%">Hernández-García, V</style></author><author><style face="normal" font="default" size="100%">Quintana-Ortí, E S</style></author><author><style face="normal" font="default" size="100%">Dopazo, J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Highly sensitive and ultrafast read mapping for RNA-seq analysis.</style></title><secondary-title><style face="normal" font="default" size="100%">DNA Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">DNA Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity and Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">93-100</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;As sequencing technologies progress, the amount of data produced grows exponentially, shifting the bottleneck of discovery towards the data analysis phase. In particular, currently available mapping solutions for RNA-seq leave room for improvement in terms of sensitivity and performance, hindering an efficient analysis of transcriptomes by massive sequencing. Here, we present an innovative approach that combines re-engineering, optimization and parallelization. This solution results in a significant increase of mapping sensitivity over a wide range of read lengths and substantial shorter runtimes when compared with current RNA-seq mapping methods available.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26740642?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Corton, M</style></author><author><style face="normal" font="default" size="100%">Avila-Fernández, A</style></author><author><style face="normal" font="default" size="100%">Campello, L</style></author><author><style face="normal" font="default" size="100%">Sánchez, M</style></author><author><style face="normal" font="default" size="100%">Benavides, B</style></author><author><style face="normal" font="default" size="100%">López-Molina, M I</style></author><author><style face="normal" font="default" size="100%">Fernández-Sánchez, L</style></author><author><style face="normal" font="default" size="100%">Sánchez-Alcudia, R</style></author><author><style face="normal" font="default" size="100%">da Silva, L R J</style></author><author><style face="normal" font="default" size="100%">Reyes, N</style></author><author><style face="normal" font="default" size="100%">Martín-Garrido, E</style></author><author><style face="normal" font="default" size="100%">Zurita, O</style></author><author><style face="normal" font="default" size="100%">Fernández-San José, P</style></author><author><style face="normal" font="default" size="100%">Pérez-Carro, R</style></author><author><style face="normal" font="default" size="100%">García-García, F</style></author><author><style face="normal" font="default" size="100%">Dopazo, J</style></author><author><style face="normal" font="default" size="100%">García-Sandoval, B</style></author><author><style face="normal" font="default" size="100%">Cuenca, N</style></author><author><style face="normal" font="default" size="100%">Ayuso, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identification of the Photoreceptor Transcriptional Co-Repressor SAMD11 as Novel Cause of Autosomal Recessive Retinitis Pigmentosa.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Co-Repressor Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Codon, Nonsense</style></keyword><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Comparative Genomic Hybridization</style></keyword><keyword><style  face="normal" font="default" size="100%">Consanguinity</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Mutational Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Eye Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Recessive</style></keyword><keyword><style  face="normal" font="default" size="100%">Homeodomain Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Homozygote</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Retina</style></keyword><keyword><style  face="normal" font="default" size="100%">Retinal Dystrophies</style></keyword><keyword><style  face="normal" font="default" size="100%">Retinal Rod Photoreceptor Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Retinitis pigmentosa</style></keyword><keyword><style  face="normal" font="default" size="100%">Spain</style></keyword><keyword><style  face="normal" font="default" size="100%">Trans-Activators</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Oct 13</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">35370</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Retinitis pigmentosa (RP), the most frequent form of inherited retinal dystrophy is characterized by progressive photoreceptor degeneration. Many genes have been implicated in RP development, but several others remain to be identified. Using a combination of homozygosity mapping, whole-exome and targeted next-generation sequencing, we found a novel homozygous nonsense mutation in SAMD11 in five individuals diagnosed with adult-onset RP from two unrelated consanguineous Spanish families. SAMD11 is ortholog to the mouse major retinal SAM domain (mr-s) protein that is implicated in CRX-mediated transcriptional regulation in the retina. Accordingly, protein-protein network analysis revealed a significant interaction of SAMD11 with CRX. Immunoblotting analysis confirmed strong expression of SAMD11 in human retina. Immunolocalization studies revealed SAMD11 was detected in the three nuclear layers of the human retina and interestingly differential expression between cone and rod photoreceptors was observed. Our study strongly implicates SAMD11 as novel cause of RP playing an important role in the pathogenesis of human degeneration of photoreceptors.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/27734943?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Moschen, Sebastián</style></author><author><style face="normal" font="default" size="100%">Bengoa Luoni, Sofía</style></author><author><style face="normal" font="default" size="100%">Di Rienzo, Julio A</style></author><author><style face="normal" font="default" size="100%">Caro, María Del Pilar</style></author><author><style face="normal" font="default" size="100%">Tohge, Takayuki</style></author><author><style face="normal" font="default" size="100%">Watanabe, Mutsumi</style></author><author><style face="normal" font="default" size="100%">Hollmann, Julien</style></author><author><style face="normal" font="default" size="100%">Gonzalez, Sergio</style></author><author><style face="normal" font="default" size="100%">Rivarola, Máximo</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Hopp, Horacio Esteban</style></author><author><style face="normal" font="default" size="100%">Hoefgen, Rainer</style></author><author><style face="normal" font="default" size="100%">Fernie, Alisdair R</style></author><author><style face="normal" font="default" size="100%">Paniego, Norma</style></author><author><style face="normal" font="default" size="100%">Fernandez, Paula</style></author><author><style face="normal" font="default" size="100%">Heinz, Ruth A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrating transcriptomic and metabolomic analysis to understand natural leaf senescence in sunflower.</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Biotechnol J</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Plant Biotechnol J</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Gas Chromatography-Mass Spectrometry</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Helianthus</style></keyword><keyword><style  face="normal" font="default" size="100%">Ions</style></keyword><keyword><style  face="normal" font="default" size="100%">metabolomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Leaves</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal Component Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Feb</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">719-34</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Leaf senescence is a complex process, which has dramatic consequences on crop yield. In sunflower, gap between potential and actual yields reveals the economic impact of senescence. Indeed, sunflower plants are incapable of maintaining their green leaf area over sustained periods. This study characterizes the leaf senescence process in sunflower through a systems biology approach integrating transcriptomic and metabolomic analyses: plants being grown under both glasshouse and field conditions. Our results revealed a correspondence between profile changes detected at the molecular, biochemical and physiological level throughout the progression of leaf senescence measured at different plant developmental stages. Early metabolic changes were detected prior to anthesis and before the onset of the first senescence symptoms, with more pronounced changes observed when physiological and molecular variables were assessed under field conditions. During leaf development, photosynthetic activity and cell growth processes decreased, whereas sucrose, fatty acid, nucleotide and amino acid metabolisms increased. Pathways related to nutrient recycling processes were also up-regulated. Members of the NAC, AP2-EREBP, HB, bZIP and MYB transcription factor families showed high expression levels, and their expression level was highly correlated, suggesting their involvement in sunflower senescence. The results of this study thus contribute to the elucidation of the molecular mechanisms involved in the onset and progression of leaf senescence in sunflower leaves as well as to the identification of candidate genes involved in this process. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26132509?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ibáñez, Mariam</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Such, Esperanza</style></author><author><style face="normal" font="default" size="100%">Jiménez-Almazán, Jorge</style></author><author><style face="normal" font="default" size="100%">Vidal, Enrique</style></author><author><style face="normal" font="default" size="100%">Barragán, Eva</style></author><author><style face="normal" font="default" size="100%">López-Pavía, María</style></author><author><style face="normal" font="default" size="100%">LLop, Marta</style></author><author><style face="normal" font="default" size="100%">Martín, Iván</style></author><author><style face="normal" font="default" size="100%">Gómez-Seguí, Inés</style></author><author><style face="normal" font="default" size="100%">Montesinos, Pau</style></author><author><style face="normal" font="default" size="100%">Sanz, Miguel A</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Cervera, José</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Mutational Landscape of Acute Promyelocytic Leukemia Reveals an Interacting Network of Co-Occurrences and Recurrent Mutations.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">INDEL Mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Leukemia, Promyelocytic, Acute</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Mutation Rate</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">e0148346</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Preliminary Acute Promyelocytic Leukemia (APL) whole exome sequencing (WES) studies have identified a huge number of somatic mutations affecting more than a hundred different genes mainly in a non-recurrent manner, suggesting that APL is a heterogeneous disease with secondary relevant changes not yet defined. To extend our knowledge of subtle genetic alterations involved in APL that might cooperate with PML/RARA in the leukemogenic process, we performed a comprehensive analysis of somatic mutations in APL combining WES with sequencing of a custom panel of targeted genes by next-generation sequencing. To select a reduced subset of high confidence candidate driver genes, further in silico analysis were carried out. After prioritization and network analysis we found recurrent deleterious mutations in 8 individual genes (STAG2, U2AF1, SMC1A, USP9X, IKZF1, LYN, MYCBP2 and PTPN11) with a strong potential of being involved in APL pathogenesis. Our network analysis of multiple mutations provides a reliable approach to prioritize genes for additional analysis, improving our knowledge of the leukemogenesis interactome. Additionally, we have defined a functional module in the interactome of APL. The hypothesis is that the number, or the specific combinations, of mutations harbored in each patient might not be as important as the disturbance caused in biological key functions, triggered by several not necessarily recurrent mutations. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26886259?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sevilla, Teresa</style></author><author><style face="normal" font="default" size="100%">Lupo, Vincenzo</style></author><author><style face="normal" font="default" size="100%">Martínez-Rubio, Dolores</style></author><author><style face="normal" font="default" size="100%">Sancho, Paula</style></author><author><style face="normal" font="default" size="100%">Sivera, Rafael</style></author><author><style face="normal" font="default" size="100%">Chumillas, María J</style></author><author><style face="normal" font="default" size="100%">García-Romero, Mar</style></author><author><style face="normal" font="default" size="100%">Pascual-Pascual, Samuel I</style></author><author><style face="normal" font="default" size="100%">Muelas, Nuria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Vílchez, Juan J</style></author><author><style face="normal" font="default" size="100%">Palau, Francesc</style></author><author><style face="normal" font="default" size="100%">Espinós, Carmen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mutations in the MORC2 gene cause axonal Charcot-Marie-Tooth disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Brain</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Brain</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Axons</style></keyword><keyword><style  face="normal" font="default" size="100%">Charcot-Marie-Tooth Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">gene expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Infant</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Pedigree</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Sciatic Nerve</style></keyword><keyword><style  face="normal" font="default" size="100%">Sural Nerve</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">139</style></volume><pages><style face="normal" font="default" size="100%">62-72</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Charcot-Marie-Tooth disease (CMT) is a complex disorder with wide genetic heterogeneity. Here we present a new axonal Charcot-Marie-Tooth disease form, associated with the gene microrchidia family CW-type zinc finger 2 (MORC2). Whole-exome sequencing in a family with autosomal dominant segregation identified the novel MORC2 p.R190W change in four patients. Further mutational screening in our axonal Charcot-Marie-Tooth disease clinical series detected two additional sporadic cases, one patient who also carried the same MORC2 p.R190W mutation and another patient that harboured a MORC2 p.S25L mutation. Genetic and in silico studies strongly supported the pathogenicity of these sequence variants. The phenotype was variable and included patients with congenital or infantile onset, as well as others whose symptoms started in the second decade. The patients with early onset developed a spinal muscular atrophy-like picture, whereas in the later onset cases, the initial symptoms were cramps, distal weakness and sensory impairment. Weakness and atrophy progressed in a random and asymmetric fashion and involved limb girdle muscles, leading to a severe incapacity in adulthood. Sensory loss was always prominent and proportional to disease severity. Electrophysiological studies were consistent with an asymmetric axonal motor and sensory neuropathy, while fasciculations and myokymia were recorded rather frequently by needle electromyography. Sural nerve biopsy revealed pronounced multifocal depletion of myelinated fibres with some regenerative clusters and occasional small onion bulbs. Morc2 is expressed in both axons and Schwann cells of mouse peripheral nerve. Different roles in biological processes have been described for MORC2. As the silencing of Charcot-Marie-Tooth disease genes have been associated with DNA damage response, it is tempting to speculate that a deregulation of this pathway may be linked to the axonal degeneration observed in MORC2 neuropathy, thus adding a new pathogenic mechanism to the long list of causes of Charcot-Marie-Tooth disease. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Pt 1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26497905?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Falco, Matias M</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The pan-cancer pathological regulatory landscape.</style></title><secondary-title><style face="normal" font="default" size="100%">Scientific reports</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Dec 21</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/articles/srep39709</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">39709</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Dysregulation of the normal gene expression program is the cause of a broad range of diseases, including cancer. Detecting the specific perturbed regulators that have an effect on the generation and the development of the disease is crucial for understanding the disease mechanism and for taking decisions on efficient preventive and curative therapies. Moreover, detecting such perturbations at the patient level is even more important from the perspective of personalized medicine. We applied the Transcription Factor Target Enrichment Analysis, a method that detects the activity of transcription factors based on the quantification of the collective transcriptional activation of their targets, to a large collection of 5607 cancer samples covering eleven cancer types. We produced for the first time a comprehensive catalogue of altered transcription factor activities in cancer, a considerable number of them significantly associated to patient’s survival. Moreover, we described several interesting TFs whose activity do not change substantially in the cancer with respect to the normal tissue but ultimately play an important role in patient prognostic determination, which suggest they might be promising therapeutic targets. An additional advantage of this method is that it allows obtaining personalized TF activity estimations for individual patients.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Falco, Matias M.</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The pan-cancer pathological regulatory landscape</style></title><secondary-title><style face="normal" font="default" size="100%">Scientific Reports</style></secondary-title><short-title><style face="normal" font="default" size="100%">Sci Rep</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-12-2016</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/articles/srep39709http://www.nature.com/articles/srep39709.pdfhttp://www.nature.com/articles/srep39709.pdfhttp://www.nature.com/articles/srep39709</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">6</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maroñas, Olalla</style></author><author><style face="normal" font="default" size="100%">Latorre, Ana</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Pirmohamed, Munir</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Antona, Cristina</style></author><author><style face="normal" font="default" size="100%">Siest, Gérard</style></author><author><style face="normal" font="default" size="100%">Carracedo, Ángel</style></author><author><style face="normal" font="default" size="100%">LLerena, Adrián</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Progress in pharmacogenetics: consortiums and new strategies.</style></title><secondary-title><style face="normal" font="default" size="100%">Drug Metab Pers Ther</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Drug Metab Pers Ther</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cooperative Behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Screening Assays</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Patient Care Team</style></keyword><keyword><style  face="normal" font="default" size="100%">pharmacogenetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Precision Medicine</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Mar</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">17-23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Pharmacogenetics (PGx), as a field dedicated to achieving the goal of personalized medicine (PM), is devoted to the study of genes involved in inter-individual response to drugs. Due to its nature, PGx requires access to large samples; therefore, in order to progress, the formation of collaborative consortia seems to be crucial. Some examples of this collective effort are the European Society of Pharmacogenomics and personalized Therapy and the Ibero-American network of Pharmacogenetics. As an emerging field, one of the major challenges that PGx faces is translating their discoveries from research bench to bedside. The development of genomic high-throughput technologies is generating a revolution and offers the possibility of producing vast amounts of genome-wide single nucleotide polymorphisms for each patient. Moreover, there is a need of identifying and replicating associations of new biomarkers, and, in addition, a greater effort must be invested in developing regulatory organizations to accomplish a correct standardization. In this review, we outline the current progress in PGx using examples to highlight both the importance of polymorphisms and the research strategies for their detection. These concepts need to be applied together with a proper dissemination of knowledge to improve clinician and patient understanding, in a multidisciplinary team-based approach. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26913460?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Urreizti, Roser</style></author><author><style face="normal" font="default" size="100%">Roca-Ayats, Neus</style></author><author><style face="normal" font="default" size="100%">Trepat, Judith</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Alemán, Alejandro</style></author><author><style face="normal" font="default" size="100%">Orteschi, Daniela</style></author><author><style face="normal" font="default" size="100%">Marangi, Giuseppe</style></author><author><style face="normal" font="default" size="100%">Neri, Giovanni</style></author><author><style face="normal" font="default" size="100%">Opitz, John M</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Cormand, Bru</style></author><author><style face="normal" font="default" size="100%">Vilageliu, Lluïsa</style></author><author><style face="normal" font="default" size="100%">Balcells, Susana</style></author><author><style face="normal" font="default" size="100%">Grinberg, Daniel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Screening of CD96 and ASXL1 in 11 patients with Opitz C or Bohring-Opitz syndromes.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Med Genet A</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am J Med Genet A</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Antigens, CD</style></keyword><keyword><style  face="normal" font="default" size="100%">Child</style></keyword><keyword><style  face="normal" font="default" size="100%">Child, Preschool</style></keyword><keyword><style  face="normal" font="default" size="100%">Craniosynostoses</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Infant</style></keyword><keyword><style  face="normal" font="default" size="100%">Intellectual Disability</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Pedigree</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Prognosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Repressor Proteins</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">170A</style></volume><pages><style face="normal" font="default" size="100%">24-31</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Opitz C trigonocephaly (or Opitz C syndrome, OTCS) and Bohring-Opitz syndrome (BOS or C-like syndrome) are two rare genetic disorders with phenotypic overlap. The genetic causes of these diseases are not understood. However, two genes have been associated with OTCS or BOS with dominantly inherited de novo mutations. Whereas CD96 has been related to OTCS (one case) and to BOS (one case), ASXL1 has been related to BOS only (several cases). In this study we analyze CD96 and ASXL1 in a group of 11 affected individuals, including 2 sibs, 10 of them were diagnosed with OTCS, and one had a BOS phenotype. Exome sequences were available on six patients with OTCS and three parent pairs. Thus, we could analyze the CD96 and ASXL1 sequences in these patients bioinformatically. Sanger sequencing of all exons of CD96 and ASXL1 was carried out in the remaining patients. Detailed scrutiny of the sequences and assessment of variants allowed us to exclude putative pathogenic and private mutations in all but one of the patients. In this patient (with BOS) we identified a de novo mutation in ASXL1 (c.2100dupT). By nature and location within the gene, this mutation resembles those previously described in other BOS patients and we conclude that it may be responsible for the condition. Our results indicate that in 10 of 11, the disease (OTCS or BOS) cannot be explained by small changes in CD96 or ASXL1. However, the cohort is too small to make generalizations about the genetic etiology of these diseases.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26768331?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Puchades-Carrasco, Leonor</style></author><author><style face="normal" font="default" size="100%">Jantus-Lewintre, Eloisa</style></author><author><style face="normal" font="default" size="100%">Pérez-Rambla, Clara</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Lucas, Rut</style></author><author><style face="normal" font="default" size="100%">Calabuig, Silvia</style></author><author><style face="normal" font="default" size="100%">Blasco, Ana</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Camps, Carlos</style></author><author><style face="normal" font="default" size="100%">Pineda-Lucena, Antonio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Serum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer.</style></title><secondary-title><style face="normal" font="default" size="100%">Oncotarget</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Oncotarget</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Biomarkers, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Carcinoma, Non-Small-Cell Lung</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease Progression</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Lung Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">metabolomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Proton Magnetic Resonance Spectroscopy</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Mar 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">12904-16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Lung cancer (LC) is responsible for most cancer deaths. One of the main factors contributing to the lethality of this disease is the fact that a large proportion of patients are diagnosed at advanced stages when a clinical intervention is unlikely to succeed. In this study, we evaluated the potential of metabolomics by 1H-NMR to facilitate the identification of accurate and reliable biomarkers to support the early diagnosis and prognosis of non-small cell lung cancer (NSCLC).We found that the metabolic profile of NSCLC patients, compared with healthy individuals, is characterized by statistically significant changes in the concentration of 18 metabolites representing different amino acids, organic acids and alcohols, as well as different lipids and molecules involved in lipid metabolism. Furthermore, the analysis of the differences between the metabolic profiles of NSCLC patients at different stages of the disease revealed the existence of 17 metabolites involved in metabolic changes associated with disease progression.Our results underscore the potential of metabolomics profiling to uncover pathophysiological mechanisms that could be useful to objectively discriminate NSCLC patients from healthy individuals, as well as between different stages of the disease. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26883203?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Razzoli, Maria</style></author><author><style face="normal" font="default" size="100%">Frontini, Andrea</style></author><author><style face="normal" font="default" size="100%">Gurney, Allison</style></author><author><style face="normal" font="default" size="100%">Mondini, Eleonora</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Katz, Liora S</style></author><author><style face="normal" font="default" size="100%">Cero, Cheryl</style></author><author><style face="normal" font="default" size="100%">Bolan, Patrick J</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Vidal-Puig, Antonio</style></author><author><style face="normal" font="default" size="100%">Cinti, Saverio</style></author><author><style face="normal" font="default" size="100%">Bartolomucci, Alessandro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Stress-induced activation of brown adipose tissue prevents obesity in conditions of low adaptive thermogenesis.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Metab</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Metab</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">19-33</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Stress-associated conditions such as psychoemotional reactivity and depression have been paradoxically linked to either weight gain or weight loss. This bi-directional effect of stress is not understood at the functional level. Here we tested the hypothesis that pre-stress level of adaptive thermogenesis and brown adipose tissue (BAT) functions explain the vulnerability or resilience to stress-induced obesity.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We used wt and triple β1,β2,β3-Adrenergic Receptors knockout (β-less) mice exposed to a model of chronic subordination stress (CSS) at either room temperature (22 °C) or murine thermoneutrality (30 °C). A combined behavioral, physiological, molecular, and immunohistochemical analysis was conducted to determine stress-induced modulation of energy balance and BAT structure and function. Immortalized brown adipocytes were used for in vitro assays.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Departing from our initial observation that βARs are dispensable for cold-induced BAT browning, we demonstrated that under physiological conditions promoting low adaptive thermogenesis and BAT activity (e.g. thermoneutrality or genetic deletion of the βARs), exposure to CSS acted as a stimulus for BAT activation and thermogenesis, resulting in resistance to diet-induced obesity despite the presence of hyperphagia. Conversely, in wt mice acclimatized to room temperature, and therefore characterized by sustained BAT function, exposure to CSS increased vulnerability to obesity. Exposure to CSS enhanced the sympathetic innervation of BAT in wt acclimatized to thermoneutrality and in β-less mice. Despite increased sympathetic innervation suggesting adrenergic-mediated browning, norepinephrine did not promote browning in βARs knockout brown adipocytes, which led us to identify an alternative sympathetic/brown adipocytes purinergic pathway in the BAT. This pathway is downregulated under conditions of low adaptive thermogenesis requirements, is induced by stress, and elicits activation of UCP1 in wt and β-less brown adipocytes. Importantly, this purinergic pathway is conserved in human BAT.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Our findings demonstrate that thermogenesis and BAT function are determinant of the resilience or vulnerability to stress-induced obesity. Our data support a model in which adrenergic and purinergic pathways exert complementary/synergistic functions in BAT, thus suggesting an alternative to βARs agonists for the activation of human BAT.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26844204?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hillung, Julia</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Cuevas, José M</style></author><author><style face="normal" font="default" size="100%">Elena, Santiago F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The transcriptomics of an experimentally evolved plant-virus interaction.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Ecotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Host-Pathogen Interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">Potyvirus</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 04 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">24901</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Models of plant-virus interaction assume that the ability of a virus to infect a host genotype depends on the matching between virulence and resistance genes. Recently, we evolved tobacco etch potyvirus (TEV) lineages on different ecotypes of Arabidopsis thaliana, and found that some ecotypes selected for specialist viruses whereas others selected for generalists. Here we sought to evaluate the transcriptomic basis of such relationships. We have characterized the transcriptomic responses of five ecotypes infected with the ancestral and evolved viruses. Genes and functional categories differentially expressed by plants infected with local TEV isolates were identified, showing heterogeneous responses among ecotypes, although significant parallelism existed among lineages evolved in the same ecotype. Although genes involved in immune responses were altered upon infection, other functional groups were also pervasively over-represented, suggesting that plant resistance genes were not the only drivers of viral adaptation. Finally, the transcriptomic consequences of infection with the generalist and specialist lineages were compared. Whilst the generalist induced very similar perturbations in the transcriptomes of the different ecotypes, the perturbations induced by the specialist were divergent. Plant defense mechanisms were activated when the infecting virus was specialist but they were down-regulated when infecting with generalist.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/27113435?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alonso, Roberto</style></author><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Sanchis-Juan, Alba</style></author><author><style face="normal" font="default" size="100%">Perez-Gil, Daniel</style></author><author><style face="normal" font="default" size="100%">Marin-Garcia, Pablo</style></author><author><style face="normal" font="default" size="100%">Sánchez, Rubén</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Hernansaiz-Ballesteros, Rosa D</style></author><author><style face="normal" font="default" size="100%">Alemán, Alejandro</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Babelomics 5.0: functional interpretation for new generations of genomic data.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic acids research</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword><keyword><style  face="normal" font="default" size="100%">data integration</style></keyword><keyword><style  face="normal" font="default" size="100%">gene set analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">interactome</style></keyword><keyword><style  face="normal" font="default" size="100%">network analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-seq</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword><keyword><style  face="normal" font="default" size="100%">transcriptomics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Apr 20</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/content/43/W1/W117</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">W1</style></number><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">W117-W121</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Romero, Atocha</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">López-Perolio, Irene</style></author><author><style face="normal" font="default" size="100%">Ruiz de Garibay, Gorka</style></author><author><style face="normal" font="default" size="100%">García-Sáenz, José A</style></author><author><style face="normal" font="default" size="100%">Garre, Pilar</style></author><author><style face="normal" font="default" size="100%">Ayllón, Patricia</style></author><author><style face="normal" font="default" size="100%">Benito, Esperanza</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Díaz-Rubio, Eduardo</style></author><author><style face="normal" font="default" size="100%">Caldés, Trinidad</style></author><author><style face="normal" font="default" size="100%">de la Hoya, Miguel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">BRCA1 Alternative splicing landscape in breast tissue samples.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC cancer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.biomedcentral.com/1471-2407/15/219</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">219</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BACKGROUND: BRCA1 is a key protein in cell network, involved in DNA repair pathways and cell cycle. Recently, the ENIGMA consortium has reported a high number of alternative splicing (AS) events at this locus in blood-derived samples. However, BRCA1 splicing pattern in breast tissue samples is unknown. Here, we provide an accurate description of BRCA1 splicing events distribution in breast tissue samples. METHODS: BRCA1 splicing events were scanned in 70 breast tumor samples, 4 breast samples from healthy individuals and in 72 blood-derived samples by capillary electrophoresis (capillary EP). Molecular subtype was identified in all tumor samples. Splicing events were considered predominant if their relative expression level was at least the 10% of the full-length reference signal. RESULTS: 54 BRCA1 AS events were identified, 27 of them were annotated as predominant in at least one sample. Δ5q, Δ13, Δ9, Δ5 and ▼1aA were significantly more frequently annotated as predominant in breast tumor samples than in blood-derived samples. Predominant splicing events were, on average, more frequent in tumor samples than in normal breast tissue samples (P = 0.010). Similarly, likely inactivating splicing events (PTC-NMDs, Non-Coding, Δ5 and Δ18) were more frequently annotated as predominant in tumor than in normal breast samples (P = 0.020), whereas there were no significant differences for other splicing events (No-Fs) frequency distribution between tumor and normal breast samples (P = 0.689). CONCLUSIONS: Our results complement recent findings by the ENIGMA consortium, demonstrating that BRCA1 AS, despite its tremendous complexity, is similar in breast and blood samples, with no evidences for tissue specific AS events. Further on, we conclude that somatic inactivation of BRCA1 through spliciogenic mutations is, at best, a rare mechanism in breast carcinogenesis, albeit our data detects an excess of likely inactivating AS events in breast tumor samples.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ewing, Adam D</style></author><author><style face="normal" font="default" size="100%">Houlahan, Kathleen E</style></author><author><style face="normal" font="default" size="100%">Hu, Yin</style></author><author><style face="normal" font="default" size="100%">Ellrott, Kyle</style></author><author><style face="normal" font="default" size="100%">Caloian, Cristian</style></author><author><style face="normal" font="default" size="100%">Yamaguchi, Takafumi N</style></author><author><style face="normal" font="default" size="100%">Bare, J Christopher</style></author><author><style face="normal" font="default" size="100%">P’ng, Christine</style></author><author><style face="normal" font="default" size="100%">Waggott, Daryl</style></author><author><style face="normal" font="default" size="100%">Sabelnykova, Veronica Y</style></author><author><style face="normal" font="default" size="100%">Kellen, Michael R</style></author><author><style face="normal" font="default" size="100%">Norman, Thea C</style></author><author><style face="normal" font="default" size="100%">Haussler, David</style></author><author><style face="normal" font="default" size="100%">Friend, Stephen H</style></author><author><style face="normal" font="default" size="100%">Stolovitzky, Gustavo</style></author><author><style face="normal" font="default" size="100%">Margolin, Adam A</style></author><author><style face="normal" font="default" size="100%">Stuart, Joshua M</style></author><author><style face="normal" font="default" size="100%">Boutros, Paul C</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">ICGC-TCGA DREAM Somatic Mutation Calling Challenge participants</style></author><author><style face="normal" font="default" size="100%">Liu Xi</style></author><author><style face="normal" font="default" size="100%">Ninad Dewal</style></author><author><style face="normal" font="default" size="100%">Yu Fan</style></author><author><style face="normal" font="default" size="100%">Wenyi Wang</style></author><author><style face="normal" font="default" size="100%">David Wheeler</style></author><author><style face="normal" font="default" size="100%">Andreas Wilm</style></author><author><style face="normal" font="default" size="100%">Grace Hui Ting</style></author><author><style face="normal" font="default" size="100%">Chenhao Li</style></author><author><style face="normal" font="default" size="100%">Denis Bertrand</style></author><author><style face="normal" font="default" size="100%">Niranjan Nagarajan</style></author><author><style face="normal" font="default" size="100%">Qing-Rong Chen</style></author><author><style face="normal" font="default" size="100%">Chih-Hao Hsu</style></author><author><style face="normal" font="default" size="100%">Ying Hu</style></author><author><style face="normal" font="default" size="100%">Chunhua Yan</style></author><author><style face="normal" font="default" size="100%">Warren Kibbe</style></author><author><style face="normal" font="default" size="100%">Daoud Meerzaman</style></author><author><style face="normal" font="default" size="100%">Kristian Cibulskis</style></author><author><style face="normal" font="default" size="100%">Mara Rosenberg</style></author><author><style face="normal" font="default" size="100%">Louis Bergelson</style></author><author><style face="normal" font="default" size="100%">Adam Kiezun</style></author><author><style face="normal" font="default" size="100%">Amie Radenbaugh</style></author><author><style face="normal" font="default" size="100%">Anne-Sophie Sertier</style></author><author><style face="normal" font="default" size="100%">Anthony Ferrari</style></author><author><style face="normal" font="default" size="100%">Laurie Tonton</style></author><author><style face="normal" font="default" size="100%">Kunal Bhutani</style></author><author><style face="normal" font="default" size="100%">Nancy F Hansen</style></author><author><style face="normal" font="default" size="100%">Difei Wang</style></author><author><style face="normal" font="default" size="100%">Lei Song</style></author><author><style face="normal" font="default" size="100%">Zhongwu Lai</style></author><author><style face="normal" font="default" size="100%">Liao, Yang</style></author><author><style face="normal" font="default" size="100%">Shi, Wei</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Cheryl C K Lau</style></author><author><style face="normal" font="default" size="100%">Justin Guinney</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature methods</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cancer</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">variant calling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 May 18</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3407.html</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rodríguez-de la Rosa, Lourdes</style></author><author><style face="normal" font="default" size="100%">Sánchez-Calderón, Hortensia</style></author><author><style face="normal" font="default" size="100%">Contreras, Julio</style></author><author><style face="normal" font="default" size="100%">Murillo-Cuesta, Silvia</style></author><author><style face="normal" font="default" size="100%">Falagan, Sandra</style></author><author><style face="normal" font="default" size="100%">Avendaño, Carlos</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Varela-Nieto, Isabel</style></author><author><style face="normal" font="default" size="100%">Milo, Marta</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparative gene expression study of the vestibular organ of the Igf1 deficient mouse using whole-transcript arrays.</style></title><secondary-title><style face="normal" font="default" size="100%">Hearing research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Sep 1</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0378595515001835</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The auditory and vestibular organs form the inner ear and have a common developmental origin. Insulin like growth factor 1 (IGF-1) has a central role in the development of the cochlea and maintenance of hearing. Its deficiency causes sensorineural hearing loss in man and mice. During chicken early development, IGF-1 modulates neurogenesis of the cochleovestibular ganglion but no further studies have been conducted to explore the potential role of IGF-1 in the vestibular system. In this study we have compared the whole transcriptome of the vestibular organ from wild type and Igf1(-/-) mice at different developmental and postnatal times. RNA was prepared from E18.5, P15 and P90 vestibular organs of Igf1(-/-) and Igf1(+/+) mice and the transcriptome analysed in triplicates using Affymetrix® Mouse Gene 1.1 ST Array Plates. These plates are whole-transcript arrays that include probes to measure both messenger (mRNA) and long intergenic non-coding RNA transcripts (lincRNA), with a coverage of over 28 thousand coding transcripts and over 7 thousands non-coding transcripts. Given the complexity of the data we used two different methods VSN-RMA and mmBGX to analyse and compare the data. This is to better evaluate the number of false positives and to quantify uncertainty of low signals. We identified a number of differentially expressed genes that we described using functional analysis and validated using RT-qPCR. The morphology of the vestibular organ did not show differences between genotypes and no evident alterations were observed in the vestibular sensory areas of the null mice. However, well-defined cellular alterations were found in the vestibular neurons with respect their number and size. Although these mice did not show a dramatic vestibular phenotype, we conducted a functional analysis on differentially expressed genes between genotypes and across time. This was with the aim to identify new pathways that are involved in the development of the vestibular organ as well as pathways that maybe affected by the lack of IGF-1 and be associated to the morphological changes of the vestibular neurons that we observed in the Igf1(-/-) mice.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martinez, Hector</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Barrachina, Sergio</style></author><author><style face="normal" font="default" size="100%">Castillo, Maribel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Quintana-Orti, Enrique S</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Concurrent and Accurate Short Read Mapping on Multicore Processors.</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">HPC</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">short real mapping</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Sep-Oct</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&amp;arnumber=7010005</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">995-1007</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We introduce a parallel aligner with a work-flow organization for fast and accurate mapping of RNA sequences on servers equipped with multicore processors. Our software, [Formula: see text] ([Formula: see text] is an open-source application. The software is available at http://www.opencb.org, exploits a suffix array to rapidly map a large fraction of the RNA fragments (reads), as well as leverages the accuracy of the Smith-Waterman algorithm to deal with conflictive reads. The aligner is enhanced with a careful strategy to detect splice junctions based on an adaptive division of RNA reads into small segments (or seeds), which are then mapped onto a number of candidate alignment locations, providing crucial information for the successful alignment of the complete reads. The experimental results on a platform with Intel multicore technology report the parallel performance of [Formula: see text], on RNA reads of 100-400 nucleotides, which excels in execution time/sensitivity to state-of-the-art aligners such as TopHat 2+Bowtie 2, MapSplice, and STAR.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carretero, M</style></author><author><style face="normal" font="default" size="100%">Guerrero-Aspizua, S</style></author><author><style face="normal" font="default" size="100%">Illera, N</style></author><author><style face="normal" font="default" size="100%">Galvez, V</style></author><author><style face="normal" font="default" size="100%">Navarro, M</style></author><author><style face="normal" font="default" size="100%">García-García, F</style></author><author><style face="normal" font="default" size="100%">Dopazo, J</style></author><author><style face="normal" font="default" size="100%">Jorcano, J L</style></author><author><style face="normal" font="default" size="100%">Larcher, F</style></author><author><style face="normal" font="default" size="100%">Del Rio, M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Differential Features Between Chronic Skin Inflammatory Diseases Revealed in Skin-Humanized Psoriasis and Atopic Dermatitis Mouse Models.</style></title><secondary-title><style face="normal" font="default" size="100%">J Invest Dermatol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J. Invest. Dermatol.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Sep 23</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Psoriasis (PS) and atopic dermatitis (AD) are chronic and relapsing inflammatory diseases of the skin affecting a large number of patients worldwide. Psoriasis is characterized by a Th1/Th17 immunological response whereas acute AD lesions exhibit Th2-dominant inflammation. Current single gene and signaling pathways-based models of inflammatory skin diseases are incomplete. Previous work allowed us to model psoriasis in skin-humanized mice through proper combinations of inflammatory cell components and disruption of barrier function. Herein we describe and characterize an animal model for AD using similar bioengineered-based approaches, by intradermal injection of human Th2 lymphocytes in regenerated human skin after partial removal of stratum corneum. In the present work we have extensively compared this model with the previous and an improved version of the PS model, in which Th17/Th1 lymphocytes replace exogenous cytokines. Comparative expression analyses revealed marked differences in specific epidermal proliferation and differentiation markers and immune-related molecules including antimicrobial peptides. Likewise, the composition of the dermal inflammatory infiltrate presented important differences. Availability of accurate and reliable animal models for these diseases will contribute to the understanding of the pathogenesis and provide valuable tools for drug development and testing.Journal of Investigative Dermatology accepted article preview online, 23 September 2015. doi:10.1038/jid.2015.362.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26398345?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sevilla, T</style></author><author><style face="normal" font="default" size="100%">Sivera, R</style></author><author><style face="normal" font="default" size="100%">Martínez-Rubio, D</style></author><author><style face="normal" font="default" size="100%">Lupo, V</style></author><author><style face="normal" font="default" size="100%">Chumillas, M J</style></author><author><style face="normal" font="default" size="100%">Calpena, E</style></author><author><style face="normal" font="default" size="100%">Dopazo, J</style></author><author><style face="normal" font="default" size="100%">Vílchez, J J</style></author><author><style face="normal" font="default" size="100%">Palau, F</style></author><author><style face="normal" font="default" size="100%">Espinós, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The EGR2 gene is involved in axonal Charcot-Marie-Tooth disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Eur J Neurol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Eur J Neurol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged, 80 and over</style></keyword><keyword><style  face="normal" font="default" size="100%">Axons</style></keyword><keyword><style  face="normal" font="default" size="100%">Charcot-Marie-Tooth Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Early Growth Response Protein 2</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Pedigree</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Severity of Illness Index</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Dec</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">1548-55</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND AND PURPOSE: &lt;/b&gt;A three-generation family affected by axonal Charcot-Marie-Tooth disease (CMT) was investigated with the aim of discovering genetic defects and to further characterize the phenotype.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;The clinical, nerve conduction studies and muscle magnetic resonance images of the patients were reviewed. A whole exome sequencing was performed and the changes were investigated by genetic studies, in silico analysis and luciferase reporter assays.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;A novel c.1226G&gt;A change (p.R409Q) in the EGR2 gene was identified. Patients presented with a typical, late-onset axonal CMT phenotype with variable severity that was confirmed in the ancillary tests. The in silico studies showed that the residue R409 is an evolutionary conserved amino acid. The p.R409Q mutation, which is predicted as probably damaging, would alter the conformation of the protein slightly and would cause a decrease of gene expression.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;This is the first report of an EGR2 mutation presenting as an axonal CMT phenotype with variable severity. This study broadens the phenotype of the EGR2-related neuropathies and suggests that the genetic testing of patients suffering from axonal CMT should include the EGR2 gene.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26204789?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luzón-Toro, Berta</style></author><author><style face="normal" font="default" size="100%">Gui, Hongsheng</style></author><author><style face="normal" font="default" size="100%">Ruiz-Ferrer, Macarena</style></author><author><style face="normal" font="default" size="100%">Sze-Man Tang, Clara</style></author><author><style face="normal" font="default" size="100%">Fernández, Raquel M.</style></author><author><style face="normal" font="default" size="100%">Sham, Pak-Chung</style></author><author><style face="normal" font="default" size="100%">Torroglosa, Ana</style></author><author><style face="normal" font="default" size="100%">Kwong-Hang Tam, Paul</style></author><author><style face="normal" font="default" size="100%">Espino-Paisán, Laura</style></author><author><style face="normal" font="default" size="100%">Cherny, Stacey S.</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Enguix-Riego, María Del Valle</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author><author><style face="normal" font="default" size="100%">Garcia-Barceló, Maria-Mercè</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exome sequencing reveals a high genetic heterogeneity on familial Hirschsprung disease</style></title><secondary-title><style face="normal" font="default" size="100%">Scientific Reports</style></secondary-title><short-title><style face="normal" font="default" size="100%">Sci Rep</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-12-2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/articles/srep16473http://www.nature.com/articles/srep16473.pdfhttp://www.nature.com/articles/srep16473.pdfhttp://www.nature.com/articles/srep16473</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">5</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luzón-Toro, Berta</style></author><author><style face="normal" font="default" size="100%">Gui, Hongsheng</style></author><author><style face="normal" font="default" size="100%">Ruiz-Ferrer, Macarena</style></author><author><style face="normal" font="default" size="100%">Sze-Man Tang, Clara</style></author><author><style face="normal" font="default" size="100%">Fernández, Raquel M</style></author><author><style face="normal" font="default" size="100%">Sham, Pak-Chung</style></author><author><style face="normal" font="default" size="100%">Torroglosa, Ana</style></author><author><style face="normal" font="default" size="100%">Kwong-Hang Tam, Paul</style></author><author><style face="normal" font="default" size="100%">Espino-Paisán, Laura</style></author><author><style face="normal" font="default" size="100%">Cherny, Stacey S</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Enguix-Riego, María Del Valle</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author><author><style face="normal" font="default" size="100%">Garcia-Barceló, Maria-Mercè</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exome sequencing reveals a high genetic heterogeneity on familial Hirschsprung disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Scientific reports</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Hirschprung</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">prioritization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/articles/srep16473</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">16473</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Hirschsprung disease (HSCR; OMIM 142623) is a developmental disorder characterized by aganglionosis along variable lengths of the distal gastrointestinal tract, which results in intestinal obstruction. Interactions among known HSCR genes and/or unknown disease susceptibility loci lead to variable severity of phenotype. Neither linkage nor genome-wide association studies have efficiently contributed to completely dissect the genetic pathways underlying this complex genetic disorder. We have performed whole exome sequencing of 16 HSCR patients from 8 unrelated families with SOLID platform. Variants shared by affected relatives were validated by Sanger sequencing. We searched for genes recurrently mutated across families. Only variations in the FAT3 gene were significantly enriched in five families. Within-family analysis identified compound heterozygotes for AHNAK and several genes (N = 23) with heterozygous variants that co-segregated with the phenotype. Network and pathway analyses facilitated the discovery of polygenic inheritance involving FAT3, HSCR known genes and their gene partners. Altogether, our approach has facilitated the detection of more than one damaging variant in biologically plausible genes that could jointly contribute to the phenotype. Our data may contribute to the understanding of the complex interactions that occur during enteric nervous system development and the etiopathology of familial HSCR.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fonseca, Renata F</style></author><author><style face="normal" font="default" size="100%">de Carvalho, Flávia M</style></author><author><style face="normal" font="default" size="100%">Poletta, Fernando A</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Mereb, Juan C</style></author><author><style face="normal" font="default" size="100%">Moreira, Miguel A M</style></author><author><style face="normal" font="default" size="100%">Seuanez, Hector N</style></author><author><style face="normal" font="default" size="100%">Vieira, Alexandre R</style></author><author><style face="normal" font="default" size="100%">Castilla, Eduardo E</style></author><author><style face="normal" font="default" size="100%">Orioli, Iêda M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Family-based genome-wide association study in Patagonia confirms the association of the DMD locus and cleft lip and palate.</style></title><secondary-title><style face="normal" font="default" size="100%">Eur J Oral Sci</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Eur J Oral Sci</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Oct</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">123</style></volume><pages><style face="normal" font="default" size="100%">381-384</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The etiology of cleft lip with or without cleft palate (CL±P) is complex and heterogeneous, and multiple genetic and environmental factors are involved. Some candidate genes reported to be associated with oral clefts are located on the X chromosome. At least three genes causing X-linked syndromes [midline 1 (MID1), oral-facial-digital syndrome 1 (OFD1), and dystrophin (DMD)] were previously found to be associated with isolated CL±P. We attempted to confirm the role of X-linked genes in the etiology of isolated CL±P in a South American population through a family-based genome-wide scan. We studied 27 affected children and their mothers, from 26 families, in a Patagonian population with a high prevalence of CL±P. We conducted an exploratory analysis of the X chromosome to identify candidate regions associated with CL±P. Four genomic segments were identified, two of which showed a statistically significant association with CL±P. One is an 11-kb region of Xp21.1 containing the DMD gene, and the other is an intergenic region (8.7 kb; Xp11.4). Our results are consistent with recent data on the involvement of the DMD gene in the etiology of CL±P. The MID1 and OFD1 genes were not included in the four potential CL±P-associated X-chromosome genomic segments.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26331285?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Terol, Javier</style></author><author><style face="normal" font="default" size="100%">Ibañez, Victoria</style></author><author><style face="normal" font="default" size="100%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Alonso, Roberto</style></author><author><style face="normal" font="default" size="100%">Estornell, Leandro H.</style></author><author><style face="normal" font="default" size="100%">Licciardello, Concetta</style></author><author><style face="normal" font="default" size="100%">Gut, Ivo G.</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Talon, Manuel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Involvement of a citrus meiotic recombination TTC-repeat motif in the formation of gross deletions generated by ionizing radiation and MULE activation</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Genomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Feb</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1186/s12864-015-1280-3</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">69</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Transposable-element mediated chromosomal rearrangements require the involvement of two transposons and two double-strand breaks (DSB) located in close proximity. In radiobiology, DSB proximity is also a major factor contributing to rearrangements. However, the whole issue of DSB proximity remains virtually unexplored.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Terol, Javier</style></author><author><style face="normal" font="default" size="100%">Ibañez, Victoria</style></author><author><style face="normal" font="default" size="100%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Alonso, Roberto</style></author><author><style face="normal" font="default" size="100%">Estornell, Leandro H</style></author><author><style face="normal" font="default" size="100%">Licciardello, Concetta</style></author><author><style face="normal" font="default" size="100%">Gut, Ivo G</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Talon, Manuel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Involvement of a citrus meiotic recombination TTC-repeat motif in the formation of gross deletions generated by ionizing radiation and MULE activation.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC genomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Feb 13</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.biomedcentral.com/1471-2164/16/69</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">69</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BACKGROUND: Transposable-element mediated chromosomal rearrangements require the involvement of two transposons and two double-strand breaks (DSB) located in close proximity. In radiobiology, DSB proximity is also a major factor contributing to rearrangements. However, the whole issue of DSB proximity remains virtually unexplored. RESULTS: Based on DNA sequencing analysis we show that the genomes of 2 derived mutations, Arrufatina (sport) and Nero (irradiation), share a similar 2 Mb deletion of chromosome 3. A 7 kb Mutator-like element found in Clemenules was present in Arrufatina in inverted orientation flanking the 5’ end of the deletion. The Arrufatina Mule displayed &quot;dissimilar&quot; 9-bp target site duplications separated by 2 Mb. Fine-scale single nucleotide variant analyses of the deleted fragments identified a TTC-repeat sequence motif located in the center of the deletion responsible of a meiotic crossover detected in the citrus reference genome. CONCLUSIONS: Taken together, this information is compatible with the proposal that in both mutants, the TTC-repeat motif formed a triplex DNA structure generating a loop that brought in close proximity the originally distinct reactive ends. In Arrufatina, the loop brought the Mule ends nearby the 2 distinct insertion target sites and the inverted insertion of the transposable element between these target sites provoked the release of the in-between fragment. This proposal requires the involvement of a unique transposon and sheds light on the unresolved question of how two distinct sites become located in close proximity. These observations confer a crucial role to the TTC-repeats in fundamental plant processes as meiotic recombination and chromosomal rearrangements.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Alonso, Roberto</style></author><author><style face="normal" font="default" size="100%">Ibañez, Victoria</style></author><author><style face="normal" font="default" size="100%">Terol, Javier</style></author><author><style face="normal" font="default" size="100%">Talon, Manuel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A phylogenetic analysis of 34 chloroplast genomes elucidates the relationships between wild and domestic species within the genus Citrus.</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular biology and evolution</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">chloroplast</style></keyword><keyword><style  face="normal" font="default" size="100%">citrus</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">WGS</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Apr 14</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://mbe.oxfordjournals.org/content/early/2015/04/27/molbev.msv082.full</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">8</style></number><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">2015-2035</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Citrus genus includes some of the most important cultivated fruit trees worldwide. Despite being extensively studied because of its commercial relevance, the origin of cultivated citrus species and the history of its domestication still remain an open question. Here we present a phylogenetic analysis of the chloroplast genomes of 34 citrus genotypes which constitutes the most comprehensive and detailed study to date on the evolution and variability of the genus Citrus. A statistical model was used to estimate divergence times between the major citrus groups. Additionally, a complete map of the variability across the genome of different citrus species was produced, including single nucleotide variants, heteroplasmic positions, indels and large structural variants. The distribution of all these variants provided further independent support to the phylogeny obtained. An unexpected finding was the high level of heteroplasmy found in several of the analysed genomes. The use of the complete chloroplast DNA not only paves the way for a better understanding of the phylogenetic relationships within the Citrus genus, but also provides original insights into other elusive evolutionary processes such as chloroplast inheritance, heteroplasmy and gene selection.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Calzada, David</style></author><author><style face="normal" font="default" size="100%">Aguerri, Miriam</style></author><author><style face="normal" font="default" size="100%">Baos, Selene</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Mata, Manuel</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Quiralte, Joaquín</style></author><author><style face="normal" font="default" size="100%">Florido, Fernando</style></author><author><style face="normal" font="default" size="100%">Lahoz, Carlos</style></author><author><style face="normal" font="default" size="100%">Cárdaba, Blanca</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Therapeutic targets for olive pollen allergy defined by gene markers modulated by Ole e 1-derived peptides.</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular immunology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Apr</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0161589014003356</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">64</style></volume><pages><style face="normal" font="default" size="100%">252-61</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Two regions of Ole e 1, the major olive-pollen allergen, have been characterized as T-cell epitopes, one as immunodominant region (aa91-130) and the other, as mainly recognized by non-allergic subjects (aa10-31). This report tries to characterize the specific relevance of these epitopes in the allergic response to olive pollen by analyzing the secreted cytokines and the gene expression profiles induced after specific stimulation of peripheral blood mononuclear cells (PBMCs). PBMCs from olive pollen-allergic and non-allergic control subjects were stimulated with olive-pollen extract and Ole e 1 dodecapeptides containing relevant T-cell epitopes. Levels of cytokines were measured in cellular supernatants and gene expression was determined by microarrays, on the RNAs extracted from PBMCs. One hundred eighty-nine differential genes (fold change &gt;2 or &lt;-2, P&lt;0.05) were validated by qRT-PCR in a large population. It was not possible to define a pattern of response according the overall cytokine results but interesting differences were observed, mainly in the regulatory cytokines. Principal component (PCA) gene-expression analysis defined clusters that correlated with the experimental conditions in the group of allergic subjects. Gene expression and functional analyses revealed differential genes and pathways among the experimental conditions. A set of 51 genes (many essential to T-cell tolerance and homeostasis) correlated with the response to aa10-31 of Ole e 1. In conclusion, two peptides derived from Ole e 1 could regulate the immune response in allergic patients, by gene-expression modification of several regulation-related genes. These results open new research ways to the regulation of allergy by Oleaceae family members.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Avila-Fernandez, Almudena</style></author><author><style face="normal" font="default" size="100%">Perez-Carro, Raquel</style></author><author><style face="normal" font="default" size="100%">Corton, Marta</style></author><author><style face="normal" font="default" size="100%">Lopez-Molina, Maria Isabel</style></author><author><style face="normal" font="default" size="100%">Campello, Laura</style></author><author><style face="normal" font="default" size="100%">Garanto, Alex</style></author><author><style face="normal" font="default" size="100%">Fernadez-Sanchez, Laura</style></author><author><style face="normal" font="default" size="100%">Duijkers, Lonneke</style></author><author><style face="normal" font="default" size="100%">Lopez-Martinez, Miguel Angel</style></author><author><style face="normal" font="default" size="100%">Riveiro-Alvarez, Rosa</style></author><author><style face="normal" font="default" size="100%">da Silva, Luciana Rodrigues Jacy</style></author><author><style face="normal" font="default" size="100%">Sanchez-Alcudia, Rocío</style></author><author><style face="normal" font="default" size="100%">Martin-Garrido, Esther</style></author><author><style face="normal" font="default" size="100%">Reyes, Noelia</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Garcia-Sandoval, Blanca</style></author><author><style face="normal" font="default" size="100%">Collin, Rob W</style></author><author><style face="normal" font="default" size="100%">Cuenca, Nicolas</style></author><author><style face="normal" font="default" size="100%">Ayuso, Carmen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Whole Exome Sequencing Reveals ZNF408 as a New Gene Associated With Autosomal Recessive Retinitis Pigmentosa with Vitreal Alterations.</style></title><secondary-title><style face="normal" font="default" size="100%">Human molecular genetics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Apr 16</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://hmg.oxfordjournals.org/content/early/2015/04/16/hmg.ddv140.abstract</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">14</style></number><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">4037-4048</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Retinitis Pigmentosa (RP) is a group of progressive inherited retinal dystrophies that cause visual impairment as a result of photoreceptor cell death. RP is heterogeneous, both clinically and genetically making difficult to establish precise genotype-phenotype correlations. In a Spanish family with autosomal recessive RP (arRP), homozygosity mapping and whole exome sequencing led to the identification of a homozygous mutation (c.358_359delGT; p.Ala122Leufs*2) in the ZNF408 gene. A screening performed in 217 additional unrelated families revealed another homozygous mutation (c.1621C&gt;T; p.Arg541Cys) in an isolated RP case. ZNF408 encodes a transcription factor that harbors ten predicted C2H2-type fingers thought to be implicated in DNA binding. To elucidate the ZNF408 role in the retina and the pathogenesis of these mutations we have performed different functional studies. By immunohistochemical analysis in healthy human retina, we identified that ZNF408 is expressed in both cone and rod photoreceptors, in a specific type of amacrine and ganglion cells, and in retinal blood vessels. ZNF408 revealed a cytoplasmic localization and a nuclear distribution in areas corresponding with the euchromatin fraction. Immunolocalization studies showed a partial mislocalization of the p.Arg541Cys mutant protein retaining part of the WT protein in the cytoplasm. Our study demonstrates that ZNF408, previously associated with Familial Exudative Vitreoretinopathy (FEVR), is a new gene causing arRP with vitreous condensations supporting the evidence that this protein plays additional functions into the human retina.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Avila-Fernandez, Almudena</style></author><author><style face="normal" font="default" size="100%">Perez-Carro, Raquel</style></author><author><style face="normal" font="default" size="100%">Corton, Marta</style></author><author><style face="normal" font="default" size="100%">Lopez-Molina, Maria Isabel</style></author><author><style face="normal" font="default" size="100%">Campello, Laura</style></author><author><style face="normal" font="default" size="100%">Garanto, Alejandro</style></author><author><style face="normal" font="default" size="100%">Fernandez-Sanchez, Laura</style></author><author><style face="normal" font="default" size="100%">Duijkers, Lonneke</style></author><author><style face="normal" font="default" size="100%">Lopez-Martinez, Miguel Angel</style></author><author><style face="normal" font="default" size="100%">Riveiro-Alvarez, Rosa</style></author><author><style face="normal" font="default" size="100%">da Silva, Luciana Rodrigues Jacy</style></author><author><style face="normal" font="default" size="100%">Sanchez-Alcudia, Rocío</style></author><author><style face="normal" font="default" size="100%">Martin-Garrido, Esther</style></author><author><style face="normal" font="default" size="100%">Reyes, Noelia</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Garcia-Sandoval, Blanca</style></author><author><style face="normal" font="default" size="100%">Collin, Rob W J</style></author><author><style face="normal" font="default" size="100%">Cuenca, Nicolas</style></author><author><style face="normal" font="default" size="100%">Ayuso, Carmen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Whole-exome sequencing reveals ZNF408 as a new gene associated with autosomal recessive retinitis pigmentosa with vitreal alterations.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Mol Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Mol Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acid Sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Chlorocebus aethiops</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosome Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">COS Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA-Binding Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Homozygote</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Data</style></keyword><keyword><style  face="normal" font="default" size="100%">Mutant Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Pedigree</style></keyword><keyword><style  face="normal" font="default" size="100%">Retina</style></keyword><keyword><style  face="normal" font="default" size="100%">Retinal Cone Photoreceptor Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Retinal Rod Photoreceptor Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Retinitis pigmentosa</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Jul 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">4037-48</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Retinitis pigmentosa (RP) is a group of progressive inherited retinal dystrophies that cause visual impairment as a result of photoreceptor cell death. RP is heterogeneous, both clinically and genetically making difficult to establish precise genotype-phenotype correlations. In a Spanish family with autosomal recessive RP (arRP), homozygosity mapping and whole-exome sequencing led to the identification of a homozygous mutation (c.358_359delGT; p.Ala122Leufs*2) in the ZNF408 gene. A screening performed in 217 additional unrelated families revealed another homozygous mutation (c.1621C&gt;T; p.Arg541Cys) in an isolated RP case. ZNF408 encodes a transcription factor that harbors 10 predicted C2H2-type fingers thought to be implicated in DNA binding. To elucidate the ZNF408 role in the retina and the pathogenesis of these mutations we have performed different functional studies. By immunohistochemical analysis in healthy human retina, we identified that ZNF408 is expressed in both cone and rod photoreceptors, in a specific type of amacrine and ganglion cells, and in retinal blood vessels. ZNF408 revealed a cytoplasmic localization and a nuclear distribution in areas corresponding with the euchromatin fraction. Immunolocalization studies showed a partial mislocalization of the p.Arg541Cys mutant protein retaining part of the WT protein in the cytoplasm. Our study demonstrates that ZNF408, previously associated with Familial Exudative Vitreoretinopathy (FEVR), is a new gene causing arRP with vitreous condensations supporting the evidence that this protein plays additional functions into the human retina. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">14</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/25882705?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Arnau, Vicente</style></author><author><style face="normal" font="default" size="100%">Martinez, Hector</style></author><author><style face="normal" font="default" size="100%">Moreno, Raul</style></author><author><style face="normal" font="default" size="100%">Cazorla, Diego</style></author><author><style face="normal" font="default" size="100%">Salavert-Torres, José</style></author><author><style face="normal" font="default" size="100%">Blanquer-Espert, Ignacio</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Acceleration of short and long DNA read mapping without loss of accuracy using suffix array.</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics (Oxford, England)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">short read mapping. HPC. suffix arrays</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Aug 20</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://bioinformatics.oxfordjournals.org/content/early/2014/08/19/bioinformatics.btu553.long</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">3396-3398</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">HPG Aligner applies suffix arrays for DNA read mapping. This implementation produces a highly sensitive and extremely fast mapping of DNA reads that scales up almost linearly with read length. The approach presented here is faster (over 20x for long reads) and more sensitive (over 98% in a wide range of read lengths) than the current, state-of-the-art mappers. HPG Aligner is not only an optimal alternative for current sequencers but also the only solution available to cope with longer reads and growing throughputs produced by forthcoming sequencing technologies.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Munro, Sarah A</style></author><author><style face="normal" font="default" size="100%">Lund, Steven P</style></author><author><style face="normal" font="default" size="100%">Pine, P Scott</style></author><author><style face="normal" font="default" size="100%">Binder, Hans</style></author><author><style face="normal" font="default" size="100%">Clevert, Djork-Arné</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Fasold, Mario</style></author><author><style face="normal" font="default" size="100%">Hochreiter, Sepp</style></author><author><style face="normal" font="default" size="100%">Hong, Huixiao</style></author><author><style face="normal" font="default" size="100%">Jafari, Nadereh</style></author><author><style face="normal" font="default" size="100%">Kreil, David P</style></author><author><style face="normal" font="default" size="100%">Labaj, Paweł P</style></author><author><style face="normal" font="default" size="100%">Li, Sheng</style></author><author><style face="normal" font="default" size="100%">Liao, Yang</style></author><author><style face="normal" font="default" size="100%">Lin, Simon M</style></author><author><style face="normal" font="default" size="100%">Meehan, Joseph</style></author><author><style face="normal" font="default" size="100%">Mason, Christopher E</style></author><author><style face="normal" font="default" size="100%">Santoyo-López, Javier</style></author><author><style face="normal" font="default" size="100%">Setterquist, Robert A</style></author><author><style face="normal" font="default" size="100%">Shi, Leming</style></author><author><style face="normal" font="default" size="100%">Shi, Wei</style></author><author><style face="normal" font="default" size="100%">Smyth, Gordon K</style></author><author><style face="normal" font="default" size="100%">Stralis-Pavese, Nancy</style></author><author><style face="normal" font="default" size="100%">Su, Zhenqiang</style></author><author><style face="normal" font="default" size="100%">Tong, Weida</style></author><author><style face="normal" font="default" size="100%">Wang, Charles</style></author><author><style face="normal" font="default" size="100%">Wang, Jian</style></author><author><style face="normal" font="default" size="100%">Xu, Joshua</style></author><author><style face="normal" font="default" size="100%">Ye, Zhan</style></author><author><style face="normal" font="default" size="100%">Yang, Yong</style></author><author><style face="normal" font="default" size="100%">Yu, Ying</style></author><author><style face="normal" font="default" size="100%">Salit, Marc</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature communications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">RNA-seq</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/ncomms/2014/140925/ncomms6125/full/ncomms6125.html</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">5125</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard ’dashboard’ of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carmona, F Javier</style></author><author><style face="normal" font="default" size="100%">Davalos, Veronica</style></author><author><style face="normal" font="default" size="100%">Vidal, Enrique</style></author><author><style face="normal" font="default" size="100%">Gomez, Antonio</style></author><author><style face="normal" font="default" size="100%">Heyn, Holger</style></author><author><style face="normal" font="default" size="100%">Hashimoto, Yutaka</style></author><author><style face="normal" font="default" size="100%">Vizoso, Miguel</style></author><author><style face="normal" font="default" size="100%">Martinez-Cardus, Anna</style></author><author><style face="normal" font="default" size="100%">Sayols, Sergi</style></author><author><style face="normal" font="default" size="100%">Ferreira, Humberto</style></author><author><style face="normal" font="default" size="100%">Sanchez-Mut, Jose</style></author><author><style face="normal" font="default" size="100%">Moran, Sebastian</style></author><author><style face="normal" font="default" size="100%">Margeli, Mireia</style></author><author><style face="normal" font="default" size="100%">Castella, Eva</style></author><author><style face="normal" font="default" size="100%">Berdasco, Maria</style></author><author><style face="normal" font="default" size="100%">Stefansson, Olafur Andri</style></author><author><style face="normal" font="default" size="100%">Eyfjord, Jorunn E</style></author><author><style face="normal" font="default" size="100%">Gonzalez-Suarez, Eva</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Orozco, Modesto</style></author><author><style face="normal" font="default" size="100%">Gut, Ivo</style></author><author><style face="normal" font="default" size="100%">Esteller, Manel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comprehensive DNA Methylation Profile of Epithelial-to-Mesenchymal Transition.</style></title><secondary-title><style face="normal" font="default" size="100%">Cancer research</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Methyl-Seq</style></keyword><keyword><style  face="normal" font="default" size="100%">Methylomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Next Generation Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Aug 8</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/25106427</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">19</style></number><volume><style face="normal" font="default" size="100%">74</style></volume><pages><style face="normal" font="default" size="100%">5608–19</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Epithelial-to-mesenchymal transition (EMT) is a plastic process in which fully differentiated epithelial cells are converted into poorly differentiated, migratory and invasive mesenchymal cells and it has been related to the metastasis potential of tumors. This is a reversible process and cells can also eventually undergo mesenchymal-to-epithelial transition (MET). The existence of a dynamic EMT process suggests the involvement of epigenetic shifts in the phenotype. Herein, we obtained the DNA methylomes at single-base resolution of MDCK cells undergoing epithelial-to-mesenchymal transition (EMT) and translated the identified differentially methylated regions (DMRs) to human breast cancer cells undergoing a gain of migratory and invasive capabilities associated with the EMT phenotype. We noticed dynamic and reversible changes of DNA methylation, both on promoter sequences and gene-bodies in association with transcription regulation of EMT-related genes. Most importantly, the identified DNA methylation markers of EMT were present in primary mammary tumors in association with the epithelial or the mesenchymal phenotype of the studied breast cancer samples.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">N. Desoignies</style></author><author><style face="normal" font="default" size="100%">Carbonell, J.</style></author><author><style face="normal" font="default" size="100%">J.-S. Moreau</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">A. Legrève</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Molecular interactions between sugar beet and Polymyxa betae during its life cycle</style></title><secondary-title><style face="normal" font="default" size="100%">Annals of Applied Biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://onlinelibrary.wiley.com/doi/10.1111/aab.12095/abstract</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">164</style></volume><pages><style face="normal" font="default" size="100%">244–256</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Polymyxa betae is a biotrophic obligate sugar beet parasite that belongs to plasmodiophorids. The infection of sugar beet roots by this parasite is asymptomatic, except when it transmits Beet necrotic yellow vein virus (BNYVV), the causal agent of rhizomania. To date, there has been little work on P. betae–sugar beet molecular interactions, mainly because of the obligate nature of the parasite and also because research on rhizomania has tended to focus on the virus. In this study, we investigated these interactions through differential transcript analysis, using suppressive subtractive hybridization. The analysis included 76 P. betae and 120 sugar beet expressed sequence tags (ESTs). The expression of selected ESTs from both organisms was monitored during the protist life cycle, revealing a potential role of two P. betae proteins, profilin and a Von Willebrand factor domain-containing protein, in the early phase of infection. This study also revealed an over-expression of some sugar beet genes involved in defence, such as those encoding PR proteins, stress resistance proteins or lectins, especially during the plasmodial stage of the P. betae life cycle. In addition to providing new information on the molecular aspects of P. betae–sugar beet interactions, this study also enabled previously unknown ESTs of P. betae to be sequenced, thus enhancing our knowledge of the genome of this protist.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tenorio, Jair</style></author><author><style face="normal" font="default" size="100%">Mansilla, Alicia</style></author><author><style face="normal" font="default" size="100%">Valencia, María</style></author><author><style face="normal" font="default" size="100%">Martínez-Glez, Víctor</style></author><author><style face="normal" font="default" size="100%">Romanelli, Valeria</style></author><author><style face="normal" font="default" size="100%">Arias, Pedro</style></author><author><style face="normal" font="default" size="100%">Castrejón, Nerea</style></author><author><style face="normal" font="default" size="100%">Poletta, Fernando</style></author><author><style face="normal" font="default" size="100%">Guillén-Navarro, Encarna</style></author><author><style face="normal" font="default" size="100%">Gordo, Gema</style></author><author><style face="normal" font="default" size="100%">Mansilla, Elena</style></author><author><style face="normal" font="default" size="100%">García-Santiago, Fé</style></author><author><style face="normal" font="default" size="100%">González-Casado, Isabel</style></author><author><style face="normal" font="default" size="100%">Vallespín, Elena</style></author><author><style face="normal" font="default" size="100%">Palomares, María</style></author><author><style face="normal" font="default" size="100%">Mori, María A</style></author><author><style face="normal" font="default" size="100%">Santos-Simarro, Fernando</style></author><author><style face="normal" font="default" size="100%">García-Miñaur, Sixto</style></author><author><style face="normal" font="default" size="100%">Fernández, Luis</style></author><author><style face="normal" font="default" size="100%">Mena, Rocío</style></author><author><style face="normal" font="default" size="100%">Benito-Sanz, Sara</style></author><author><style face="normal" font="default" size="100%">Del Pozo, Angela</style></author><author><style face="normal" font="default" size="100%">Silla, Juan Carlos</style></author><author><style face="normal" font="default" size="100%">Ibañez, Kristina</style></author><author><style face="normal" font="default" size="100%">López-Granados, Eduardo</style></author><author><style face="normal" font="default" size="100%">Martín-Trujillo, Alex</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Heath, Karen E</style></author><author><style face="normal" font="default" size="100%">Campos-Barros, Angel</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Nevado, Julián</style></author><author><style face="normal" font="default" size="100%">Monk, David</style></author><author><style face="normal" font="default" size="100%">Ruiz-Pérez, Víctor L</style></author><author><style face="normal" font="default" size="100%">Lapunzina, Pablo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A New Overgrowth Syndrome is Due to Mutations in RNF125.</style></title><secondary-title><style face="normal" font="default" size="100%">Human mutation</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">prioritization</style></keyword><keyword><style  face="normal" font="default" size="100%">Rare Disease</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Sep 5</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://onlinelibrary.wiley.com/doi/10.1002/humu.22689/abstract</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">1436–1441</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Overgrowth syndromes (OGS) are a group of disorders in which all parameters of growth and physical development are above the mean for age and sex. We evaluated a series of 270 families from the Spanish Overgrowth Syndrome Registry with no known overgrowth syndrome. We identified one de novo deletion and three missense mutations in RNF125 in six patients from 4 families with overgrowth, macrocephaly, intellectual disability, mild hydrocephaly, hypoglycaemia and inflammatory diseases resembling Sjögren syndrome. RNF125 encodes an E3 ubiquitin ligase and is a novel gene of OGS. Our studies of the RNF125 pathway point to upregulation of RIG-I-IPS1-MDA5 and/or disruption of the PI3K-AKT and interferon signaling pathways as the putative final effectors. This article is protected by copyright. All rights reserved.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Calpena, E</style></author><author><style face="normal" font="default" size="100%">Martínez-Rubio, D</style></author><author><style face="normal" font="default" size="100%">Arpa, J</style></author><author><style face="normal" font="default" size="100%">García-Peñas, J J</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Palau, F</style></author><author><style face="normal" font="default" size="100%">Espinós, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A novel locus for a hereditary recurrent neuropathy on chromosome 21q21.</style></title><secondary-title><style face="normal" font="default" size="100%">Neuromuscular disorders : NMD</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 May 9</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0960896614001060#</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">8</style></number><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">660-5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Hereditary recurrent neuropathies are uncommon. Disorders with a known molecular basis falling within this group include hereditary neuropathy with liability to pressure palsies (HNPP) due to the deletion of the PMP22 gene or to mutations in this same gene, and hereditary neuralgic amyotrophy (HNA) caused by mutations in the SEPT9 gene. We report a three-generation family presenting a hereditary recurrent neuropathy without pathological changes in either PMP22 or SEPT9 genes. We performed a genome-wide mapping, which yielded a locus of 12.4Mb on chromosome 21q21. The constructed haplotype fully segregated with the disease and we found significant evidence of linkage. After mutational screening of genes located within this locus, encoding for proteins and microRNAs, as well as analysis of large deletions/insertions, we identified 71 benign polymorphisms. Our findings suggest a novel genetic locus for a recurrent hereditary neuropathy of which the molecular defect remains elusive. Our results further underscore the clinical and genetic heterogeneity of this group of neuropathies.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ponzoni, Ignacio</style></author><author><style face="normal" font="default" size="100%">Nueda, María</style></author><author><style face="normal" font="default" size="100%">Tarazona, Sonia</style></author><author><style face="normal" font="default" size="100%">Götz, Stefan</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Dussaut, Julieta</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pathway network inference from gene expression data.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Syst Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Syst Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alzheimer Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Cycle</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Replication</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Gluconeogenesis</style></keyword><keyword><style  face="normal" font="default" size="100%">Glycolysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Oxidative Phosphorylation</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteolysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Purines</style></keyword><keyword><style  face="normal" font="default" size="100%">Saccharomyces cerevisiae</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Ubiquitin</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8 Suppl 2</style></volume><pages><style face="normal" font="default" size="100%">S7</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/25032889?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Torre, Iratxe</style></author><author><style face="normal" font="default" size="100%">González-Tendero, Anna</style></author><author><style face="normal" font="default" size="100%">García-Cañadilla, Patricia</style></author><author><style face="normal" font="default" size="100%">Crispi, Fátima</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Bijnens, Bart</style></author><author><style face="normal" font="default" size="100%">Iruretagoyena, Igor</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Amat-Roldán, Ivan</style></author><author><style face="normal" font="default" size="100%">Gratacós, Eduard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Permanent cardiac sarcomere changes in a rabbit model of intrauterine growth restriction.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">biomarkers</style></keyword><keyword><style  face="normal" font="default" size="100%">Blood Pressure</style></keyword><keyword><style  face="normal" font="default" size="100%">Body Weight</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease Models, Animal</style></keyword><keyword><style  face="normal" font="default" size="100%">Echocardiography</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Fetal Growth Retardation</style></keyword><keyword><style  face="normal" font="default" size="100%">Fetal Heart</style></keyword><keyword><style  face="normal" font="default" size="100%">Fetus</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Organ Size</style></keyword><keyword><style  face="normal" font="default" size="100%">Placenta</style></keyword><keyword><style  face="normal" font="default" size="100%">Pregnancy</style></keyword><keyword><style  face="normal" font="default" size="100%">Rabbits</style></keyword><keyword><style  face="normal" font="default" size="100%">Sarcomeres</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">e113067</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Intrauterine growth restriction (IUGR) induces fetal cardiac remodelling and dysfunction, which persists postnatally and may explain the link between low birth weight and increased cardiovascular mortality in adulthood. However, the cellular and molecular bases for these changes are still not well understood. We tested the hypothesis that IUGR is associated with structural and functional gene expression changes in the fetal sarcomere cytoarchitecture, which remain present in adulthood.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS AND RESULTS: &lt;/b&gt;IUGR was induced in New Zealand pregnant rabbits by selective ligation of the utero-placental vessels. Fetal echocardiography demonstrated more globular hearts and signs of cardiac dysfunction in IUGR. Second harmonic generation microscopy (SHGM) showed shorter sarcomere length and shorter A-band and thick-thin filament interaction lengths, that were already present in utero and persisted at 70 postnatal days (adulthood). Sarcomeric M-band (GO: 0031430) functional term was over-represented in IUGR fetal hearts.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;The results suggest that IUGR induces cardiac dysfunction and permanent changes on the sarcomere.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/25402351?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Jiménez-Almazán, Jorge</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Vela-Boza, Alicia</style></author><author><style face="normal" font="default" size="100%">Santoyo-López, Javier</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The role of the interactome in the maintenance of deleterious variability in human populations.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Syst Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Syst Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alleles</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Library</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetics, Population</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Conformation</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Maps</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Whites</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Sep 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">752</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Recent genomic projects have revealed the existence of an unexpectedly large amount of deleterious variability in the human genome. Several hypotheses have been proposed to explain such an apparently high mutational load. However, the mechanisms by which deleterious mutations in some genes cause a pathological effect but are apparently innocuous in other genes remain largely unknown. This study searched for deleterious variants in the 1,000 genomes populations, as well as in a newly sequenced population of 252 healthy Spanish individuals. In addition, variants causative of monogenic diseases and somatic variants from 41 chronic lymphocytic leukaemia patients were analysed. The deleterious variants found were analysed in the context of the interactome to understand the role of network topology in the maintenance of the observed mutational load. Our results suggest that one of the mechanisms whereby the effect of these deleterious variants on the phenotype is suppressed could be related to the configuration of the protein interaction network. Most of the deleterious variants observed in healthy individuals are concentrated in peripheral regions of the interactome, in combinations that preserve their connectivity, and have a marginal effect on interactome integrity. On the contrary, likely pathogenic cancer somatic deleterious variants tend to occur in internal regions of the interactome, often with associated structural consequences. Finally, variants causative of monogenic diseases seem to occupy an intermediate position. Our observations suggest that the real pathological potential of a variant might be more a systems property rather than an intrinsic property of individual proteins. &lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/25261458?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Larriba, Eduardo</style></author><author><style face="normal" font="default" size="100%">Jaime, María D L A</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Nislow, Corey</style></author><author><style face="normal" font="default" size="100%">Martín-Nieto, José</style></author><author><style face="normal" font="default" size="100%">Lopez-Llorca, Luis Vicente</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sequencing and functional analysis of the genome of a nematode egg-parasitic fungus, Pochonia chlamydosporia.</style></title><secondary-title><style face="normal" font="default" size="100%">Fungal Genet Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Fungal Genet Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Ascomycota</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Fungal</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Fungal</style></keyword><keyword><style  face="normal" font="default" size="100%">Hordeum</style></keyword><keyword><style  face="normal" font="default" size="100%">Host-Pathogen Interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">Nematoda</style></keyword><keyword><style  face="normal" font="default" size="100%">Ovum</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Roots</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">65</style></volume><pages><style face="normal" font="default" size="100%">69-80</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Pochonia chlamydosporia is a worldwide-distributed soil fungus with a great capacity to infect and destroy the eggs and kill females of plant-parasitic nematodes. Additionally, it has the ability to colonize endophytically roots of economically-important crop plants, thereby promoting their growth and eliciting plant defenses. This multitrophic behavior makes P. chlamydosporia a potentially useful tool for sustainable agriculture approaches. We sequenced and assembled ∼41 Mb of P. chlamydosporia genomic DNA and predicted 12,122 gene models, of which many were homologous to genes of fungal pathogens of invertebrates and fungal plant pathogens. Predicted genes (65%) were functionally annotated according to Gene Ontology, and 16% of them found to share homology with genes in the Pathogen Host Interactions (PHI) database. The genome of this fungus is highly enriched in genes encoding hydrolytic enzymes, such as proteases, glycoside hydrolases and carbohydrate esterases. We used RNA-Seq technology in order to identify the genes expressed during endophytic behavior of P. chlamydosporia when colonizing barley roots. Functional annotation of these genes showed that hydrolytic enzymes and transporters are expressed during endophytism. This structural and functional analysis of the P. chlamydosporia genome provides a starting point for understanding the molecular mechanisms involved in the multitrophic lifestyle of this fungus. The genomic information provided here should also prove useful for enhancing the capabilities of this fungus as a biocontrol agent of plant-parasitic nematodes and as a plant growth-promoting organism.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/24530791?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">García-Cazorla, Angels</style></author><author><style face="normal" font="default" size="100%">Oyarzabal, Alfonso</style></author><author><style face="normal" font="default" size="100%">Fort, Joana</style></author><author><style face="normal" font="default" size="100%">Robles, Concepción</style></author><author><style face="normal" font="default" size="100%">Castejón, Esperanza</style></author><author><style face="normal" font="default" size="100%">Ruiz-Sala, Pedro</style></author><author><style face="normal" font="default" size="100%">Bodoy, Susanna</style></author><author><style face="normal" font="default" size="100%">Merinero, Begoña</style></author><author><style face="normal" font="default" size="100%">Lopez-Sala, Anna</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Nunes, Virginia</style></author><author><style face="normal" font="default" size="100%">Ugarte, Magdalena</style></author><author><style face="normal" font="default" size="100%">Artuch, Rafael</style></author><author><style face="normal" font="default" size="100%">Palacín, Manuel</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Pombo, Pilar</style></author><author><style face="normal" font="default" size="100%">Alcaide, Patricia</style></author><author><style face="normal" font="default" size="100%">Navarrete, Rosa</style></author><author><style face="normal" font="default" size="100%">Sanz, Paloma</style></author><author><style face="normal" font="default" size="100%">Font-Llitjós, Mariona</style></author><author><style face="normal" font="default" size="100%">Vilaseca, Ma Antonia</style></author><author><style face="normal" font="default" size="100%">Ormaizabal, Aida</style></author><author><style face="normal" font="default" size="100%">Pristoupilova, Anna</style></author><author><style face="normal" font="default" size="100%">Agulló, Sergi Beltran</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Two novel mutations in the BCKDK (branched-chain keto-acid dehydrogenase kinase) gene are responsible for a neurobehavioral deficit in two pediatric unrelated patients.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Mutat</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Mutat</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acids, Branched-Chain</style></keyword><keyword><style  face="normal" font="default" size="100%">Developmental Disabilities</style></keyword><keyword><style  face="normal" font="default" size="100%">Fibroblasts</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mutation, Missense</style></keyword><keyword><style  face="normal" font="default" size="100%">Nervous System Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Pediatrics</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Kinases</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">470-7</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Inactivating mutations in the BCKDK gene, which codes for the kinase responsible for the negative regulation of the branched-chain α-keto acid dehydrogenase complex (BCKD), have recently been associated with a form of autism in three families. In this work, two novel exonic BCKDK mutations, c.520C&gt;G/p.R174G and c.1166T&gt;C/p.L389P, were identified at the homozygous state in two unrelated children with persistently reduced body fluid levels of branched-chain amino acids (BCAAs), developmental delay, microcephaly, and neurobehavioral abnormalities. Functional analysis of the mutations confirmed the missense character of the c.1166T&gt;C change and showed a splicing defect r.[520c&gt;g;521_543del]/p.R174Gfs1*, for c.520C&gt;G due to the presence of a new donor splice site. Mutation p.L389P showed total loss of kinase activity. Moreover, patient-derived fibroblasts showed undetectable (p.R174Gfs1*) or barely detectable (p.L389P) levels of BCKDK protein and its phosphorylated substrate (phospho-E1α), resulting in increased BCKD activity and the very rapid BCAA catabolism manifested by the patients' clinical phenotype. Based on these results, a protein-rich diet plus oral BCAA supplementation was implemented in the patient homozygous for p.R174Gfs1*. This treatment normalized plasma BCAA levels and improved growth, developmental and behavioral variables. Our results demonstrate that BCKDK mutations can result in neurobehavioral deficits in humans and support the rationale for dietary intervention. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/24449431?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">García-Cazorla, Angels</style></author><author><style face="normal" font="default" size="100%">Oyarzabal, Alfonso</style></author><author><style face="normal" font="default" size="100%">Fort, Joana</style></author><author><style face="normal" font="default" size="100%">Robles, Concepción</style></author><author><style face="normal" font="default" size="100%">Castejón, Esperanza</style></author><author><style face="normal" font="default" size="100%">Ruiz-Sala, Pedro</style></author><author><style face="normal" font="default" size="100%">Bodoy, Susanna</style></author><author><style face="normal" font="default" size="100%">Merinero, Begoña</style></author><author><style face="normal" font="default" size="100%">Lopez-Sala, Anna</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Nunes, Virginia</style></author><author><style face="normal" font="default" size="100%">Ugarte, Magdalena</style></author><author><style face="normal" font="default" size="100%">Artuch, Rafael</style></author><author><style face="normal" font="default" size="100%">Palacín, Manuel</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Pombo, Pilar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Two Novel Mutations in the BCKDK Gene (Branched-Chain Keto-Acid Dehydrogenase Kinase) are Responsible of a Neurobehavioral Deficit in two Pediatric Unrelated Patients.</style></title><secondary-title><style face="normal" font="default" size="100%">Human mutation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Jan 21</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://onlinelibrary.wiley.com/doi/10.1002/humu.22513/abstract</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">470-7</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Inactivating mutations in the BCKDK gene, which codes for the kinase responsible for the negative regulation of the branched-chain keto-acid dehydrogenase complex (BCKD), have recently been associated with a form of autism in three families. In this work, two novel exonic BCKDK mutations, c.520C&gt;G/p.R174G and c.1166T&gt;C/p.L389P, were identified at the homozygous state in two unrelated children with persistently reduced body fluid levels of branched-chain amino acids (BCAAs), developmental delay, microcephaly and neurobehavioral abnormalities. Functional analysis of the mutations confirmed the missense character of the c.1166T&gt;C change and showed a splicing defect r.[520c&gt;g;521_543del]/p.R174Gfs1*, for c.520C&gt;G due to the presence of a new donor splice site. Mutation p.L389P showed total loss of kinase activity. Moreover, patient-derived fibroblasts showed undetectable (p.R174Gfs1*) or barely detectable (p.L389P) levels of BCKDK protein and its phosphorylated substrate (phospho-E1α), resulting in increased BCKD activity and the very rapid BCAA catabolism manifested by the patients’ clinical phenotype. Based on these results, a protein-rich diet plus oral BCAA supplementation was implemented in the patient homozygous for p.R174Gfs1*. This treatment normalized plasma BCAA levels and improved growth, developmental and behavioral variables. Our results demonstrate that BCKDK mutations can result in neurobehavioral deficits in humans and support the rationale for dietary intervention. This article is protected by copyright. All rights reserved.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sebastián-Leon, Patricia</style></author><author><style face="normal" font="default" size="100%">Vidal, Enrique</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Sonia Tarazona</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Armero, Carmen</style></author><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">Vidal-Puig, Antonio</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Understanding disease mechanisms with models of signaling pathway activities.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC systems biology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Disease mechanism</style></keyword><keyword><style  face="normal" font="default" size="100%">pathway</style></keyword><keyword><style  face="normal" font="default" size="100%">signalling</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Oct 25</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.biomedcentral.com/1752-0509/8/121/abstract</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">121</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BackgroundUnderstanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine.ResultsHere we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets.ConclusionsThe proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sebastián-Leon, Patricia</style></author><author><style face="normal" font="default" size="100%">Vidal, Enrique</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author><author><style face="normal" font="default" size="100%">Tarazona, Sonia</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Armero, Carmen</style></author><author><style face="normal" font="default" size="100%">Salavert Torres, Francisco</style></author><author><style face="normal" font="default" size="100%">Vidal-Puig, Antonio</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Understanding disease mechanisms with models of signaling pathway activities</style></title><secondary-title><style face="normal" font="default" size="100%">BMC systems biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">121</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ariño, Joaquín</style></author><author><style face="normal" font="default" size="100%">Casamayor, Antonio</style></author><author><style face="normal" font="default" size="100%">Pérez, Julián Perez</style></author><author><style face="normal" font="default" size="100%">Pedrola, Laia</style></author><author><style face="normal" font="default" size="100%">Alvarez-Tejado, Miguel</style></author><author><style face="normal" font="default" size="100%">Marbà, Martina</style></author><author><style face="normal" font="default" size="100%">Santoyo, Javier</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessing Differential Expression Measurements by Highly Parallel Pyrosequencing and DNA Microarrays: A Comparative Study.</style></title><secondary-title><style face="normal" font="default" size="100%">Omics : a journal of integrative biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Sep 15</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3545353/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Abstract To explore the feasibility of pyrosequencing for quantitative differential gene expression analysis we have performed a comparative study of the results of the sequencing experiments to those obtained by a conventional DNA microarray platform. A conclusion from our analysis is that, over a threshold of 35 normalized reads per gene, the measurements of gene expression display a good correlation with the references. The observed concordance between pyrosequencing and DNA microarray platforms beyond the threshold was of 0.8, measured as a Pearson’s correlation coefficient. In differential gene expression the initial aim is the quantification the differences among transcripts when comparing experimental conditions. Thus, even in a scenario of low coverage the concordance in the measurements is quite acceptable. On the other hand, the comparatively longer read size obtained by pyrosequencing allows detecting unconventional splicing forms.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Galan, Amparo</style></author><author><style face="normal" font="default" size="100%">Diaz-Gimeno, Patricia</style></author><author><style face="normal" font="default" size="100%">Poo, Maria Eugenia</style></author><author><style face="normal" font="default" size="100%">Valbuena, Diana</style></author><author><style face="normal" font="default" size="100%">Sanchez, Eva</style></author><author><style face="normal" font="default" size="100%">Ruiz, Veronica</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author><author><style face="normal" font="default" size="100%">Simon, Carlos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Defining the genomic signature of totipotency and pluripotency during early human development.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Blastocyst Inner Cell Mass</style></keyword><keyword><style  face="normal" font="default" size="100%">Blastomeres</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Differentiation</style></keyword><keyword><style  face="normal" font="default" size="100%">Embryonic Development</style></keyword><keyword><style  face="normal" font="default" size="100%">Embryonic Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Annotation</style></keyword><keyword><style  face="normal" font="default" size="100%">Pluripotent Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Totipotent Stem Cells</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">e62135</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The genetic mechanisms governing human pre-implantation embryo development and the in vitro counterparts, human embryonic stem cells (hESCs), still remain incomplete. Previous global genome studies demonstrated that totipotent blastomeres from day-3 human embryos and pluripotent inner cell masses (ICMs) from blastocysts, display unique and differing transcriptomes. Nevertheless, comparative gene expression analysis has revealed that no significant differences exist between hESCs derived from blastomeres versus those obtained from ICMs, suggesting that pluripotent hESCs involve a new developmental progression. To understand early human stages evolution, we developed an undifferentiation network signature (UNS) and applied it to a differential gene expression profile between single blastomeres from day-3 embryos, ICMs and hESCs. This allowed us to establish a unique signature composed of highly interconnected genes characteristic of totipotency (61 genes), in vivo pluripotency (20 genes), and in vitro pluripotency (107 genes), and which are also proprietary according to functional analysis. This systems biology approach has led to an improved understanding of the molecular and signaling processes governing human pre-implantation embryo development, as well as enabling us to comprehend how hESCs might adapt to in vitro culture conditions.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/23614026?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aguerri, M</style></author><author><style face="normal" font="default" size="100%">Calzada, D</style></author><author><style face="normal" font="default" size="100%">Montaner, D</style></author><author><style face="normal" font="default" size="100%">Mata, M</style></author><author><style face="normal" font="default" size="100%">Florido, F</style></author><author><style face="normal" font="default" size="100%">Quiralte, J</style></author><author><style face="normal" font="default" size="100%">Dopazo, J</style></author><author><style face="normal" font="default" size="100%">Lahoz, C</style></author><author><style face="normal" font="default" size="100%">Cardaba, B</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Differential gene-expression analysis defines a molecular pattern related to olive pollen allergy.</style></title><secondary-title><style face="normal" font="default" size="100%">J Biol Regul Homeost Agents</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Biol Regul Homeost Agents</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Olea</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal Component Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Rhinitis, Allergic, Seasonal</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Apr-Jun</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">337-50</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Analysis of gene-expression profiles by microarrays is useful for characterization of candidate genes, key regulatory networks, and to define phenotypes or molecular signatures which improve the diagnosis and/or classification of the allergic processes. We have used this approach in the study of olive pollen response in order to find differential molecular markers among responders and non-responders to this allergenic source. Five clinical groups, non-allergic, asymptomatic, allergic but not to olive pollen, untreated-olive-pollen allergic patients and olive-pollen allergic patients (under specific-immunotherapy), were assessed during and outside pollen seasons. Whole-genome gene expression analysis was performed in RNAs extracted from PBMCs. After assessment of data quality and principal components analysis (PCA), differential gene-expression, by multiple testing and, functional analyses by KEGG, for pathways and Gene-Ontology for biological processes were performed. Relevance was defined by fold change and corrected P values (less than 0.05). The most differential genes were validated by qRT-PCR in a larger set of individuals. Interestingly, gene-expression profiling obtained by PCA clearly showed five clusters of samples that correlated with the five clinical groups. Furthermore, differential gene expression and functional analyses revealed differential genes and pathways in the five clinical groups. The 93 most significant genes found were validated, and one set of 35 genes was able to discriminate profiles of olive pollen response. Our results, in addition to providing new information on allergic response, define a possible molecular signature for olive pollen allergy which could be useful for the diagnosis and treatment of this and other sensitizations. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/23830385?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tort, Frederic</style></author><author><style face="normal" font="default" size="100%">García-Silva, María Teresa</style></author><author><style face="normal" font="default" size="100%">Ferrer-Cortès, Xènia</style></author><author><style face="normal" font="default" size="100%">Navarro-Sastre, Aleix</style></author><author><style face="normal" font="default" size="100%">Garcia-Villoria, Judith</style></author><author><style face="normal" font="default" size="100%">Coll, Maria Josep</style></author><author><style face="normal" font="default" size="100%">Vidal, Enrique</style></author><author><style face="normal" font="default" size="100%">Jiménez-Almazán, Jorge</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Briones, Paz</style></author><author><style face="normal" font="default" size="100%">Elpeleg, Orly</style></author><author><style face="normal" font="default" size="100%">Ribes, Antonia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exome sequencing identifies a new mutation in SERAC1 in a patient with 3-methylglutaconic aciduria.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Genet Metab</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Genet Metab</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Carboxylic Ester Hydrolases</style></keyword><keyword><style  face="normal" font="default" size="100%">Child</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Infant</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolism, Inborn Errors</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Sep-Oct</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">110</style></volume><pages><style face="normal" font="default" size="100%">73-7</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;3-Methylglutaconic aciduria (3-MGA-uria) is a heterogeneous group of syndromes characterized by an increased excretion of 3-methylglutaconic and 3-methylglutaric acids. Five types of 3-MGA-uria (I to V) with different clinical presentations have been described. Causative mutations in TAZ, OPA3, DNAJC19, ATP12, ATP5E, and TMEM70 have been identified. After excluding the known genetic causes of 3-MGA-uria we used exome sequencing to investigate a patient with Leigh syndrome and 3-MGA-uria. We identified a homozygous variant in SERAC1 (c.202C&gt;T; p.Arg68*), that generates a premature stop codon at position 68 of SERAC1 protein. Western blot analysis in patient's fibroblasts showed a complete absence of SERAC1 that was consistent with the prediction of a truncated protein and supports the pathogenic role of the mutation. During the course of this project a parallel study identified mutations in SERAC1 as the genetic cause of the disease in 15 patients with MEGDEL syndrome, which was compatible with the clinical and biochemical phenotypes of the patient described here. In addition, our patient developed microcephaly and optic atrophy, two features not previously reported in MEGDEL syndrome. We highlight the usefulness of exome sequencing to reveal the genetic bases of human rare diseases even if only one affected individual is available. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1-2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/23707711?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sánchez-Tena, Susana</style></author><author><style face="normal" font="default" size="100%">Lizarraga, Daneida</style></author><author><style face="normal" font="default" size="100%">Miranda, Anibal</style></author><author><style face="normal" font="default" size="100%">Vinardell, Maria Pilar</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Torres, Josep Lluís</style></author><author><style face="normal" font="default" size="100%">Saura-Calixto, Fulgencio</style></author><author><style face="normal" font="default" size="100%">Capellà, Gabriel</style></author><author><style face="normal" font="default" size="100%">Cascante, Marta</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Grape antioxidant dietary fiber (GADF) inhibits intestinal polyposis in ApcMin/+ mice: relation to cell cycle and immune response.</style></title><secondary-title><style face="normal" font="default" size="100%">Carcinogenesis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Apr 24</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://carcin.oxfordjournals.org/content/early/2013/04/23/carcin.bgt140.abstract</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Epidemiological and experimental studies suggest that fiber and phenolic compounds might have a protective effect on the development of colon cancer in humans. Accordingly, we assessed the chemopreventive efficacy and associated mechanisms of action of a lyophilized red grape pomace containing proanthocyanidin-rich dietary fiber (Grape Antioxidant Dietary Fiber, GADF) on spontaneous intestinal tumorigenesis in the Apc(Min/+) mouse model. Mice were fed a standard diet (control group) or a 1% (w/w) GADF-supplemented diet (GADF group) for 6 weeks. GADF supplementation greatly reduced intestinal tumorigenesis, significantly decreasing the total number of polyps by 76%. Moreover, size distribution analysis showed a considerable reduction in all polyp size categories [diameter &lt;1 mm (65%), 1-2 mm (67%) and &gt;2 mm (87%)]. In terms of polyp formation in the proximal, middle and distal portions of the small intestine a decrease of 76%, 81% and 73% was observed respectively. Putative molecular mechanisms underlying the inhibition of intestinal tumorigenesis were investigated by comparison of microarray expression profiles of GADF-treated and non-treated mice. We observed that the effects of GADF are mainly associated with the induction of a G1 cell cycle arrest and the downregulation of genes related to the immune response and inflammation. Our findings show for the first time the efficacy and associated mechanisms of action of GADF against intestinal tumorigenesis in Apc(Min/+) mice, suggesting its potential for the prevention of colorectal cancer.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sánchez-Tena, Susana</style></author><author><style face="normal" font="default" size="100%">Lizarraga, Daneida</style></author><author><style face="normal" font="default" size="100%">Miranda, Anibal</style></author><author><style face="normal" font="default" size="100%">Vinardell, Maria P</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Torres, Josep L</style></author><author><style face="normal" font="default" size="100%">Saura-Calixto, Fulgencio</style></author><author><style face="normal" font="default" size="100%">Capellà, Gabriel</style></author><author><style face="normal" font="default" size="100%">Cascante, Marta</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Grape antioxidant dietary fiber inhibits intestinal polyposis in ApcMin/+ mice: relation to cell cycle and immune response.</style></title><secondary-title><style face="normal" font="default" size="100%">Carcinogenesis</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Carcinogenesis</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Antioxidants</style></keyword><keyword><style  face="normal" font="default" size="100%">Body Weight</style></keyword><keyword><style  face="normal" font="default" size="100%">Carcinogenesis</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Cycle</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Cycle Checkpoints</style></keyword><keyword><style  face="normal" font="default" size="100%">Colorectal Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Dietary Fiber</style></keyword><keyword><style  face="normal" font="default" size="100%">Dietary Supplements</style></keyword><keyword><style  face="normal" font="default" size="100%">Down-Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">G1 Phase</style></keyword><keyword><style  face="normal" font="default" size="100%">Inflammation</style></keyword><keyword><style  face="normal" font="default" size="100%">Intestinal Polyposis</style></keyword><keyword><style  face="normal" font="default" size="100%">Intestinal Polyps</style></keyword><keyword><style  face="normal" font="default" size="100%">Intestine, Small</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">Vitis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Aug</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">1881-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Epidemiological and experimental studies suggest that fiber and phenolic compounds might have a protective effect on the development of colon cancer in humans. Accordingly, we assessed the chemopreventive efficacy and associated mechanisms of action of a lyophilized red grape pomace containing proanthocyanidin (PA)-rich dietary fiber [grape antioxidant dietary fiber (GADF)] on spontaneous intestinal tumorigenesis in the Apc(Min/+) mouse model. Mice were fed a standard diet (control group) or a 1% (w/w) GADF-supplemented diet (GADF group) for 6 weeks. GADF supplementation greatly reduced intestinal tumorigenesis, significantly decreasing the total number of polyps by 76%. Moreover, size distribution analysis showed a considerable reduction in all polyp size categories [diameter &lt;1mm (65%), 1-2mm (67%) and &gt;2mm (87%)]. In terms of polyp formation in the proximal, middle and distal portions of the small intestine, a decrease of 76, 81 and 73% was observed, respectively. Putative molecular mechanisms underlying the inhibition of intestinal tumorigenesis were investigated by comparison of microarray expression profiles of GADF-treated and non-treated mice. We observed that the effects of GADF are mainly associated with the induction of a G1 cell cycle arrest and the downregulation of genes related to the immune response and inflammation. Our findings show for the first time the efficacy and associated mechanisms of action of GADF against intestinal tumorigenesis in Apc(Min/+) mice, suggesting its potential for the prevention of colorectal cancer. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/23615403?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sebastián-Leon, Patricia</style></author><author><style face="normal" font="default" size="100%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">Sánchez, Rubén</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inferring the functional effect of gene expression changes in signaling pathways.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Statistical</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptors, Cell Surface</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">41</style></volume><pages><style face="normal" font="default" size="100%">W213-7</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Signaling pathways constitute a valuable source of information that allows interpreting the way in which alterations in gene activities affect to particular cell functionalities. There are web tools available that allow viewing and editing pathways, as well as representing experimental data on them. However, few methods aimed to identify the signaling circuits, within a pathway, associated to the biological problem studied exist and none of them provide a convenient graphical web interface. We present PATHiWAYS, a web-based signaling pathway visualization system that infers changes in signaling that affect cell functionality from the measurements of gene expression values in typical expression microarray case-control experiments. A simple probabilistic model of the pathway is used to estimate the probabilities for signal transmission from any receptor to any final effector molecule (taking into account the pathway topology) using for this the individual probabilities of gene product presence/absence inferred from gene expression values. Significant changes in these probabilities allow linking different cell functionalities triggered by the pathway to the biological problem studied. PATHiWAYS is available at: http://pathiways.babelomics.org/. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Web Server issue</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/23748960?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">González-Tendero, Anna</style></author><author><style face="normal" font="default" size="100%">Torre, Iratxe</style></author><author><style face="normal" font="default" size="100%">García-Cañadilla, Patricia</style></author><author><style face="normal" font="default" size="100%">Crispi, Fátima</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Bijnens, Bart</style></author><author><style face="normal" font="default" size="100%">Gratacós, Eduard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Intrauterine growth restriction is associated with cardiac ultrastructural and gene expression changes related to the energetic metabolism in a rabbit model.</style></title><secondary-title><style face="normal" font="default" size="100%">Am J Physiol Heart Circ Physiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Am J Physiol Heart Circ Physiol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease Models, Animal</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy Metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Fetal Growth Retardation</style></keyword><keyword><style  face="normal" font="default" size="100%">gene expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Mitochondria</style></keyword><keyword><style  face="normal" font="default" size="100%">Myocardium</style></keyword><keyword><style  face="normal" font="default" size="100%">Oxidative Phosphorylation</style></keyword><keyword><style  face="normal" font="default" size="100%">Placenta</style></keyword><keyword><style  face="normal" font="default" size="100%">Pregnancy</style></keyword><keyword><style  face="normal" font="default" size="100%">Rabbits</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Dec</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">305</style></volume><pages><style face="normal" font="default" size="100%">H1752-60</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Intrauterine growth restriction (IUGR) affects 7-10% of pregnancies and is associated with cardiovascular remodeling and dysfunction, which persists into adulthood. The underlying subcellular remodeling and cardiovascular programming events are still poorly documented. Cardiac muscle is central in the fetal adaptive mechanism to IUGR given its high energetic demands. The energetic homeostasis depends on the correct interaction of several molecular pathways and the adequate arrangement of intracellular energetic units (ICEUs), where mitochondria interact with the contractile machinery and the main cardiac ATPases to enable a quick and efficient energy transfer. We studied subcellular cardiac adaptations to IUGR in an experimental rabbit model. We evaluated the ultrastructure of ICEUs with transmission electron microscopy and observed an altered spatial arrangement in IUGR, with significant increases in cytosolic space between mitochondria and myofilaments. A global decrease of mitochondrial density was also observed. In addition, we conducted a global gene expression profile by advanced bioinformatics tools to assess the expression of genes involved in the cardiomyocyte energetic metabolism and identified four gene modules with a coordinated over-representation in IUGR: oxygen homeostasis (GO: 0032364), mitochondrial respiratory chain complex I (GO:0005747), oxidative phosphorylation (GO: 0006119), and NADH dehydrogenase activity (GO:0003954). These findings might contribute to changes in energetic homeostasis in IUGR. The potential persistence and role of these changes in long-term cardiovascular programming deserves further investigation. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/24097427?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Manuel Iglesias, Juan</style></author><author><style face="normal" font="default" size="100%">Beloqui, Izaskun</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Leis, Olatz</style></author><author><style face="normal" font="default" size="100%">Vazquez-Martin, Alejandro</style></author><author><style face="normal" font="default" size="100%">Eguiara, Arrate</style></author><author><style face="normal" font="default" size="100%">Cufi, Silvia</style></author><author><style face="normal" font="default" size="100%">Pavon, Andres</style></author><author><style face="normal" font="default" size="100%">Menendez, Javier A.</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Martin, Angel G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mammosphere Formation in Breast Carcinoma Cell Lines Depends upon Expression of E-cadherin</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS ONE</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013/10/04</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1371%2Fjournal.pone.0077281</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Public Library of Science</style></publisher><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">e77281 -</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Tumors are heterogeneous at the cellular level where the ability to maintain tumor growth resides in discrete cell populations. Floating sphere-forming assays are broadly used to test stem cell activity in tissues, tumors and cell lines. Spheroids are originated from a small population of cells with stem cell features able to grow in suspension culture and behaving as tumorigenic in mice. We tested the ability of eleven common breast cancer cell lines representing the major breast cancer subtypes to grow as mammospheres, measuring the ability to maintain cell viability upon serial non-adherent passage. Only MCF7, T47D, BT474, MDA-MB-436 and JIMT1 were successfully propagated as long-term mammosphere cultures, measured as the increase in the number of viable cells upon serial non-adherent passages. Other cell lines tested (SKBR3, MDA-MB-231, MDA-MB-468 and MDA-MB-435) formed cell clumps that can be disaggregated mechanically, but cell viability drops dramatically on their second passage. HCC1937 and HCC1569 cells formed typical mammospheres, although they could not be propagated as long-term mammosphere cultures. All the sphere forming lines but MDA-MB-436 express E-cadherin on their surface. Knock down of E-cadherin expression in MCF-7 cells abrogated its ability to grow as mammospheres, while re-expression of E-cadherin in SKBR3 cells allow them to form mammospheres. Therefore, the mammosphere assay is suitable to reveal stem like features in breast cancer cell lines that express E-cadherin.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Manuel Iglesias, Juan</style></author><author><style face="normal" font="default" size="100%">Beloqui, Izaskun</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Leis, Olatz</style></author><author><style face="normal" font="default" size="100%">Vazquez-Martin, Alejandro</style></author><author><style face="normal" font="default" size="100%">Eguiara, Arrate</style></author><author><style face="normal" font="default" size="100%">Cufi, Silvia</style></author><author><style face="normal" font="default" size="100%">Pavon, Andres</style></author><author><style face="normal" font="default" size="100%">Menendez, Javier A</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Martin, Angel G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mammosphere formation in breast carcinoma cell lines depends upon expression of E-cadherin.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Cadherins</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Proliferation</style></keyword><keyword><style  face="normal" font="default" size="100%">Cluster Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">gene expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Knockdown Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">MCF-7 Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplastic Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Spheroids, Cellular</style></keyword><keyword><style  face="normal" font="default" size="100%">Tumor Cells, Cultured</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">e77281</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Tumors are heterogeneous at the cellular level where the ability to maintain tumor growth resides in discrete cell populations. Floating sphere-forming assays are broadly used to test stem cell activity in tissues, tumors and cell lines. Spheroids are originated from a small population of cells with stem cell features able to grow in suspension culture and behaving as tumorigenic in mice. We tested the ability of eleven common breast cancer cell lines representing the major breast cancer subtypes to grow as mammospheres, measuring the ability to maintain cell viability upon serial non-adherent passage. Only MCF7, T47D, BT474, MDA-MB-436 and JIMT1 were successfully propagated as long-term mammosphere cultures, measured as the increase in the number of viable cells upon serial non-adherent passages. Other cell lines tested (SKBR3, MDA-MB-231, MDA-MB-468 and MDA-MB-435) formed cell clumps that can be disaggregated mechanically, but cell viability drops dramatically on their second passage. HCC1937 and HCC1569 cells formed typical mammospheres, although they could not be propagated as long-term mammosphere cultures. All the sphere forming lines but MDA-MB-436 express E-cadherin on their surface. Knock down of E-cadherin expression in MCF-7 cells abrogated its ability to grow as mammospheres, while re-expression of E-cadherin in SKBR3 cells allow them to form mammospheres. Therefore, the mammosphere assay is suitable to reveal stem like features in breast cancer cell lines that express E-cadherin.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/24124614?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sánchez-Tena, Susana</style></author><author><style face="normal" font="default" size="100%">Reyes-Zurita, Fernando J</style></author><author><style face="normal" font="default" size="100%">Díaz-Moralli, Santiago</style></author><author><style face="normal" font="default" size="100%">Vinardell, Maria Pilar</style></author><author><style face="normal" font="default" size="100%">Reed, Michelle</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Lupiáñez, José A</style></author><author><style face="normal" font="default" size="100%">Günther, Ulrich</style></author><author><style face="normal" font="default" size="100%">Cascante, Marta</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Maslinic Acid-Enriched Diet Decreases Intestinal Tumorigenesis in Apc(Min/+) Mice through Transcriptomic and Metabolomic Reprogramming.</style></title><secondary-title><style face="normal" font="default" size="100%">PloS one</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">e59392</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Chemoprevention is a pragmatic approach to reduce the risk of colorectal cancer, one of the leading causes of cancer-related death in western countries. In this regard, maslinic acid (MA), a pentacyclic triterpene extracted from wax-like coatings of olives, is known to inhibit proliferation and induce apoptosis in colon cancer cell lines without affecting normal intestinal cells. The present study evaluated the chemopreventive efficacy and associated mechanisms of maslinic acid treatment on spontaneous intestinal tumorigenesis in Apc(Min/+) mice. Twenty-two mice were randomized into 2 groups: control group and MA group, fed with a maslinic acid-supplemented diet for six weeks. MA treatment reduced total intestinal polyp formation by 45% (P&lt;0.01). Putative molecular mechanisms associated with suppressing intestinal polyposis in Apc(Min/+) mice were investigated by comparing microarray expression profiles of MA-treated and control mice and by analyzing the serum metabolic profile using NMR techniques. The different expression phenotype induced by MA suggested that it exerts its chemopreventive action mainly by inhibiting cell-survival signaling and inflammation. These changes eventually induce G1-phase cell cycle arrest and apoptosis. Moreover, the metabolic changes induced by MA treatment were associated with a protective profile against intestinal tumorigenesis. These results show the efficacy and underlying mechanisms of MA against intestinal tumor development in the Apc(Min/+) mice model, suggesting its chemopreventive potential against colorectal cancer.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Silbiger, Vivian N</style></author><author><style face="normal" font="default" size="100%">Luchessi, André D</style></author><author><style face="normal" font="default" size="100%">Hirata, Rosário D C</style></author><author><style face="normal" font="default" size="100%">Lima-Neto, Lídio G</style></author><author><style face="normal" font="default" size="100%">Cavichioli, Débora</style></author><author><style face="normal" font="default" size="100%">Carracedo, Ángel</style></author><author><style face="normal" font="default" size="100%">Brión, Maria</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dos Santos, Elizabete S</style></author><author><style face="normal" font="default" size="100%">Ramos, Rui F</style></author><author><style face="normal" font="default" size="100%">Sampaio, Marcelo F</style></author><author><style face="normal" font="default" size="100%">Armaganijan, Dikran</style></author><author><style face="normal" font="default" size="100%">Sousa, Amanda G M R</style></author><author><style face="normal" font="default" size="100%">Hirata, Mario H</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel genes detected by transcriptional profiling from whole-blood cells in patients with early onset of acute coronary syndrome: Transcriptional profiling of acute coronary syndrome.</style></title><secondary-title><style face="normal" font="default" size="100%">Clinica chimica acta; international journal of clinical chemistry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Mar 24</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">{BACKGROUND: Genome-wide expression analysis using microarrays has been used as a research strategy to discovery new biomarkers and candidate genes for a number of diseases. We aim to find new biomarkers for the prediction of acute coronary syndrome (ACS) with a differentially expressed mRNA profiling approach using whole genomic expression analysis in a peripheral blood cell model from patients with early ACS. METHODS AND RESULTS: This study was carried out in two phases. On phase 1 a restricted clinical criteria (ACS-Ph1</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Silbiger, Vivian N</style></author><author><style face="normal" font="default" size="100%">Luchessi, André D</style></author><author><style face="normal" font="default" size="100%">Hirata, Rosário D C</style></author><author><style face="normal" font="default" size="100%">Lima-Neto, Lídio G</style></author><author><style face="normal" font="default" size="100%">Cavichioli, Débora</style></author><author><style face="normal" font="default" size="100%">Carracedo, Ángel</style></author><author><style face="normal" font="default" size="100%">Brión, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dos Santos, Elizabete S</style></author><author><style face="normal" font="default" size="100%">Ramos, Rui F</style></author><author><style face="normal" font="default" size="100%">Sampaio, Marcelo F</style></author><author><style face="normal" font="default" size="100%">Armaganijan, Dikran</style></author><author><style face="normal" font="default" size="100%">Sousa, Amanda G M R</style></author><author><style face="normal" font="default" size="100%">Hirata, Mario H</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel genes detected by transcriptional profiling from whole-blood cells in patients with early onset of acute coronary syndrome.</style></title><secondary-title><style face="normal" font="default" size="100%">Clin Chim Acta</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Clin Chim Acta</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Acute Coronary Syndrome</style></keyword><keyword><style  face="normal" font="default" size="100%">Acute-Phase Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">biomarkers</style></keyword><keyword><style  face="normal" font="default" size="100%">Blood Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Early Diagnosis</style></keyword><keyword><style  face="normal" font="default" size="100%">gene expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Jun 05</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">421</style></volume><pages><style face="normal" font="default" size="100%">184-90</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Genome-wide expression analysis using microarrays has been used as a research strategy to discovery new biomarkers and candidate genes for a number of diseases. We aim to find new biomarkers for the prediction of acute coronary syndrome (ACS) with a differentially expressed mRNA profiling approach using whole genomic expression analysis in a peripheral blood cell model from patients with early ACS.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS AND RESULTS: &lt;/b&gt;This study was carried out in two phases. On phase 1 a restricted clinical criteria (ACS-Ph1, n=9 and CG-Ph1, n=6) was used in order to select potential mRNA biomarkers candidates. A subsequent phase 2 study was performed using selected phase 1 markers analyzed by RT-qPCR using a larger and independent casuistic (ACS-Ph2, n=74 and CG-Ph2, n=41). A total of 549 genes were found to be differentially expressed in the first 48 h after the ACS-Ph1. Technical and biological validation further confirmed that ALOX15, AREG, BCL2A1, BCL2L1, CA1, COX7B, ECHDC3, IL18R1, IRS2, KCNE1, MMP9, MYL4 and TREML4, are differentially expressed in both phases of this study.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Transcriptomic analysis by microarray technology demonstrated differential expression during a 48 h time course suggesting a potential use of some of these genes as biomarkers for very early stages of ACS, as well as for monitoring early cardiac ischemic recovery.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/23535507?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fernández, Raquel M</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Luzón-Toro, Berta</style></author><author><style face="normal" font="default" size="100%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Arnold, Stacey</style></author><author><style face="normal" font="default" size="100%">Sribudiani, Yunia</style></author><author><style face="normal" font="default" size="100%">Besmond, Claude</style></author><author><style face="normal" font="default" size="100%">Lantieri, Francesca</style></author><author><style face="normal" font="default" size="100%">Doan, Betty</style></author><author><style face="normal" font="default" size="100%">Ceccherini, Isabella</style></author><author><style face="normal" font="default" size="100%">Lyonnet, Stanislas</style></author><author><style face="normal" font="default" size="100%">Hofstra, Robert Mw</style></author><author><style face="normal" font="default" size="100%">Chakravarti, Aravinda</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pathways systematically associated to Hirschsprung’s disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Orphanet journal of rare diseases</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">GWAS</style></keyword><keyword><style  face="normal" font="default" size="100%">Hirschprung</style></keyword><keyword><style  face="normal" font="default" size="100%">network analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Pathway Based Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Dec 2</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ojrd.com/content/8/1/187/abstract</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">187</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Despite it has been reported that several loci are involved in Hirschsprung’s disease, the molecular basis of the disease remains yet essentially unknown. The study of collective properties of modules of functionally-related genes provides an efficient and sensitive statistical framework that can overcome sample size limitations in the study of rare diseases. Here, we present the extension of a previous study of a Spanish series of HSCR trios to an international cohort of 162 HSCR trios to validate the generality of the underlying functional basis of the Hirschsprung’s disease mechanisms previously found. The Pathway-Based Analysis (PBA) confirms a strong association of gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other processes related to the disease. In addition, network analysis recovers sub-networks significantly associated to the disease, which contain genes related to the same functionalities, thus providing an independent validation of these findings. The functional profiles of association obtained for patients populations from different countries were compared to each other. While gene associations were different at each series, the main functional associations were identical in all the five populations. These observations would also explain the reported low reproducibility of associations of individual disease genes across populations.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fernández, Raquel M</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Luzón-Toro, Berta</style></author><author><style face="normal" font="default" size="100%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Arnold, Stacey</style></author><author><style face="normal" font="default" size="100%">Sribudiani, Yunia</style></author><author><style face="normal" font="default" size="100%">Besmond, Claude</style></author><author><style face="normal" font="default" size="100%">Lantieri, Francesca</style></author><author><style face="normal" font="default" size="100%">Doan, Betty</style></author><author><style face="normal" font="default" size="100%">Ceccherini, Isabella</style></author><author><style face="normal" font="default" size="100%">Lyonnet, Stanislas</style></author><author><style face="normal" font="default" size="100%">Hofstra, Robert Mw</style></author><author><style face="normal" font="default" size="100%">Chakravarti, Aravinda</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pathways systematically associated to Hirschsprung's disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Orphanet J Rare Dis</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Orphanet J Rare Dis</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Hirschsprung Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Dec 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">187</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Despite it has been reported that several loci are involved in Hirschsprung's disease, the molecular basis of the disease remains yet essentially unknown. The study of collective properties of modules of functionally-related genes provides an efficient and sensitive statistical framework that can overcome sample size limitations in the study of rare diseases. Here, we present the extension of a previous study of a Spanish series of HSCR trios to an international cohort of 162 HSCR trios to validate the generality of the underlying functional basis of the Hirschsprung's disease mechanisms previously found. The Pathway-Based Analysis (PBA) confirms a strong association of gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other processes related to the disease. In addition, network analysis recovers sub-networks significantly associated to the disease, which contain genes related to the same functionalities, thus providing an independent validation of these findings. The functional profiles of association obtained for patients populations from different countries were compared to each other. While gene associations were different at each series, the main functional associations were identical in all the five populations. These observations would also explain the reported low reproducibility of associations of individual disease genes across populations. &lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/24289864?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">De Maria, Alejandro</style></author><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Celma, Matilde</style></author><author><style face="normal" font="default" size="100%">Martin, Ainoha</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CellBase, a comprehensive collection of RESTful web services for retrieving relevant biological information from heterogeneous sources.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic acids research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Jul</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/content/40/W1/W609.long</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">40</style></volume><pages><style face="normal" font="default" size="100%">W609-14</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">During the past years, the advances in high-throughput technologies have produced an unprecedented growth in the number and size of repositories and databases storing relevant biological data. Today, there is more biological information than ever but, unfortunately, the current status of many of these repositories is far from being optimal. Some of the most common problems are that the information is spread out in many small databases; frequently there are different standards among repositories and some databases are no longer supported or they contain too specific and unconnected information. In addition, data size is increasingly becoming an obstacle when accessing or storing biological data. All these issues make very difficult to extract and integrate information from different sources, to analyze experiments or to access and query this information in a programmatic way. CellBase provides a solution to the growing necessity of integration by easing the access to biological data. CellBase implements a set of RESTful web services that query a centralized database containing the most relevant biological data sources. The database is hosted in our servers and is regularly updated. CellBase documentation can be found at http://docs.bioinfo.cipf.es/projects/cellbase.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fernandez, Paula</style></author><author><style face="normal" font="default" size="100%">Soria, Marcelo</style></author><author><style face="normal" font="default" size="100%">Blesa, David</style></author><author><style face="normal" font="default" size="100%">Dirienzo, Julio</style></author><author><style face="normal" font="default" size="100%">Moschen, Sebastián</style></author><author><style face="normal" font="default" size="100%">Rivarola, Máximo</style></author><author><style face="normal" font="default" size="100%">Clavijo, Bernardo Jose</style></author><author><style face="normal" font="default" size="100%">Gonzalez, Sergio</style></author><author><style face="normal" font="default" size="100%">Peluffo, Lucila</style></author><author><style face="normal" font="default" size="100%">Príncipi, Dario</style></author><author><style face="normal" font="default" size="100%">Dosio, Guillermo</style></author><author><style face="normal" font="default" size="100%">Aguirrezabal, Luis</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Hopp, Esteban</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Heinz, Ruth Amelia</style></author><author><style face="normal" font="default" size="100%">Paniego, Norma</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development, Characterization and Experimental Validation of a Cultivated Sunflower (Helianthus annuus L.) Gene Expression Oligonucleotide Microarray.</style></title><secondary-title><style face="normal" font="default" size="100%">PloS one</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0045899</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">e45899</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Oligonucleotide-based microarrays with accurate gene coverage represent a key strategy for transcriptional studies in orphan species such as sunflower, H. annuus L., which lacks full genome sequences. The goal of this study was the development and functional annotation of a comprehensive sunflower unigene collection and the design and validation of a custom sunflower oligonucleotide-based microarray. A large scale EST (&gt;130,000 ESTs) curation, assembly and sequence annotation was performed using Blast2GO (www.blast2go.de). The EST assembly comprises 41,013 putative transcripts (12,924 contigs and 28,089 singletons). The resulting Sunflower Unigen Resource (SUR version 1.0) was used to design an oligonucleotide-based Agilent microarray for cultivated sunflower. This microarray includes a total of 42,326 features: 1,417 Agilent controls, 74 control probes for sunflower replicated 10 times (740 controls) and 40,169 different non-control probes. Microarray performance was validated using a model experiment examining the induction of senescence by water deficit. Pre-processing and differential expression analysis of Agilent microarrays was performed using the Bioconductor limma package. The analyses based on p-values calculated by eBayes (p&lt;0.01) allowed the detection of 558 differentially expressed genes between water stress and control conditions; from these, ten genes were further validated by qPCR. Over-represented ontologies were identified using FatiScan in the Babelomics suite. This work generated a curated and trustable sunflower unigene collection, and a custom, validated sunflower oligonucleotide-based microarray using Agilent technology. Both the curated unigene collection and the validated oligonucleotide microarray provide key resources for sunflower genome analysis, transcriptional studies, and molecular breeding for crop improvement.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Conesa-Zamora, Pablo</style></author><author><style face="normal" font="default" size="100%">García-Solano, José</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Del Carmen Turpin, María</style></author><author><style face="normal" font="default" size="100%">Trujillo-Santos, Javier</style></author><author><style face="normal" font="default" size="100%">Torres-Moreno, Daniel</style></author><author><style face="normal" font="default" size="100%">Oviedo-Ramírez, Isabel</style></author><author><style face="normal" font="default" size="100%">Carbonell-Muñoz, Rosa</style></author><author><style face="normal" font="default" size="100%">Muñoz-Delgado, Encarnación</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Braun, Edith</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Pérez-Guillermo, Miguel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expression profiling shows differential molecular pathways and provides potential new diagnostic biomarkers for colorectal serrated adenocarcinoma.</style></title><secondary-title><style face="normal" font="default" size="100%">International journal of cancer. Journal international du cancer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Jun 14</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Serrated adenocarcinoma (SAC) is a recently recognized colorectal cancer (CRC) subtype accounting for 7.5-8.7% of CRCs. It has been shown that SAC has a poorer prognosis and has different molecular and immunohistochemical features compared to conventional carcinoma (CC) but, to date, only one previous study has analysed its mRNA expression profile by microarray. Using a different microarray platform, we have studied the molecular signature of 11 SACs and compared it with that of 15 matched CC with the aim of discerning the functions which characterize SAC biology and validating, at the mRNA and protein level, the most differentially expressed genes which were also tested using a validation set of 70 SACs and 70 CCs to assess their diagnostic and prognostic values. Microarray data showed a higher representation of morphogenesis-, hypoxia-, cytoskeleton- and vesicle transport-related functions and also an over-expression of fascin1 (actin-bundling protein associated with invasion) and the antiapoptotic gene hippocalcin in SAC all of which were validated both by qPCR and immunohistochemistry. Fascin1 expression was statistically associated with KRAS mutation with 88.6% sensitivity and 85.7% specificity for SAC diagnosis and the positivity of fascin1 or hippocalcin was highly suggestive of SAC diagnosis (sensitivity=100%). Evaluation of these markers in CRCs showing histological and molecular characteristics of high-level microsatellite instability (MSI-H) also helped to distinguish SACs from MSI-H CRCs. Molecular profiling demonstrates that SAC shows activation of distinct signalling pathways and that immunohistochemical fascin1 and hippocalcin expression can be reliably used for its differentiation from other CRC subtypes. © 2012 Wiley Periodicals, Inc.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jaime, María D L A</style></author><author><style face="normal" font="default" size="100%">Lopez-Llorca, Luis Vicente</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Lee, Anna Y</style></author><author><style face="normal" font="default" size="100%">Proctor, Michael</style></author><author><style face="normal" font="default" size="100%">Heisler, Lawrence E</style></author><author><style face="normal" font="default" size="100%">Gebbia, Marinella</style></author><author><style face="normal" font="default" size="100%">Giaever, Guri</style></author><author><style face="normal" font="default" size="100%">Westwood, J Timothy</style></author><author><style face="normal" font="default" size="100%">Nislow, Corey</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identification of yeast genes that confer resistance to chitosan oligosaccharide (COS) using chemogenomics.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC genomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">267</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BACKGROUND: Chitosan oligosaccharide (COS), a deacetylated derivative of chitin, is an abundant, and renewable natural polymer. COS has higher antimicrobial properties than chitosan and is presumed to act by disrupting/permeabilizing the cell membranes of bacteria, yeast and fungi. COS is relatively non-toxic to mammals. By identifying the molecular and genetic targets of COS, we hope to gain a better understanding of the antifungal mode of action of COS. RESULTS: Three different chemogenomic fitness assays, haploinsufficiency (HIP), homozygous deletion (HOP), and multicopy suppression (MSP) profiling were combined with a transcriptomic analysis to gain insight in to the mode of action and mechanisms of resistance to chitosan oligosaccharides. The fitness assays identified 39 yeast deletion strains sensitive to COS and 21 suppressors of COS sensitivity. The genes identified are involved in processes such as RNA biology (transcription, translation and regulatory mechanisms), membrane functions (e.g. signalling, transport and targeting), membrane structural components, cell division, and proteasome processes. The transcriptomes of control wild type and 5 suppressor strains overexpressing ARL1, BCK2, ERG24, MSG5, or RBA50, were analyzed in the presence and absence of COS. Some of the up-regulated transcripts in the suppressor overexpressing strains exposed to COS included genes involved in transcription, cell cycle, stress response and the Ras signal transduction pathway. Down-regulated transcripts included those encoding protein folding components and respiratory chain proteins. The COS-induced transcriptional response is distinct from previously described environmental stress responses (i.e. thermal, salt, osmotic and oxidative stress) and pre-treatment with these well characterized environmental stressors provided little or any resistance to COS. CONCLUSIONS: Overexpression of the ARL1 gene, a member of the Ras superfamily that regulates membrane trafficking, provides protection against COS-induced cell membrane permeability and damage. We found that the ARL1 COS-resistant over-expression strain was as sensitive to Amphotericin B, Fluconazole and Terbinafine as the wild type cells and that when COS and Fluconazole are used in combination they act in a synergistic fashion. The gene targets of COS identified in this study indicate that COS’s mechanism of action is different from other commonly studied fungicides that target membranes, suggesting that COS may be an effective fungicide for drug-resistant fungal pathogens.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carrero, Rubén</style></author><author><style face="normal" font="default" size="100%">Cerrada, Inmaculada</style></author><author><style face="normal" font="default" size="100%">Lledó, Elisa</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Rubio, Mari-Paz</style></author><author><style face="normal" font="default" size="100%">Trigueros, César</style></author><author><style face="normal" font="default" size="100%">Dorronsoro, Akaitz</style></author><author><style face="normal" font="default" size="100%">Ruiz-Sauri, Amparo</style></author><author><style face="normal" font="default" size="100%">Montero, José Anastasio</style></author><author><style face="normal" font="default" size="100%">Sepúlveda, Pilar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">IL1β induces mesenchymal stem cells migration and leucocyte chemotaxis through NF-κB.</style></title><secondary-title><style face="normal" font="default" size="100%">Stem Cell Rev Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Stem Cell Rev Rep</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cell Adhesion</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Movement</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Proliferation</style></keyword><keyword><style  face="normal" font="default" size="100%">Chemokines</style></keyword><keyword><style  face="normal" font="default" size="100%">Chemotaxis, Leukocyte</style></keyword><keyword><style  face="normal" font="default" size="100%">Collagen</style></keyword><keyword><style  face="normal" font="default" size="100%">Fibronectins</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Knockdown Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">HEK293 Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">I-kappa B Kinase</style></keyword><keyword><style  face="normal" font="default" size="100%">Inflammation Mediators</style></keyword><keyword><style  face="normal" font="default" size="100%">Intercellular Signaling Peptides and Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Interleukin-1beta</style></keyword><keyword><style  face="normal" font="default" size="100%">Laminin</style></keyword><keyword><style  face="normal" font="default" size="100%">Leukocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Mesenchymal Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">NF-kappa B</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA Interference</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Sep</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">905-16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Mesenchymal stem cells are often transplanted into inflammatory environments where they are able to survive and modulate host immune responses through a poorly understood mechanism. In this paper we analyzed the responses of MSC to IL-1β: a representative inflammatory mediator. Microarray analysis of MSC treated with IL-1β revealed that this cytokine activateds a set of genes related to biological processes such as cell survival, cell migration, cell adhesion, chemokine production, induction of angiogenesis and modulation of the immune response. Further more detailed analysis by real-time PCR and functional assays revealed that IL-1β mainly increaseds the production of chemokines such as CCL5, CCL20, CXCL1, CXCL3, CXCL5, CXCL6, CXCL10, CXCL11 and CX(3)CL1, interleukins IL-6, IL-8, IL23A, IL32, Toll-like receptors TLR2, TLR4, CLDN1, metalloproteins MMP1 and MMP3, growth factors CSF2 and TNF-α, together with adhesion molecules ICAM1 and ICAM4. Functional analysis of MSC proliferation, migration and adhesion to extracellular matrix components revealed that IL-1β did not affect proliferation but also served to induce the secretion of trophic factors and adhesion to ECM components such as collagen and laminin. IL-1β treatment enhanced the ability of MSC to recruit monocytes and granulocytes in vitro. Blockade of NF-κβ transcription factor activation with IκB kinase beta (IKKβ) shRNA impaired MSC migration, adhesion and leucocyte recruitment, induced by IL-1β demonstrating that NF-κB pathway is an important downstream regulator of these responses. These findings are relevant to understanding the biological responses of MSC to inflammatory environments.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/22467443?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Alloza, Eva</style></author><author><style face="normal" font="default" size="100%">Arce, Pablo</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author><author><style face="normal" font="default" size="100%">Santoyo, Javier</style></author><author><style face="normal" font="default" size="100%">Ruiz-Ferrer, Macarena</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Jiménez-Almazán, Jorge</style></author><author><style face="normal" font="default" size="100%">Méndez-Vidal, Cristina</style></author><author><style face="normal" font="default" size="100%">González-del Pozo, María</style></author><author><style face="normal" font="default" size="100%">Vela, Alicia</style></author><author><style face="normal" font="default" size="100%">Bhattacharya, Shomi S</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A map of human microRNA variation uncovers unexpectedly high levels of variability.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome medicine</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Aug 20</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://genomemedicine.com/content/4/8/62/abstract</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">62</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">ABSTRACT: BACKGROUND: MicroRNAs (miRNAs) are key components of the gene regulatory network in many species. During the past few years, these regulatory elements have been shown to be involved in an increasing number and range of diseases. Consequently, the compilation of a comprehensive map of natural variability in healthy population seems an obvious requirement for future research on miRNA-related pathologies. METHODS: Data on 14 populations from the 1000 Genomes Project were analysed, along with new data extracted from 60 exomes of healthy individuals from a southern Spain population, sequenced in the context of the Medical Genome Project, to derive an accurate map of miRNA variability. RESULTS: Despite the common belief that miRNAs are highly conserved elements, analysis of the sequences of the 1,152 individuals indicated that the observed level of variability is double what was expected. A total of 527 variants were found. Among these, 45 variants affected the recognition region of the corresponding miRNA and were found in 43 different miRNAs, 26 of which are known to be involved in 57 diseases. Different parts of the mature structure of the miRNA were affected to different degrees by variants, which suggests the existence of a selective pressure related to the relative functional impact of the change. Moreover, 41 variants showed a significant deviation from the Hardy-Weinberg equilibrium, which supports the existence of a selective process against some alleles. The average number of variants per individual in miRNAs was 28. CONCLUSIONS: Despite an expectation that miRNAs would be highly conserved genomic elements, our study reports a level of variability comparable to that observed for coding genes.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rizza, Serena</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Juarez, José</style></author><author><style face="normal" font="default" size="100%">Catara, Antonino</style></author><author><style face="normal" font="default" size="100%">Navarro, Luis</style></author><author><style face="normal" font="default" size="100%">Duran-Vila, Nuria</style></author><author><style face="normal" font="default" size="100%">Ancillo, Gema</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Microarray analysis of Etrog citron (Citrus medica L.) reveals changes in chloroplast, cell wall, peroxidase and symporter activities in response to viroid infection.</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular plant pathology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Mar 15</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Viroids are small (246-401 nucleotides), single-stranded, circular RNA molecules that infect several crop plants and can cause diseases of economic importance. Citrus are the hosts in which the largest number of viroids have been identified. Citrus exocortis viroid (CEVd), the causal agent of citrus exocortis disease, induces considerable losses in citrus crops. Changes in the gene expression profile during the early (pre-symptomatic) and late (post-symptomatic) stages of Etrog citron infected with CEVd were investigated using a citrus cDNA microarray. MaSigPro analysis was performed and, on the basis of gene expression profiles as a function of the time after infection, the differentially expressed genes were classified into five clusters. FatiScan analysis revealed significant enrichment of functional categories for each cluster, indicating that viroid infection triggers important changes in chloroplast, cell wall, peroxidase and symporter activities.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Koziol, Agnieszka</style></author><author><style face="normal" font="default" size="100%">Gonzalo, Pilar</style></author><author><style face="normal" font="default" size="100%">Mota, Alba</style></author><author><style face="normal" font="default" size="100%">Pollán, Angela</style></author><author><style face="normal" font="default" size="100%">Lorenzo, Cristina</style></author><author><style face="normal" font="default" size="100%">Colomé, Nuria</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Arribas, Joaquín</style></author><author><style face="normal" font="default" size="100%">Canals, Francesc</style></author><author><style face="normal" font="default" size="100%">Arroyo, Alicia G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The protease MT1-MMP drives a combinatorial proteolytic program in activated endothelial cells.</style></title><secondary-title><style face="normal" font="default" size="100%">FASEB J</style></secondary-title><alt-title><style face="normal" font="default" size="100%">FASEB J</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Blotting, Western</style></keyword><keyword><style  face="normal" font="default" size="100%">Combinatorial Chemistry Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Endothelial Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Enzymologic</style></keyword><keyword><style  face="normal" font="default" size="100%">Inflammation</style></keyword><keyword><style  face="normal" font="default" size="100%">Matrix Metalloproteinase 14</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Array Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Reverse Transcriptase Polymerase Chain Reaction</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA Interference</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Small Interfering</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">Tumor Necrosis Factor-alpha</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Nov</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">4481-94</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The mechanism by which proteolytic events translate into biological responses is not well understood. To explore the link of pericellular proteolysis to events relevant to capillary sprouting within the inflammatory context, we aimed at the identification of the collection of substrates of the protease MT1-MMP in endothelial tip cells induced by inflammatory stimuli. We applied quantitative proteomics to endothelial cells (ECs) derived from wild-type and MT1-MMP-null mice to identify the substrate repertoire of this protease in TNF-α-activated ECs. Bioinformatics analysis revealed a combinatorial MT1-MMP proteolytic program, in which combined rather than single substrate processing would determine biological decisions by activated ECs, including chemotaxis, cell motility and adhesion, and vasculature development. MT1-MMP-deficient ECs inefficiently processed several of these substrates (TSP1, CYR61, NID1, and SEM3C), validating the model. This novel concept of MT1-MMP-driven combinatorial proteolysis in angiogenesis might be extendable to proteolytic actions in other cellular contexts.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/22859368?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">García-Alcalde, Fernando</style></author><author><style face="normal" font="default" size="100%">Okonechnikov, Konstantin</style></author><author><style face="normal" font="default" size="100%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Cruz, Luis M</style></author><author><style face="normal" font="default" size="100%">Götz, Stefan</style></author><author><style face="normal" font="default" size="100%">Sonia Tarazona</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Meyer, Thomas F</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Qualimap: evaluating next-generation sequencing alignment data.</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics (Oxford, England)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Oct 15</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://bioinformatics.oxfordjournals.org/content/28/20/2678.long</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">2678-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">MOTIVATION: The sequence alignment/map (SAM) and the binary alignment/map (BAM) formats have become the standard method of representation of nucleotide sequence alignments for next-generation sequencing data. SAM/BAM files usually contain information from tens to hundreds of millions of reads. Often, the sequencing technology, protocol and/or the selected mapping algorithm introduce some unwanted biases in these data. The systematic detection of such biases is a non-trivial task that is crucial to drive appropriate downstream analyses. RESULTS: We have developed Qualimap, a Java application that supports user-friendly quality control of mapping data, by considering sequence features and their genomic properties. Qualimap takes sequence alignment data and provides graphical and statistical analyses for the evaluation of data. Such quality-control data are vital for highlighting problems in the sequencing and/or mapping processes, which must be addressed prior to further analyses. AVAILABILITY: Qualimap is freely available from http://www.qualimap.org. CONTACT: aconesa@cipf.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lorente-Galdos, Belén</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Morcillo-Suarez, Carlos</style></author><author><style face="normal" font="default" size="100%">Heredia, Txema</style></author><author><style face="normal" font="default" size="100%">Carreño-Torres, Angel</style></author><author><style face="normal" font="default" size="100%">Sangrós, Ricardo</style></author><author><style face="normal" font="default" size="100%">Alegre, Josep</style></author><author><style face="normal" font="default" size="100%">Pita, Guillermo</style></author><author><style face="normal" font="default" size="100%">Vellalta, Gemma</style></author><author><style face="normal" font="default" size="100%">Malats, Nuria</style></author><author><style face="normal" font="default" size="100%">Pisano, David G</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Navarro, Arcadi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Select your SNPs (SYSNPs): a web tool for automatic and massive selection of SNPs.</style></title><secondary-title><style face="normal" font="default" size="100%">International journal of data mining and bioinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://inderscience.metapress.com/content/f76740x8071u513n/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">324-34</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Association studies are the choice approach in the discovery of the genomic basis of complex traits. To carry out such analysis, researchers frequently need to (1) select optimally informative sets of Single Nucleotide Polymorphisms (SNPs) in candidate regions and (2) annotate the results of associations found by means of genome-wide SNP arrays. These are complex tasks, since many criteria have to be considered, including the SNPs’ functional properties, technological information and haplotype frequencies in given populations. SYSNPs implements algorithms that allow for efficient and simultaneous consideration of all the relevant criteria to obtain sets of SNPs that properly cover arbitrarily large lists of genes or genomic regions. Complementarily, SYSNPs allows for comprehensive functional annotation of SNPs linked to any given marker SNP. SYSNPs dramatically reduces the effort needed for SNP selection from days of searching various databases to a few minutes using a simple browser.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Oppert, Brenda</style></author><author><style face="normal" font="default" size="100%">Dowd, Scot E</style></author><author><style face="normal" font="default" size="100%">Bouffard, Pascal</style></author><author><style face="normal" font="default" size="100%">Li, Lewyn</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Lorenzen, Marcé D</style></author><author><style face="normal" font="default" size="100%">Toutges, Michelle</style></author><author><style face="normal" font="default" size="100%">Marshall, Jeremy</style></author><author><style face="normal" font="default" size="100%">Huestis, Diana L</style></author><author><style face="normal" font="default" size="100%">Fabrick, Jeff</style></author><author><style face="normal" font="default" size="100%">Oppert, Cris</style></author><author><style face="normal" font="default" size="100%">Jurat-Fuentes, Juan Luis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transcriptome profiling of the intoxication response of Tenebrio molitor larvae to Bacillus thuringiensis Cry3Aa protoxin.</style></title><secondary-title><style face="normal" font="default" size="100%">PloS one</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Administration</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Bacterial Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Base Sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">Biosynthetic Pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">Complementary</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Endotoxins</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy Metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Hemolysin Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Larva</style></keyword><keyword><style  face="normal" font="default" size="100%">Microarray Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Data</style></keyword><keyword><style  face="normal" font="default" size="100%">Oral</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Tenebrio</style></keyword><keyword><style  face="normal" font="default" size="100%">Time Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">e34624</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Bacillus thuringiensis (Bt) crystal (Cry) proteins are effective against a select number of insect pests, but improvements are needed to increase efficacy and decrease time to mortality for coleopteran pests. To gain insight into the Bt intoxication process in Coleoptera, we performed RNA-Seq on cDNA generated from the guts of Tenebrio molitor larvae that consumed either a control diet or a diet containing Cry3Aa protoxin. Approximately 134,090 and 124,287 sequence reads from the control and Cry3Aa-treated groups were assembled into 1,318 and 1,140 contigs, respectively. Enrichment analyses indicated that functions associated with mitochondrial respiration, signalling, maintenance of cell structure, membrane integrity, protein recycling/synthesis, and glycosyl hydrolases were significantly increased in Cry3Aa-treated larvae, whereas functions associated with many metabolic processes were reduced, especially glycolysis, tricarboxylic acid cycle, and fatty acid synthesis. Microarray analysis was used to evaluate temporal changes in gene expression after 6, 12 or 24 h of Cry3Aa exposure. Overall, microarray analysis indicated that transcripts related to allergens, chitin-binding proteins, glycosyl hydrolases, and tubulins were induced, and those related to immunity and metabolism were repressed in Cry3Aa-intoxicated larvae. The 24 h microarray data validated most of the RNA-Seq data. Of the three intoxication intervals, larvae demonstrated more differential expression of transcripts after 12 h exposure to Cry3Aa. Gene expression examined by three different methods in control vs. Cry3Aa-treated larvae at the 24 h time point indicated that transcripts encoding proteins with chitin-binding domain 3 were the most differentially expressed in Cry3Aa-intoxicated larvae. Overall, the data suggest that T. molitor larvae mount a complex response to Cry3Aa during the initial 24 h of intoxication. Data from this study represent the largest genetic sequence dataset for T. molitor to date. Furthermore, the methods in this study are useful for comparative analyses in organisms lacking a sequenced genome.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carcel-Trullols, Jaime</style></author><author><style face="normal" font="default" size="100%">Aguilar-Gallardo, Cristóbal</style></author><author><style face="normal" font="default" size="100%">García-Alcalde, Fernando</style></author><author><style face="normal" font="default" size="100%">Pardo-Cea, Miguel Angel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Simon, Carlos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transdifferentiation of MALME-3M and MCF-7 Cells toward Adipocyte-like Cells is Dependent on Clathrin-mediated Endocytosis.</style></title><secondary-title><style face="normal" font="default" size="100%">SpringerPlus</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3725915/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">44</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">ABSTRACT: Enforced cell transdifferentiation of human cancer cells is a promising alternative to conventional chemotherapy. We previously identified albumin-associated lipid- and, more specifically, saturated fatty acid-induced transdifferentiation programs in human cancer cells (HCCLs). In this study, we further characterized the adipocyte-like cells, resulting from the transdifferentiation of human cancer cell lines MCF-7 and MALME-3M, and proposed a common mechanistic approach for these transdifferentiating programs. We showed the loss of pigmentation in MALME-3M cells treated with albumin-associated lipids, based on electron microscopic analysis, and the overexpression of perilipin 2 (PLIN2) by western blotting in MALME-3M and MCF-7 cells treated with unsaturated fatty acids. Comparing the gene expression profiles of naive melanoma MALME-3M cells and albumin-associated lipid-treated cells, based on RNA sequencing, we confirmed the transcriptional upregulation of some key adipogenic gene markers and also an alternative splicing of the adipogenic master regulator PPARG, that is probably related to the reported up regulated expression of the protein. Most importantly, these results also showed the upregulation of genes responsible for Clathrin (CLTC) and other adaptor-related proteins. An increase in CLTC expression in the transdifferentiated cells was confirmed by western blotting. Inactivation of CLTC by chlorpromazine (CHP), an inhibitor of CTLC mediated endocytosis (CME), and gene silencing by siRNAs, partially reversed the accumulation of neutral lipids observed in the transdifferentiated cells. These findings give a deeper insight into the phenotypic changes observed in HCCL to adipocyte-like transdifferentiation and point towards CME as a key pathway in distinct transdifferentiation programs. DISCLOSURES: Simon C and Aguilar-Gallardo C are co-inventors of the International Patent Application No. PCT/EP2011/004941 entitled &quot;Methods for tumor treatment and adipogenesis differentiation&quot;.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Torres, J. S.</style></author><author><style face="normal" font="default" size="100%">Espert, I. B.</style></author><author><style face="normal" font="default" size="100%">Dominguez, A. T.</style></author><author><style face="normal" font="default" size="100%">Garcia, V. Hernendez</style></author><author><style face="normal" font="default" size="100%">Castello, I. Medina</style></author><author><style face="normal" font="default" size="100%">Gimenez, J. Terraga</style></author><author><style face="normal" font="default" size="100%">Blazquez, J. Dopazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using GPUs for the Exact Alignment of Short-Read Genetic Sequences by Means of the Burrows-Wheeler Transform</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE/ACM Transactions on Computational Biology and Bioinformatics</style></secondary-title><short-title><style face="normal" font="default" size="100%">IEEE/ACM Trans. Comput. Biol. and Bioinf.</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-07-2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/document/6175888/http://xplorestaging.ieee.org/ielx5/8857/6202798/06175888.pdf?arnumber=6175888</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">1245 - 1256</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Salavert Torres, Jose</style></author><author><style face="normal" font="default" size="100%">Blanquer Espert, Ignacio</style></author><author><style face="normal" font="default" size="100%">Domínguez, Andrés Tomás</style></author><author><style face="normal" font="default" size="100%">Hernández García, Vicente</style></author><author><style face="normal" font="default" size="100%">Medina Castelló, Ignacio</style></author><author><style face="normal" font="default" size="100%">Tárraga Giménez, Joaquín</style></author><author><style face="normal" font="default" size="100%">Dopazo Blázquez, Joaquín</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using GPUs for the exact alignment of short-read genetic sequences by means of the Burrows-Wheeler transform.</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE/ACM Trans Comput Biol Bioinform</style></secondary-title><alt-title><style face="normal" font="default" size="100%">IEEE/ACM Trans Comput Biol Bioinform</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Graphics</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Compression</style></keyword><keyword><style  face="normal" font="default" size="100%">Drosophila melanogaster</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Insect</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Processing, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Alignment</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Jul-Aug</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">1245-56</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;General Purpose Graphic Processing Units (GPGPUs) constitute an inexpensive resource for computing-intensive applications that could exploit an intrinsic fine-grain parallelism. This paper presents the design and implementation in GPGPUs of an exact alignment tool for nucleotide sequences based on the Burrows-Wheeler Transform. We compare this algorithm with state-of-the-art implementations of the same algorithm over standard CPUs, and considering the same conditions in terms of I/O. Excluding disk transfers, the implementation of the algorithm in GPUs shows a speedup larger than 12, when compared to CPU execution. This implementation exploits the parallelism by concurrently searching different sequences on the same reference search tree, maximizing memory locality and ensuring a symmetric access to the data. The paper describes the behavior of the algorithm in GPU, showing a good scalability in the performance, only limited by the size of the GPU inner memory.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/22450827?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Trevino, Victor</style></author><author><style face="normal" font="default" size="100%">Tadesse, Mahlet G</style></author><author><style face="normal" font="default" size="100%">Vannucci, Marina</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Antczak, Philipp</style></author><author><style face="normal" font="default" size="100%">Durant, Sarah</style></author><author><style face="normal" font="default" size="100%">Bikfalvi, Andreas</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Campbell, Moray J</style></author><author><style face="normal" font="default" size="100%">Falciani, Francesco</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of normal-tumour tissue interaction in tumours: prediction of prostate cancer features from the molecular profile of adjacent normal cells.</style></title><secondary-title><style face="normal" font="default" size="100%">PloS one</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">e16492</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Statistical modelling, in combination with genome-wide expression profiling techniques, has demonstrated that the molecular state of the tumour is sufficient to infer its pathological state. These studies have been extremely important in diagnostics and have contributed to improving our understanding of tumour biology. However, their importance in in-depth understanding of cancer patho-physiology may be limited since they do not explicitly take into consideration the fundamental role of the tissue microenvironment in specifying tumour physiology. Because of the importance of normal cells in shaping the tissue microenvironment we formulate the hypothesis that molecular components of the profile of normal epithelial cells adjacent the tumour are predictive of tumour physiology. We addressed this hypothesis by developing statistical models that link gene expression profiles representing the molecular state of adjacent normal epithelial cells to tumour features in prostate cancer. Furthermore, network analysis showed that predictive genes are linked to the activity of important secreted factors, which have the potential to influence tumor biology, such as IL1, IGF1, PDGF BB, AGT, and TGF&amp;beta;.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nueda, Maria J</style></author><author><style face="normal" font="default" size="100%">Alberto Ferrer</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments.</style></title><secondary-title><style face="normal" font="default" size="100%">Biostatistics (Oxford, England)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Nov 14</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Transcriptomic profiling experiments that aim to the identification of responsive genes in specific biological conditions are commonly set up under defined experimental designs that try to assess the effects of factors and their interactions on gene expression. Data from these controlled experiments, however, may also contain sources of unwanted noise that can distort the signal under study, affect the residuals of applied statistical models, and hamper data analysis. Commonly, normalization methods are applied to transcriptomics data to remove technical artifacts, but these are normally based on general assumptions of transcript distribution and greatly ignore both the characteristics of the experiment under consideration and the coordinative nature of gene expression. In this paper, we propose a novel methodology, ARSyN, for the preprocessing of microarray data that takes into account these 2 last aspects. By combining analysis of variance (ANOVA) modeling of gene expression values and multivariate analysis of estimated effects, the method identifies the nonstructured part of the signal associated to the experimental factors (the noise within the signal) and the structured variation of the ANOVA errors (the signal of the noise). By removing these noise fractions from the original data, we create a filtered data set that is rich in the information of interest and includes only the random noise required for inferential analysis. In this work, we focus on multifactorial time course microarray (MTCM) experiments with 2 factors: one quantitative such as time or dosage and the other qualitative, as tissue, strain, or treatment. However, the method can be used in other situations such as experiments with only one factor or more complex designs with more than 2 factors. The filtered data obtained after applying ARSyN can be further analyzed with the appropriate statistical technique to obtain the biological information required. To evaluate the performance of the filtering strategy, we have applied different statistical approaches for MTCM analysis to several real and simulated data sets, studying also the efficiency of these techniques. By comparing the results obtained with the original and ARSyN filtered data and also with other filtering techniques, we can conclude that the proposed method increases the statistical power to detect biological signals, especially in cases where there are high levels of structural noise. Software for ARSyN is freely available at http://www.ua.es/personal/mj.nueda.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Götz, Stefan</style></author><author><style face="normal" font="default" size="100%">Arnold, Roland</style></author><author><style face="normal" font="default" size="100%">Sebastián-Leon, Patricia</style></author><author><style face="normal" font="default" size="100%">Martín-Rodríguez, Samuel</style></author><author><style face="normal" font="default" size="100%">Tischler, Patrick</style></author><author><style face="normal" font="default" size="100%">Jehl, Marc-André</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Rattei, Thomas</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">B2G-FAR, a species centered GO annotation repository.</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics (Oxford, England)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Feb 18</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">7</style></number><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">919-924</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;MOTIVATION: Functional genomics research has expanded enormously in the last decade thanks to the cost-reduction in high-throughput technologies and the development of computational tools that generate, standardize and share information on gene and protein function such as the Gene Ontology (GO). Nevertheless many biologists, especially working with non-model organisms, still suffer from non-existing or low coverage functional annotation, or simply struggle retrieving, summarizing and querying these data. RESULTS: The Blast2GO Functional Annotation Repository (B2G-FAR) is a bioinformatics resource envisaged to provide functional information for otherwise uncharacterized sequence-data and offers data-mining tools to analyze a larger repertoire of species than currently available. This new annotation resource has been created by applying the Blast2GO functional annotation engine in a strongly high-throughput manner to the entire space of public available sequences. The resulting repository contains GO term predictions for over 13.2 million non-redundant protein sequences based on BLAST search alignments from the SIMAP database. We generated GO annotation for approximately 150.000 different taxa making available the 2000 species with the highest coverage through B2G-FAR. A second section within B2G-FAR holds functional annotations for 17 non-model organism Affymetrix GeneChips. Conclusions: B2G-FAR provides easy access to exhaustive functional annotation for 2000 species offering a good balance between quality and quantity, thereby supporting functional genomics research especially in the case of non-model organisms. AVAILABILITY: The annotation resource is available at http://b2gfar.bioinfo.cipf.es. CONTACT: aconesa@cipf.es, sgoetz@cipf.es.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tarazona, Sonia</style></author><author><style face="normal" font="default" size="100%">García-Alcalde, Fernando</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Ferrer, Alberto</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Differential expression in RNA-seq: a matter of depth.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genome Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Expressed Sequence Tags</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Dec</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">2213-23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach--NOISeq--that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/21903743?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prieur, Xavier</style></author><author><style face="normal" font="default" size="100%">Mok, Crystal Y L</style></author><author><style face="normal" font="default" size="100%">Velagapudi, Vidya R</style></author><author><style face="normal" font="default" size="100%">Núñez, Vanessa</style></author><author><style face="normal" font="default" size="100%">Fuentes, Lucía</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Ishikawa, Ko</style></author><author><style face="normal" font="default" size="100%">Camacho, Alberto</style></author><author><style face="normal" font="default" size="100%">Barbarroja, Nuria</style></author><author><style face="normal" font="default" size="100%">O’Rahilly, Stephen</style></author><author><style face="normal" font="default" size="100%">Sethi, Jaswinder</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Oresic, Matej</style></author><author><style face="normal" font="default" size="100%">Ricote, Mercedes</style></author><author><style face="normal" font="default" size="100%">Vidal-Puig, Antonio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Differential Lipid Partitioning Between Adipocytes and Tissue Macrophages Modulates Macrophage Lipotoxicity and M2/M1 Polarization in Obese Mice.</style></title><secondary-title><style face="normal" font="default" size="100%">Diabetes</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Jan 24</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">60</style></volume><pages><style face="normal" font="default" size="100%">797-809</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;OBJECTIVE Obesity-associated insulin resistance is characterized by a state of chronic, low-grade inflammation that is associated with the accumulation of M1 proinflammatory macrophages in adipose tissue. Although different evidence explains the mechanisms linking the expansion of adipose tissue and adipose tissue macrophage (ATM) polarization, in the current study we investigated the concept of lipid-induced toxicity as the pathogenic link that could explain the trigger of this response. RESEARCH DESIGN AND METHODS We addressed this question using isolated ATMs and adipocytes from genetic and diet-induced murine models of obesity. Through transcriptomic and lipidomic analysis, we created a model integrating transcript and lipid species networks simultaneously occurring in adipocytes and ATMs and their reversibility by thiazolidinedione treatment. RESULTS We show that polarization of ATMs is associated with lipid accumulation and the consequent formation of foam cell-like cells in adipose tissue. Our study reveals that early stages of adipose tissue expansion are characterized by M2-polarized ATMs and that progressive lipid accumulation within ATMs heralds the M1 polarization, a macrophage phenotype associated with severe obesity and insulin resistance. Furthermore, rosiglitazone treatment, which promotes redistribution of lipids toward adipocytes and extends the M2 ATM polarization state, prevents the lipid alterations associated with M1 ATM polarization. CONCLUSIONS Our data indicate that the M1 ATM polarization in obesity might be a macrophage-specific manifestation of a more general lipotoxic pathogenic mechanism. This indicates that strategies to optimize fat deposition and repartitioning toward adipocytes might improve insulin sensitivity by preventing ATM lipotoxicity and M1 polarization.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vivas, Yurena</style></author><author><style face="normal" font="default" size="100%">Martinez-Garcia, Cristina</style></author><author><style face="normal" font="default" size="100%">Izquierdo, Adriana</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Callejas, Sergio</style></author><author><style face="normal" font="default" size="100%">Velasco, Ismael</style></author><author><style face="normal" font="default" size="100%">Campbell, Mark</style></author><author><style face="normal" font="default" size="100%">Ros, Manuel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Ana</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Vidal-Puig, Antonio</style></author><author><style face="normal" font="default" size="100%">Medina-Gomez, Gema</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Early peroxisome proliferator-activated receptor gamma regulated genes involved in expansion of pancreatic beta cell mass.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Med Genomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Med Genomics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Proliferation</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Survival</style></keyword><keyword><style  face="normal" font="default" size="100%">Cholesterol</style></keyword><keyword><style  face="normal" font="default" size="100%">Down-Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Knockout Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Insulin Resistance</style></keyword><keyword><style  face="normal" font="default" size="100%">Insulin-Secreting Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">obesity</style></keyword><keyword><style  face="normal" font="default" size="100%">Oxidation-Reduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Phosphorylation</style></keyword><keyword><style  face="normal" font="default" size="100%">PPAR gamma</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Transforming Growth Factor beta</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Dec 30</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">86</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;The progression towards type 2 diabetes depends on the allostatic response of pancreatic beta cells to synthesise and secrete enough insulin to compensate for insulin resistance. The endocrine pancreas is a plastic tissue able to expand or regress in response to the requirements imposed by physiological and pathophysiological states associated to insulin resistance such as pregnancy, obesity or ageing, but the mechanisms mediating beta cell mass expansion in these scenarios are not well defined. We have recently shown that ob/ob mice with genetic ablation of PPARγ2, a mouse model known as the POKO mouse failed to expand its beta cell mass. This phenotype contrasted with the appropriate expansion of the beta cell mass observed in their obese littermate ob/ob mice. Thus, comparison of these models islets particularly at early ages could provide some new insights on early PPARγ dependent transcriptional responses involved in the process of beta cell mass expansion&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Here we have investigated PPARγ dependent transcriptional responses occurring during the early stages of beta cell adaptation to insulin resistance in wild type, ob/ob, PPARγ2 KO and POKO mice. We have identified genes known to regulate both the rate of proliferation and the survival signals of beta cells. Moreover we have also identified new pathways induced in ob/ob islets that remained unchanged in POKO islets, suggesting an important role for PPARγ in maintenance/activation of mechanisms essential for the continued function of the beta cell.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Our data suggest that the expansion of beta cell mass observed in ob/ob islets is associated with the activation of an immune response that fails to occur in POKO islets. We have also indentified other PPARγ dependent differentially regulated pathways including cholesterol biosynthesis, apoptosis through TGF-β signaling and decreased oxidative phosphorylation.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/22208362?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Khalaf, Abeer A</style></author><author><style face="normal" font="default" size="100%">Gmitter, Frederick G</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Moore, Gloria A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fortunella margarita Transcriptional Reprogramming Triggered by Xanthomonas citri subsp. citri.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC plant biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">159</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">ABSTRACT:</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leida, Carmen</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Llácer, Gerardo</style></author><author><style face="normal" font="default" size="100%">Badenes, María Luisa</style></author><author><style face="normal" font="default" size="100%">Ríos, Gabino</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Histone modifications and expression of DAM6 gene in peach are modulated during bud dormancy release in a cultivar-dependent manner.</style></title><secondary-title><style face="normal" font="default" size="100%">The New phytologist</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Sep 7</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&amp;bull; Bud dormancy release in many woody perennial plants responds to the seasonal accumulation of chilling stimulus. MADS-box transcription factors encoded by DORMANCY ASSOCIATED MADS-box (DAM) genes in peach (Prunus persica) are implicated in this pathway, but other regulatory factors remain to be identified. In addition, the regulation of DAM gene expression is not well known at the molecular level. &amp;bull; A microarray hybridization approach was performed to identify genes whose expression correlates with the bud dormancy-related behaviour in 10 different peach cultivars. Histone modifications in DAM6 gene were investigated by chromatin immunoprecipitation in two different cultivars. &amp;bull; The expression of DAM4-DAM6 and several genes related to abscisic acid and drought stress response correlated with the dormancy behaviour of peach cultivars. The trimethylation of histone H3 at K27 in the DAM6 promoter, coding region and the second large intron was preceded by a decrease in acetylated H3 and trimethylated H3K4 in the region of translation start, coinciding with repression of DAM6 during dormancy release. &amp;bull; Analysis of chromatin modifications reinforced the role of epigenetic mechanisms in DAM6 regulation and bud dormancy release, and highlighted common features with the vernalization process in Arabidopsis thaliana and cereals.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alloza, E.</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Cigudosa, J. C.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A large scale survey reveals that chromosomal copy-number alterations significantly affect gene modules involved in cancer initiation and progression</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Medical Genomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/05/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.biomedcentral.com/1755-8794/4/37</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">37</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;h4&gt;Background&lt;/h4&gt; &lt;p&gt;Recent observations point towards the existence of a large number of neighborhoods composed of functionally-related gene modules that lie together in the genome. This local component in the distribution of the functionality across chromosomes is probably affecting the own chromosomal architecture by limiting the possibilities in which genes can be arranged and distributed across the genome. As a direct consequence of this fact it is therefore presumable that diseases such as cancer, harboring DNA copy number alterations (CNAs), will have a symptomatology strongly dependent on modules of functionally-related genes rather than on a unique &amp;quot;important&amp;quot; gene.&lt;/p&gt; &lt;h4&gt;Methods&lt;/h4&gt; &lt;p&gt;We carried out a systematic analysis of more than 140,000 observations of CNAs in cancers and searched by enrichments in gene functional modules associated to high frequencies of loss or gains.&lt;/p&gt; &lt;h4&gt;Results&lt;/h4&gt; &lt;p&gt;The analysis of CNAs in cancers clearly demonstrates the existence of a significant pattern of loss of gene modules functionally related to cancer initiation and progression along with the amplification of modules of genes related to unspecific defense against xenobiotics (probably chemotherapeutical agents). With the extension of this analysis to an Array-CGH dataset (glioblastomas) from The Cancer Genome Atlas we demonstrate the validity of this approach to investigate the functional impact of CNAs.&lt;/p&gt; &lt;h4&gt;Conclusions&lt;/h4&gt; &lt;p&gt;The presented results indicate promising clinical and therapeutic implications. Our findings also directly point out to the necessity of adopting a function-centric, rather a gene-centric, view in the understanding of phenotypes or diseases harboring CNAs.&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Research article</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yung, Sun</style></author><author><style face="normal" font="default" size="100%">Ledran, Maria</style></author><author><style face="normal" font="default" size="100%">Moreno-Gimeno, Inmaculada</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Dimmick, Ian</style></author><author><style face="normal" font="default" size="100%">Slater, Nicholas J</style></author><author><style face="normal" font="default" size="100%">Marenah, Lamin</style></author><author><style face="normal" font="default" size="100%">Real, Pedro J</style></author><author><style face="normal" font="default" size="100%">Paraskevopoulou, Iliana</style></author><author><style face="normal" font="default" size="100%">Bisbal, Viviana</style></author><author><style face="normal" font="default" size="100%">Burks, Deborah</style></author><author><style face="normal" font="default" size="100%">Santibanez-Koref, Mauro</style></author><author><style face="normal" font="default" size="100%">Moreno, Ruben</style></author><author><style face="normal" font="default" size="100%">Mountford, Joanne</style></author><author><style face="normal" font="default" size="100%">Menendez, Pablo</style></author><author><style face="normal" font="default" size="100%">Armstrong, Lyle</style></author><author><style face="normal" font="default" size="100%">Lako, Majlinda</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Large-scale transcriptional profiling and functional assays reveal important roles for Rho-GTPase signalling and SCL during haematopoietic differentiation of human embryonic stem cells.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Mol Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Mol Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Acute Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Anemia, Hemolytic</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Basic Helix-Loop-Helix Transcription Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Differentiation</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Line</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Lineage</style></keyword><keyword><style  face="normal" font="default" size="100%">Cluster Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Embryonic Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Erythroid Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Flow Cytometry</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Hematopoietic Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Myeloid Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Paracrine Communication</style></keyword><keyword><style  face="normal" font="default" size="100%">Proto-Oncogene Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Reverse Transcriptase Polymerase Chain Reaction</style></keyword><keyword><style  face="normal" font="default" size="100%">rho GTP-Binding Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Stem Cell Transplantation</style></keyword><keyword><style  face="normal" font="default" size="100%">T-Cell Acute Lymphocytic Leukemia Protein 1</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Dec 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">4932-46</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding the transcriptional cues that direct differentiation of human embryonic stem cells (hESCs) and human-induced pluripotent stem cells to defined and functional cell types is essential for future clinical applications. In this study, we have compared transcriptional profiles of haematopoietic progenitors derived from hESCs at various developmental stages of a feeder- and serum-free differentiation method and show that the largest transcriptional changes occur during the first 4 days of differentiation. Data mining on the basis of molecular function revealed Rho-GTPase signalling as a key regulator of differentiation. Inhibition of this pathway resulted in a significant reduction in the numbers of emerging haematopoietic progenitors throughout the differentiation window, thereby uncovering a previously unappreciated role for Rho-GTPase signalling during human haematopoietic development. Our analysis indicated that SCL was the 11th most upregulated transcript during the first 4 days of the hESC differentiation process. Overexpression of SCL in hESCs promoted differentiation to meso-endodermal lineages, the emergence of haematopoietic and erythro-megakaryocytic progenitors and accelerated erythroid differentiation. Importantly, intrasplenic transplantation of SCL-overexpressing hESC-derived haematopoietic cells enhanced recovery from induced acute anaemia without significant cell engraftment, suggesting a paracrine-mediated effect.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">24</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/21937587?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Garrido-Gomez, Tamara</style></author><author><style face="normal" font="default" size="100%">Dominguez, Francisco</style></author><author><style face="normal" font="default" size="100%">Lopez, Juan Antonio</style></author><author><style face="normal" font="default" size="100%">Camafeita, Emilio</style></author><author><style face="normal" font="default" size="100%">Quiñonero, Alicia</style></author><author><style face="normal" font="default" size="100%">Martinez-Conejero, Jose Antonio</style></author><author><style face="normal" font="default" size="100%">Pellicer, Antonio</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Simon, Carlos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modeling human endometrial decidualization from the interaction between proteome and secretome.</style></title><secondary-title><style face="normal" font="default" size="100%">The Journal of clinical endocrinology and metabolism</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Mar</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">96</style></volume><pages><style face="normal" font="default" size="100%">706-16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Decidualization of the human endometrium, which involves morphological and biochemical modifications of the endometrial stromal cells (ESCs), is a prerequisite for adequate trophoblast invasion and placenta formation.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jimenez, Rafael C</style></author><author><style face="normal" font="default" size="100%">Salazar, Gustavo A</style></author><author><style face="normal" font="default" size="100%">Gel, Bernat</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Mulder, Nicola</style></author><author><style face="normal" font="default" size="100%">Corpas, Manuel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">myKaryoView: a light-weight client for visualization of genomic data.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer Graphics</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Annotation</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">e26345</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Distributed Annotation System (DAS) is a protocol for easy sharing and integration of biological annotations. In order to visualize feature annotations in a genomic context a client is required. Here we present myKaryoView, a simple light-weight DAS tool for visualization of genomic annotation. myKaryoView has been specifically configured to help analyse data derived from personal genomics, although it can also be used as a generic genome browser visualization. Several well-known data sources are provided to facilitate comparison of known genes and normal variation regions. The navigation experience is enhanced by simultaneous rendering of different levels of detail across chromosomes. A simple interface is provided to allow searches for any SNP, gene or chromosomal region. User-defined DAS data sources may also be added when querying the system. We demonstrate myKaryoView capabilities for adding user-defined sources with a set of genetic profiles of family-related individuals downloaded directly from 23andMe. myKaryoView is a web tool for visualization of genomic data specifically designed for direct-to-consumer genomic data that uses publicly available data distributed throughout the Internet. It does not require data to be held locally and it is capable of rendering any feature as long as it conforms to DAS specifications. Configuration and addition of sources to myKaryoView can be done through the interface. Here we show a proof of principle of myKaryoView's ability to display personal genomics data with 23andMe genome data sources. The tool is available at: http://mykaryoview.com.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/22046276?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">García-Alcalde, Fernando</style></author><author><style face="normal" font="default" size="100%">García-López, Federico</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data.</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics (Oxford, England)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Jan 1</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">137-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The development of the omics technologies such as transcriptomics, proteomics and metabolomics has made possible the realization of systems biology studies where biological systems are interrogated at different levels of biochemical activity (gene expression, protein activity and/or metabolite concentration). An effective approach to the analysis of these complex datasets is the joined visualization of the disparate biomolecular data on the framework of known biological pathways.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sánchez, Rubén</style></author><author><style face="normal" font="default" size="100%">Serra, François</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Pulido, Luis</style></author><author><style face="normal" font="default" size="100%">De Maria, Alejandro</style></author><author><style face="normal" font="default" size="100%">Capella-Gutíerrez, Salvador</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, Jaime</style></author><author><style face="normal" font="default" size="100%">Gabaldón, Toni</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Dopazo, Hernán</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Phylemon 2.0: a suite of web-tools for molecular evolution, phylogenetics, phylogenomics and hypotheses testing.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Evolution, Molecular</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Alignment</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">39</style></volume><pages><style face="normal" font="default" size="100%">W470-4</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Phylemon 2.0 is a new release of the suite of web tools for molecular evolution, phylogenetics, phylogenomics and hypotheses testing. It has been designed as a response to the increasing demand of molecular sequence analyses for experts and non-expert users. Phylemon 2.0 has several unique features that differentiates it from other similar web resources: (i) it offers an integrated environment that enables evolutionary analyses, format conversion, file storage and edition of results; (ii) it suggests further analyses, thereby guiding the users through the web server; and (iii) it allows users to design and save phylogenetic pipelines to be used over multiple genes (phylogenomics). Altogether, Phylemon 2.0 integrates a suite of 30 tools covering sequence alignment reconstruction and trimming; tree reconstruction, visualization and manipulation; and evolutionary hypotheses testing.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Web Server issue</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/21646336?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cardona, Fernando</style></author><author><style face="normal" font="default" size="100%">Sánchez-Mut, Jose Vicente</style></author><author><style face="normal" font="default" size="100%">Dopazo, Hernán</style></author><author><style face="normal" font="default" size="100%">Pérez-Tur, Jordi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Phylogenetic and in silico structural analysis of the Parkinson disease-related kinase PINK1.</style></title><secondary-title><style face="normal" font="default" size="100%">Human mutation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">369-78</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Parkinson disease (PD) is the second most common neurodegenerative disorder and is characterized by the loss of dopaminergic neurons in the substantia nigra. Mutations in PINK1 were shown to cause recessive familial PD, and today are proposed to be associated with the disease via mitochondrial dysfunction and oxidative damage. The PINK1 gene comprises eight exons, which encode a ubiquitously expressed 581 amino acid protein that contains an N-terminal mitochondrial targeting domain and a serine/threonine protein kinase. To better understand the relationship between PINK1 and PD we have first analyzed the evolutionary history of the gene showing its late emergence in evolution. In addition, we have modeled the three-dimensional structure of PINK1 and found some evidences that help to explain the effect of some PD-related mutations in this protein’s function.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Durban, Jordi</style></author><author><style face="normal" font="default" size="100%">Juárez, Paula</style></author><author><style face="normal" font="default" size="100%">Angulo, Yamileth</style></author><author><style face="normal" font="default" size="100%">Lomonte, Bruno</style></author><author><style face="normal" font="default" size="100%">Flores-Diaz, Marietta</style></author><author><style face="normal" font="default" size="100%">Alape-Girón, Alberto</style></author><author><style face="normal" font="default" size="100%">Sasa, Mahmood</style></author><author><style face="normal" font="default" size="100%">Sanz, Libia</style></author><author><style face="normal" font="default" size="100%">Gutiérrez, José M</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Calvete, Juan J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Profiling the venom gland transcriptomes of Costa Rican snakes by 454 pyrosequencing.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC genomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">259</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A long term research goal of venomics, of applied importance for improving current antivenom therapy, but also for drug discovery, is to understand the pharmacological potential of venoms. Individually or combined, proteomic and transcriptomic studies have demonstrated their feasibility to explore in depth the molecular diversity of venoms. In the absence of genome sequence, transcriptomes represent also valuable searchable databases for proteomic projects.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Marigorta, Urko M</style></author><author><style face="normal" font="default" size="100%">Lao, Oscar</style></author><author><style face="normal" font="default" size="100%">Casals, Ferran</style></author><author><style face="normal" font="default" size="100%">Calafell, Francesc</style></author><author><style face="normal" font="default" size="100%">Morcillo-Suarez, Carlos</style></author><author><style face="normal" font="default" size="100%">Faria, Rui</style></author><author><style face="normal" font="default" size="100%">Bosch, Elena</style></author><author><style face="normal" font="default" size="100%">Serra, François</style></author><author><style face="normal" font="default" size="100%">Bertranpetit, Jaume</style></author><author><style face="normal" font="default" size="100%">Dopazo, Hernán</style></author><author><style face="normal" font="default" size="100%">Navarro, Arcadi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Recent human evolution has shaped geographical differences in susceptibility to disease.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC genomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">55</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Searching for associations between genetic variants and complex diseases has been a very active area of research for over two decades. More than 51,000 potential associations have been studied and published, a figure that keeps increasing, especially with the recent explosion of array-based Genome-Wide Association Studies. Even if the number of true associations described so far is high, many of the putative risk variants detected so far have failed to be consistently replicated and are widely considered false positives. Here, we focus on the world-wide patterns of replicability of published association studies.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martín-Trillo, Mar</style></author><author><style face="normal" font="default" size="100%">Grandío, Eduardo González</style></author><author><style face="normal" font="default" size="100%">Serra, François</style></author><author><style face="normal" font="default" size="100%">Marcel, Fabien</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Buey, María Luisa</style></author><author><style face="normal" font="default" size="100%">Schmitz, Gregor</style></author><author><style face="normal" font="default" size="100%">Theres, Klaus</style></author><author><style face="normal" font="default" size="100%">Bendahmane, Abdelhafid</style></author><author><style face="normal" font="default" size="100%">Dopazo, Hernán</style></author><author><style face="normal" font="default" size="100%">Cubas, Pilar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Role of tomato BRANCHED1-like genes in the control of shoot branching.</style></title><secondary-title><style face="normal" font="default" size="100%">The Plant journal : for cell and molecular biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Aug</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">67</style></volume><pages><style face="normal" font="default" size="100%">701-14</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In angiosperms, shoot branching greatly determines overall plant architecture and affects fundamental aspects of plant life. Branching patterns are determined by genetic pathways conserved widely across angiosperms. In Arabidopsis thaliana (Brassicaceae, Rosidae) BRANCHED1 (BRC1) plays a central role in this process, acting locally to arrest axillary bud growth. In tomato (Solanum lycopersicum, Solanaceae, Asteridae) we have identified two BRC1-like paralogues, SlBRC1a and SlBRC1b. These genes are expressed in arrested axillary buds and both are down-regulated upon bud activation, although SlBRC1a is transcribed at much lower levels than SlBRC1b. Alternative splicing of SlBRC1a renders two transcripts that encode two BRC1-like proteins with different C-t domains due to a 3’-terminal frameshift. The phenotype of loss-of-function lines suggests that SlBRC1b has retained the ancestral role of BRC1 in shoot branch suppression. We have isolated the BRC1a and BRC1b genes of other Solanum species and have studied their evolution rates across the lineages. These studies indicate that, after duplication of an ancestral BRC1-like gene, BRC1b genes continued to evolve under a strong purifying selection that was consistent with the conserved function of SlBRC1b in shoot branching control. In contrast, the coding sequences of Solanum BRC1a genes have evolved at a higher evolution rate. Branch-site tests indicate that this difference does not reflect relaxation but rather positive selective pressure for adaptation.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cuenca-Bono, Bernardo</style></author><author><style face="normal" font="default" size="100%">García-Molinero, Varinia</style></author><author><style face="normal" font="default" size="100%">Pascual-García, Pau</style></author><author><style face="normal" font="default" size="100%">Dopazo, Hernán</style></author><author><style face="normal" font="default" size="100%">Llopis, Ana</style></author><author><style face="normal" font="default" size="100%">Vilardell, Josep</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Navarro, Susana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SUS1 introns are required for efficient mRNA nuclear export in yeast.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic acids research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Oct 1</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">39</style></volume><pages><style face="normal" font="default" size="100%">8599-611</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Efficient coupling between mRNA synthesis and export is essential for gene expression. Sus1/ENY2, a component of the SAGA and TREX-2 complexes, is involved in both transcription and mRNA export. While most yeast genes lack introns, we previously reported that yeast SUS1 bears two. Here we show that this feature is evolutionarily conserved and critical for Sus1 function. We determine that while SUS1 splicing is inefficient, it responds to cellular conditions, and intronic mutations either promoting or blocking splicing lead to defects in mRNA export and cell growth. Consistent with this, we find that an intron-less SUS1 only partially rescues sus1&amp;Delta; phenotypes. Remarkably, splicing of each SUS1 intron is also affected by the presence of the other and by SUS1 exonic sequences. Moreover, by following SUS1 RNA and protein levels we establish that nonsense-mediated decay (NMD) pathway and the splicing factor Mud2 both play a role in SUS1 expression. Our data (and those of the accompanying work by Hossain et al.) provide evidence of the involvement of splicing, translation, and decay in the regulation of early events in mRNP biogenesis; and imply the additional requirement for a balance in splicing isoforms from a single gene.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Baù, Davide</style></author><author><style face="normal" font="default" size="100%">Sanyal, Amartya</style></author><author><style face="normal" font="default" size="100%">Lajoie, Bryan R</style></author><author><style face="normal" font="default" size="100%">Capriotti, Emidio</style></author><author><style face="normal" font="default" size="100%">Byron, Meg</style></author><author><style face="normal" font="default" size="100%">Lawrence, Jeanne B</style></author><author><style face="normal" font="default" size="100%">Dekker, Job</style></author><author><style face="normal" font="default" size="100%">Marti-Renom, Marc A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The three-dimensional folding of the α-globin gene domain reveals formation of chromatin globules.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature structural &amp; molecular biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">107-14</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We developed a general approach that combines chromosome conformation capture carbon copy (5C) with the Integrated Modeling Platform (IMP) to generate high-resolution three-dimensional models of chromatin at the megabase scale. We applied this approach to the ENm008 domain on human chromosome 16, containing the &amp;alpha;-globin locus, which is expressed in K562 cells and silenced in lymphoblastoid cells (GM12878). The models accurately reproduce the known looping interactions between the &amp;alpha;-globin genes and their distal regulatory elements. Further, we find using our approach that the domain folds into a single globular conformation in GM12878 cells, whereas two globules are formed in K562 cells. The central cores of these globules are enriched for transcribed genes, whereas nontranscribed chromatin is more peripheral. We propose that globule formation represents a higher-order folding state related to clustering of transcribed genes around shared transcription machineries, as previously observed by microscopy.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Pulido, Luis</style></author><author><style face="normal" font="default" size="100%">Madeira, Sara C</style></author><author><style face="normal" font="default" size="100%">Goetz, Stefan</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Pascual-Montano, Alberto</style></author><author><style face="normal" font="default" size="100%">Nogales-Cadenas, Ruben</style></author><author><style face="normal" font="default" size="100%">Santoyo, Javier</style></author><author><style face="normal" font="default" size="100%">García, Francisco</style></author><author><style face="normal" font="default" size="100%">Marbà, Martina</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Research</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword><keyword><style  face="normal" font="default" size="100%">gene expression</style></keyword><keyword><style  face="normal" font="default" size="100%">genotyping</style></keyword><keyword><style  face="normal" font="default" size="100%">gepas</style></keyword><keyword><style  face="normal" font="default" size="100%">GSA</style></keyword><keyword><style  face="normal" font="default" size="100%">GWAS</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 May 16</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/content/38/suppl_2/W210.full</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">W210-W213. Featured in NAR</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Babelomics is a response to the growing necessity of integrating and analyzing different types of genomic data in an environment that allows an easy functional interpretation of the results. Babelomics includes a complete suite of methods for the analysis of gene expression data that include normalization (covering most commercial platforms), pre-processing, differential gene expression (case-controls, multiclass, survival or continuous values), predictors, clustering; large-scale genotyping assays (case controls and TDTs, and allows population stratification analysis and correction). All these genomic data analysis facilities are integrated and connected to multiple options for the functional interpretation of the experiments. Different methods of functional enrichment or gene set enrichment can be used to understand the functional basis of the experiment analyzed. Many sources of biological information, which include functional (GO, KEGG, Biocarta, Reactome, etc.), regulatory (Transfac, Jaspar, ORegAnno, miRNAs, etc.), text-mining or protein-protein interaction modules can be used for this purpose. Finally a tool for the de novo functional annotation of sequences has been included in the system. This provides support for the functional analysis of non-model species. Mirrors of Babelomics or command line execution of their individual components are now possible. Babelomics is available at http://www.babelomics.org.&lt;/p&gt;</style></abstract><section><style face="normal" font="default" size="100%">Featured in NAR</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Javierre, Biola M</style></author><author><style face="normal" font="default" size="100%">Fernandez, Agustin F</style></author><author><style face="normal" font="default" size="100%">Richter, Julia</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Martin-Subero, J Ignacio</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Ubreva, Javier</style></author><author><style face="normal" font="default" size="100%">Berdasco, Maria</style></author><author><style face="normal" font="default" size="100%">Fraga, Mario F</style></author><author><style face="normal" font="default" size="100%">O’Hanlon, Terrance P</style></author><author><style face="normal" font="default" size="100%">Rider, Lisa G</style></author><author><style face="normal" font="default" size="100%">Jacinto, Filipe V</style></author><author><style face="normal" font="default" size="100%">Lopez-Longo, F Javier</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Forn, Marta</style></author><author><style face="normal" font="default" size="100%">Peinado, Miguel A</style></author><author><style face="normal" font="default" size="100%">Carreño, Luis</style></author><author><style face="normal" font="default" size="100%">Sawalha, Amr H</style></author><author><style face="normal" font="default" size="100%">Harley, John B</style></author><author><style face="normal" font="default" size="100%">Siebert, Reiner</style></author><author><style face="normal" font="default" size="100%">Esteller, Manel</style></author><author><style face="normal" font="default" size="100%">Miller, Frederick W</style></author><author><style face="normal" font="default" size="100%">Ballestar, Esteban</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Changes in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosus.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Feb</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">170-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Monozygotic (MZ) twins are partially concordant for most complex diseases, including autoimmune disorders. Whereas phenotypic concordance can be used to study heritability, discordance suggests the role of non-genetic factors. In autoimmune diseases, environmentally driven epigenetic changes are thought to contribute to their etiology. Here we report the first high-throughput and candidate sequence analyses of DNA methylation to investigate discordance for autoimmune disease in twins. We used a cohort of MZ twins discordant for three diseases whose clinical signs often overlap: systemic lupus erythematosus (SLE), rheumatoid arthritis, and dermatomyositis. Only MZ twins discordant for SLE featured widespread changes in the DNA methylation status of a significant number of genes. Gene ontology analysis revealed enrichment in categories associated with immune function. Individual analysis confirmed the existence of DNA methylation and expression changes in genes relevant to SLE pathogenesis. These changes occurred in parallel with a global decrease in the 5-methylcytosine content that was concomitantly accompanied with changes in DNA methylation and expression levels of ribosomal RNA genes, although no changes in repetitive sequences were found. Our findings not only identify potentially relevant DNA methylation markers for the clinical characterization of SLE patients but also support the notion that epigenetic changes may be critical in the clinical manifestations of autoimmune disease.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bonifaci, Núria</style></author><author><style face="normal" font="default" size="100%">Górski, Bohdan</style></author><author><style face="normal" font="default" size="100%">Masojć, Bartlomiej</style></author><author><style face="normal" font="default" size="100%">Wokołorczyk, Dominika</style></author><author><style face="normal" font="default" size="100%">Jakubowska, Anna</style></author><author><style face="normal" font="default" size="100%">Dębniak, Tadeusz</style></author><author><style face="normal" font="default" size="100%">Berenguer, Antoni</style></author><author><style face="normal" font="default" size="100%">Serra Musach, Jordi</style></author><author><style face="normal" font="default" size="100%">Brunet, Joan</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Narod, Steven A</style></author><author><style face="normal" font="default" size="100%">Lubiński, Jan</style></author><author><style face="normal" font="default" size="100%">Lázaro, Conxi</style></author><author><style face="normal" font="default" size="100%">Cybulski, Cezary</style></author><author><style face="normal" font="default" size="100%">Pujana, Miguel Angel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring the link between germline and somatic genetic alterations in breast carcinogenesis.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Bone Morphogenetic Protein Receptors, Type I</style></keyword><keyword><style  face="normal" font="default" size="100%">Breast</style></keyword><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Calcium-Calmodulin-Dependent Protein Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Case-Control Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Cyclin-Dependent Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease Progression</style></keyword><keyword><style  face="normal" font="default" size="100%">Estrogen Receptor alpha</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Germ-Line Mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Odds Ratio</style></keyword><keyword><style  face="normal" font="default" size="100%">Poland</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Serine-Threonine Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein-Tyrosine Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptor Protein-Tyrosine Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptor, EphA3</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptor, EphA7</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptor, EphB1</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Nov 22</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">e14078</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Recent genome-wide association studies (GWASs) have identified candidate genes contributing to cancer risk through low-penetrance mutations. Many of these genes were unexpected and, intriguingly, included well-known players in carcinogenesis at the somatic level. To assess the hypothesis of a germline-somatic link in carcinogenesis, we evaluated the distribution of somatic gene labels within the ordered results of a breast cancer risk GWAS. This analysis suggested frequent influence on risk of genetic variation in loci encoding for &quot;driver kinases&quot; (i.e., kinases encoded by genes that showed higher somatic mutation rates than expected by chance and, therefore, whose deregulation may contribute to cancer development and/or progression). Assessment of these predictions using a population-based case-control study in Poland replicated the association for rs3732568 in EPHB1 (odds ratio (OR) = 0.79; 95% confidence interval (CI): 0.63-0.98; P(trend) = 0.031). Analyses by early age at diagnosis and by estrogen receptor α (ERα) tumor status indicated potential associations for rs6852678 in CDKL2 (OR = 0.32, 95% CI: 0.10-1.00; P(recessive) = 0.044) and rs10878640 in DYRK2 (OR = 2.39, 95% CI: 1.32-4.30; P(dominant) = 0.003), and for rs12765929, rs9836340, rs4707795 in BMPR1A, EPHA3 and EPHA7, respectively (ERα tumor status P(interaction)&lt;0.05). The identification of three novel candidates as EPH receptor genes might indicate a link between perturbed compartmentalization of early neoplastic lesions and breast cancer risk and progression. Together, these data may lay the foundations for replication in additional populations and could potentially increase our knowledge of the underlying molecular mechanisms of breast carcinogenesis.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/21124932?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shi, W</style></author><author><style face="normal" font="default" size="100%">Bessarabova, M</style></author><author><style face="normal" font="default" size="100%">Dosymbekov, D</style></author><author><style face="normal" font="default" size="100%">Dezso, Z</style></author><author><style face="normal" font="default" size="100%">Nikolskaya, T</style></author><author><style face="normal" font="default" size="100%">Dudoladova, M</style></author><author><style face="normal" font="default" size="100%">Serebryiskaya, T</style></author><author><style face="normal" font="default" size="100%">Bugrim, A</style></author><author><style face="normal" font="default" size="100%">Guryanov, A</style></author><author><style face="normal" font="default" size="100%">Brennan, R J</style></author><author><style face="normal" font="default" size="100%">Shah, R</style></author><author><style face="normal" font="default" size="100%">Dopazo, J</style></author><author><style face="normal" font="default" size="100%">Chen, M</style></author><author><style face="normal" font="default" size="100%">Deng, Y</style></author><author><style face="normal" font="default" size="100%">Shi, T</style></author><author><style face="normal" font="default" size="100%">Jurman, G</style></author><author><style face="normal" font="default" size="100%">Furlanello, C</style></author><author><style face="normal" font="default" size="100%">Thomas, R S</style></author><author><style face="normal" font="default" size="100%">Corton, J C</style></author><author><style face="normal" font="default" size="100%">Tong, W</style></author><author><style face="normal" font="default" size="100%">Shi, L</style></author><author><style face="normal" font="default" size="100%">Nikolsky, Y</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes.</style></title><secondary-title><style face="normal" font="default" size="100%">Pharmacogenomics J</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Pharmacogenomics J</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Endpoint Determination</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural Networks, Computer</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Predictive Value of Tests</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Quality Control</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Aug</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">310-23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Gene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/20676069?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prado-Lopez, Sonia</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author><author><style face="normal" font="default" size="100%">Armiñán, Ana</style></author><author><style face="normal" font="default" size="100%">Martínez-Losa, Magdalena</style></author><author><style face="normal" font="default" size="100%">Escobedo-Lucea, Carmen</style></author><author><style face="normal" font="default" size="100%">Gandia, Carolina</style></author><author><style face="normal" font="default" size="100%">Tarazona, Sonia</style></author><author><style face="normal" font="default" size="100%">Melguizo, Dario</style></author><author><style face="normal" font="default" size="100%">Blesa, David</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Sanz-González, Silvia</style></author><author><style face="normal" font="default" size="100%">Sepúlveda, Pilar</style></author><author><style face="normal" font="default" size="100%">Götz, Stefan</style></author><author><style face="normal" font="default" size="100%">O'Connor, José Enrique</style></author><author><style face="normal" font="default" size="100%">Moreno, Ruben</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Burks, Deborah J</style></author><author><style face="normal" font="default" size="100%">Stojkovic, Miodrag</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hypoxia promotes efficient differentiation of human embryonic stem cells to functional endothelium.</style></title><secondary-title><style face="normal" font="default" size="100%">Stem Cells</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Stem Cells</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Angiopoietin-1</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">biomarkers</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Culture Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Differentiation</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Hypoxia</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Transplantation</style></keyword><keyword><style  face="normal" font="default" size="100%">Cells, Cultured</style></keyword><keyword><style  face="normal" font="default" size="100%">Down-Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Embryonic Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Endothelial Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Myocardial Infarction</style></keyword><keyword><style  face="normal" font="default" size="100%">Neovascularization, Physiologic</style></keyword><keyword><style  face="normal" font="default" size="100%">Oxygen</style></keyword><keyword><style  face="normal" font="default" size="100%">Pluripotent Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Rats</style></keyword><keyword><style  face="normal" font="default" size="100%">Rats, Nude</style></keyword><keyword><style  face="normal" font="default" size="100%">Vascular Endothelial Growth Factor A</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Mar 31</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">407-18</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Early development of mammalian embryos occurs in an environment of relative hypoxia. Nevertheless, human embryonic stem cells (hESC), which are derived from the inner cell mass of blastocyst, are routinely cultured under the same atmospheric conditions (21% O(2)) as somatic cells. We hypothesized that O(2) levels modulate gene expression and differentiation potential of hESC, and thus, we performed gene profiling of hESC maintained under normoxic or hypoxic (1% or 5% O(2)) conditions. Our analysis revealed that hypoxia downregulates expression of pluripotency markers in hESC but increases significantly the expression of genes associated with angio- and vasculogenesis including vascular endothelial growth factor and angiopoitein-like proteins. Consequently, we were able to efficiently differentiate hESC to functional endothelial cells (EC) by varying O(2) levels; after 24 hours at 5% O(2), more than 50% of cells were CD34+. Transplantation of resulting endothelial-like cells improved both systolic function and fractional shortening in a rodent model of myocardial infarction. Moreover, analysis of the infarcted zone revealed that transplanted EC reduced the area of fibrous scar tissue by 50%. Thus, use of hypoxic conditions to specify the endothelial lineage suggests a novel strategy for cellular therapies aimed at repair of damaged vasculature in pathologies such as cerebral ischemia and myocardial infarction.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/20049902?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Németh, Attila</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Santoyo-López, Javier</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Péterfia, Bálint</style></author><author><style face="normal" font="default" size="100%">Solovei, Irina</style></author><author><style face="normal" font="default" size="100%">Cremer, Thomas</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Längst, Gernot</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Initial genomics of the human nucleolus.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS genetics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">nucleolus</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000889</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">e1000889</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We report for the first time the genomics of a nuclear compartment of the eukaryotic cell. 454 sequencing and microarray analysis revealed the pattern of nucleolus-associated chromatin domains (NADs) in the linear human genome and identified different gene families and certain satellite repeats as the major building blocks of NADs, which constitute about 4% of the genome. Bioinformatic evaluation showed that NAD-localized genes take part in specific biological processes, like the response to other organisms, odor perception, and tissue development. 3D FISH and immunofluorescence experiments illustrated the spatial distribution of NAD-specific chromatin within interphase nuclei and its alteration upon transcriptional changes. Altogether, our findings describe the nature of DNA sequences associated with the human nucleolus and provide insights into the function of the nucleolus in genome organization and establishment of nuclear architecture.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shi, Leming</style></author><author><style face="normal" font="default" size="100%">Campbell, Gregory</style></author><author><style face="normal" font="default" size="100%">Jones, Wendell D</style></author><author><style face="normal" font="default" size="100%">Campagne, Fabien</style></author><author><style face="normal" font="default" size="100%">Wen, Zhining</style></author><author><style face="normal" font="default" size="100%">Walker, Stephen J</style></author><author><style face="normal" font="default" size="100%">Su, Zhenqiang</style></author><author><style face="normal" font="default" size="100%">Chu, Tzu-Ming</style></author><author><style face="normal" font="default" size="100%">Goodsaid, Federico M</style></author><author><style face="normal" font="default" size="100%">Pusztai, Lajos</style></author><author><style face="normal" font="default" size="100%">Shaughnessy, John D</style></author><author><style face="normal" font="default" size="100%">Oberthuer, André</style></author><author><style face="normal" font="default" size="100%">Thomas, Russell S</style></author><author><style face="normal" font="default" size="100%">Paules, Richard S</style></author><author><style face="normal" font="default" size="100%">Fielden, Mark</style></author><author><style face="normal" font="default" size="100%">Barlogie, Bart</style></author><author><style face="normal" font="default" size="100%">Chen, Weijie</style></author><author><style face="normal" font="default" size="100%">Du, Pan</style></author><author><style face="normal" font="default" size="100%">Fischer, Matthias</style></author><author><style face="normal" font="default" size="100%">Furlanello, Cesare</style></author><author><style face="normal" font="default" size="100%">Gallas, Brandon D</style></author><author><style face="normal" font="default" size="100%">Ge, Xijin</style></author><author><style face="normal" font="default" size="100%">Megherbi, Dalila B</style></author><author><style face="normal" font="default" size="100%">Symmans, W Fraser</style></author><author><style face="normal" font="default" size="100%">Wang, May D</style></author><author><style face="normal" font="default" size="100%">Zhang, John</style></author><author><style face="normal" font="default" size="100%">Bitter, Hans</style></author><author><style face="normal" font="default" size="100%">Brors, Benedikt</style></author><author><style face="normal" font="default" size="100%">Bushel, Pierre R</style></author><author><style face="normal" font="default" size="100%">Bylesjo, Max</style></author><author><style face="normal" font="default" size="100%">Chen, Minjun</style></author><author><style face="normal" font="default" size="100%">Cheng, Jie</style></author><author><style face="normal" font="default" size="100%">Cheng, Jing</style></author><author><style face="normal" font="default" size="100%">Chou, Jeff</style></author><author><style face="normal" font="default" size="100%">Davison, Timothy S</style></author><author><style face="normal" font="default" size="100%">Delorenzi, Mauro</style></author><author><style face="normal" font="default" size="100%">Deng, Youping</style></author><author><style face="normal" font="default" size="100%">Devanarayan, Viswanath</style></author><author><style face="normal" font="default" size="100%">Dix, David J</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Dorff, Kevin C</style></author><author><style face="normal" font="default" size="100%">Elloumi, Fathi</style></author><author><style face="normal" font="default" size="100%">Fan, Jianqing</style></author><author><style face="normal" font="default" size="100%">Fan, Shicai</style></author><author><style face="normal" font="default" size="100%">Fan, Xiaohui</style></author><author><style face="normal" font="default" size="100%">Fang, Hong</style></author><author><style face="normal" font="default" size="100%">Gonzaludo, Nina</style></author><author><style face="normal" font="default" size="100%">Hess, Kenneth R</style></author><author><style face="normal" font="default" size="100%">Hong, Huixiao</style></author><author><style face="normal" font="default" size="100%">Huan, Jun</style></author><author><style face="normal" font="default" size="100%">Irizarry, Rafael A</style></author><author><style face="normal" font="default" size="100%">Judson, Richard</style></author><author><style face="normal" font="default" size="100%">Juraeva, Dilafruz</style></author><author><style face="normal" font="default" size="100%">Lababidi, Samir</style></author><author><style face="normal" font="default" size="100%">Lambert, Christophe G</style></author><author><style face="normal" font="default" size="100%">Li, Li</style></author><author><style face="normal" font="default" size="100%">Li, Yanen</style></author><author><style face="normal" font="default" size="100%">Li, Zhen</style></author><author><style face="normal" font="default" size="100%">Lin, Simon M</style></author><author><style face="normal" font="default" size="100%">Liu, Guozhen</style></author><author><style face="normal" font="default" size="100%">Lobenhofer, Edward K</style></author><author><style face="normal" font="default" size="100%">Luo, Jun</style></author><author><style face="normal" font="default" size="100%">Luo, Wen</style></author><author><style face="normal" font="default" size="100%">McCall, Matthew N</style></author><author><style face="normal" font="default" size="100%">Nikolsky, Yuri</style></author><author><style face="normal" font="default" size="100%">Pennello, Gene A</style></author><author><style face="normal" font="default" size="100%">Perkins, Roger G</style></author><author><style face="normal" font="default" size="100%">Philip, Reena</style></author><author><style face="normal" font="default" size="100%">Popovici, Vlad</style></author><author><style face="normal" font="default" size="100%">Price, Nathan D</style></author><author><style face="normal" font="default" size="100%">Qian, Feng</style></author><author><style face="normal" font="default" size="100%">Scherer, Andreas</style></author><author><style face="normal" font="default" size="100%">Shi, Tieliu</style></author><author><style face="normal" font="default" size="100%">Shi, Weiwei</style></author><author><style face="normal" font="default" size="100%">Sung, Jaeyun</style></author><author><style face="normal" font="default" size="100%">Thierry-Mieg, Danielle</style></author><author><style face="normal" font="default" size="100%">Thierry-Mieg, Jean</style></author><author><style face="normal" font="default" size="100%">Thodima, Venkata</style></author><author><style face="normal" font="default" size="100%">Trygg, Johan</style></author><author><style face="normal" font="default" size="100%">Vishnuvajjala, Lakshmi</style></author><author><style face="normal" font="default" size="100%">Wang, Sue Jane</style></author><author><style face="normal" font="default" size="100%">Wu, Jianping</style></author><author><style face="normal" font="default" size="100%">Wu, Yichao</style></author><author><style face="normal" font="default" size="100%">Xie, Qian</style></author><author><style face="normal" font="default" size="100%">Yousef, Waleed A</style></author><author><style face="normal" font="default" size="100%">Zhang, Liang</style></author><author><style face="normal" font="default" size="100%">Zhang, Xuegong</style></author><author><style face="normal" font="default" size="100%">Zhong, Sheng</style></author><author><style face="normal" font="default" size="100%">Zhou, Yiming</style></author><author><style face="normal" font="default" size="100%">Zhu, Sheng</style></author><author><style face="normal" font="default" size="100%">Arasappan, Dhivya</style></author><author><style face="normal" font="default" size="100%">Bao, Wenjun</style></author><author><style face="normal" font="default" size="100%">Lucas, Anne Bergstrom</style></author><author><style face="normal" font="default" size="100%">Berthold, Frank</style></author><author><style face="normal" font="default" size="100%">Brennan, Richard J</style></author><author><style face="normal" font="default" size="100%">Buness, Andreas</style></author><author><style face="normal" font="default" size="100%">Catalano, Jennifer G</style></author><author><style face="normal" font="default" size="100%">Chang, Chang</style></author><author><style face="normal" font="default" size="100%">Chen, Rong</style></author><author><style face="normal" font="default" size="100%">Cheng, Yiyu</style></author><author><style face="normal" font="default" size="100%">Cui, Jian</style></author><author><style face="normal" font="default" size="100%">Czika, Wendy</style></author><author><style face="normal" font="default" size="100%">Demichelis, Francesca</style></author><author><style face="normal" font="default" size="100%">Deng, Xutao</style></author><author><style face="normal" font="default" size="100%">Dosymbekov, Damir</style></author><author><style face="normal" font="default" size="100%">Eils, Roland</style></author><author><style face="normal" font="default" size="100%">Feng, Yang</style></author><author><style face="normal" font="default" size="100%">Fostel, Jennifer</style></author><author><style face="normal" font="default" size="100%">Fulmer-Smentek, Stephanie</style></author><author><style face="normal" font="default" size="100%">Fuscoe, James C</style></author><author><style face="normal" font="default" size="100%">Gatto, Laurent</style></author><author><style face="normal" font="default" size="100%">Ge, Weigong</style></author><author><style face="normal" font="default" size="100%">Goldstein, Darlene R</style></author><author><style face="normal" font="default" size="100%">Guo, Li</style></author><author><style face="normal" font="default" size="100%">Halbert, Donald N</style></author><author><style face="normal" font="default" size="100%">Han, Jing</style></author><author><style face="normal" font="default" size="100%">Harris, Stephen C</style></author><author><style face="normal" font="default" size="100%">Hatzis, Christos</style></author><author><style face="normal" font="default" size="100%">Herman, Damir</style></author><author><style face="normal" font="default" size="100%">Huang, Jianping</style></author><author><style face="normal" font="default" size="100%">Jensen, Roderick V</style></author><author><style face="normal" font="default" size="100%">Jiang, Rui</style></author><author><style face="normal" font="default" size="100%">Johnson, Charles D</style></author><author><style face="normal" font="default" size="100%">Jurman, Giuseppe</style></author><author><style face="normal" font="default" size="100%">Kahlert, Yvonne</style></author><author><style face="normal" font="default" size="100%">Khuder, Sadik A</style></author><author><style face="normal" font="default" size="100%">Kohl, Matthias</style></author><author><style face="normal" font="default" size="100%">Li, Jianying</style></author><author><style face="normal" font="default" size="100%">Li, Li</style></author><author><style face="normal" font="default" size="100%">Li, Menglong</style></author><author><style face="normal" font="default" size="100%">Li, Quan-Zhen</style></author><author><style face="normal" font="default" size="100%">Li, Shao</style></author><author><style face="normal" font="default" size="100%">Li, Zhiguang</style></author><author><style face="normal" font="default" size="100%">Liu, Jie</style></author><author><style face="normal" font="default" size="100%">Liu, Ying</style></author><author><style face="normal" font="default" size="100%">Liu, Zhichao</style></author><author><style face="normal" font="default" size="100%">Meng, Lu</style></author><author><style face="normal" font="default" size="100%">Madera, Manuel</style></author><author><style face="normal" font="default" size="100%">Martinez-Murillo, Francisco</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Meehan, Joseph</style></author><author><style face="normal" font="default" size="100%">Miclaus, Kelci</style></author><author><style face="normal" font="default" size="100%">Moffitt, Richard A</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Mukherjee, Piali</style></author><author><style face="normal" font="default" size="100%">Mulligan, George J</style></author><author><style face="normal" font="default" size="100%">Neville, Padraic</style></author><author><style face="normal" font="default" size="100%">Nikolskaya, Tatiana</style></author><author><style face="normal" font="default" size="100%">Ning, Baitang</style></author><author><style face="normal" font="default" size="100%">Page, Grier P</style></author><author><style face="normal" font="default" size="100%">Parker, Joel</style></author><author><style face="normal" font="default" size="100%">Parry, R Mitchell</style></author><author><style face="normal" font="default" size="100%">Peng, Xuejun</style></author><author><style face="normal" font="default" size="100%">Peterson, Ron L</style></author><author><style face="normal" font="default" size="100%">Phan, John H</style></author><author><style face="normal" font="default" size="100%">Quanz, Brian</style></author><author><style face="normal" font="default" size="100%">Ren, Yi</style></author><author><style face="normal" font="default" size="100%">Riccadonna, Samantha</style></author><author><style face="normal" font="default" size="100%">Roter, Alan H</style></author><author><style face="normal" font="default" size="100%">Samuelson, Frank W</style></author><author><style face="normal" font="default" size="100%">Schumacher, Martin M</style></author><author><style face="normal" font="default" size="100%">Shambaugh, Joseph D</style></author><author><style face="normal" font="default" size="100%">Shi, Qiang</style></author><author><style face="normal" font="default" size="100%">Shippy, Richard</style></author><author><style face="normal" font="default" size="100%">Si, Shengzhu</style></author><author><style face="normal" font="default" size="100%">Smalter, Aaron</style></author><author><style face="normal" font="default" size="100%">Sotiriou, Christos</style></author><author><style face="normal" font="default" size="100%">Soukup, Mat</style></author><author><style face="normal" font="default" size="100%">Staedtler, Frank</style></author><author><style face="normal" font="default" size="100%">Steiner, Guido</style></author><author><style face="normal" font="default" size="100%">Stokes, Todd H</style></author><author><style face="normal" font="default" size="100%">Sun, Qinglan</style></author><author><style face="normal" font="default" size="100%">Tan, Pei-Yi</style></author><author><style face="normal" font="default" size="100%">Tang, Rong</style></author><author><style face="normal" font="default" size="100%">Tezak, Zivana</style></author><author><style face="normal" font="default" size="100%">Thorn, Brett</style></author><author><style face="normal" font="default" size="100%">Tsyganova, Marina</style></author><author><style face="normal" font="default" size="100%">Turpaz, Yaron</style></author><author><style face="normal" font="default" size="100%">Vega, Silvia C</style></author><author><style face="normal" font="default" size="100%">Visintainer, Roberto</style></author><author><style face="normal" font="default" size="100%">von Frese, Juergen</style></author><author><style face="normal" font="default" size="100%">Wang, Charles</style></author><author><style face="normal" font="default" size="100%">Wang, Eric</style></author><author><style face="normal" font="default" size="100%">Wang, Junwei</style></author><author><style face="normal" font="default" size="100%">Wang, Wei</style></author><author><style face="normal" font="default" size="100%">Westermann, Frank</style></author><author><style face="normal" font="default" size="100%">Willey, James C</style></author><author><style face="normal" font="default" size="100%">Woods, Matthew</style></author><author><style face="normal" font="default" size="100%">Wu, Shujian</style></author><author><style face="normal" font="default" size="100%">Xiao, Nianqing</style></author><author><style face="normal" font="default" size="100%">Xu, Joshua</style></author><author><style face="normal" font="default" size="100%">Xu, Lei</style></author><author><style face="normal" font="default" size="100%">Yang, Lun</style></author><author><style face="normal" font="default" size="100%">Zeng, Xiao</style></author><author><style face="normal" font="default" size="100%">Zhang, Jialu</style></author><author><style face="normal" font="default" size="100%">Zhang, Li</style></author><author><style face="normal" font="default" size="100%">Zhang, Min</style></author><author><style face="normal" font="default" size="100%">Zhao, Chen</style></author><author><style face="normal" font="default" size="100%">Puri, Raj K</style></author><author><style face="normal" font="default" size="100%">Scherf, Uwe</style></author><author><style face="normal" font="default" size="100%">Tong, Weida</style></author><author><style face="normal" font="default" size="100%">Wolfinger, Russell D</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature biotechnology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Aug</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/nbt/journal/v28/n8/full/nbt.1665.html</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">827-38</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, &amp;gt;30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">E. Capriotti</style></author><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantifying the relationship between sequence and three-dimensional structure conservation in RNA.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC bioinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Jun 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">322</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;ABSTRACT: BACKGROUND: In recent years, the number of available RNA structures has rapidly grown reflecting the increased interest on RNA biology. Similarly to the studies carried out two decades ago for proteins, which gave the fundamental grounds for developing comparative protein structure prediction methods, we are now able to quantify the relationship between sequence and structure conservation in RNA. RESULTS: Here we introduce an all-against-all sequence- and three-dimensional (3D) structure-based comparison of a representative set of RNA structures, which have allowed us to quantitatively confirm that: (i) there is a measurable relationship between sequence and structure conservation that weakens for alignments resulting in below 60% sequence identity, (ii) evolution tends to conserve more RNA structure than sequence, and (iii) there is a twilight zone for RNA homology detection. DISCUSSION: The computational analysis here presented quantitatively describes the relationship between sequence and structure for RNA molecules and defines a twilight zone region for detecting RNA homology. Our work could represent the theoretical basis and limitations for future developments in comparative RNA 3D structure prediction.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nueda, Maria José</style></author><author><style face="normal" font="default" size="100%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Serial Expression Analysis: a web tool for the analysis of serial gene expression data.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Kinetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Linear Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">W239-45</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Serial transcriptomics experiments investigate the dynamics of gene expression changes associated with a quantitative variable such as time or dosage. The statistical analysis of these data implies the study of global and gene-specific expression trends, the identification of significant serial changes, the comparison of expression profiles and the assessment of transcriptional changes in terms of cellular processes. We have created the SEA (Serial Expression Analysis) suite to provide a complete web-based resource for the analysis of serial transcriptomics data. SEA offers five different algorithms based on univariate, multivariate and functional profiling strategies framed within a user-friendly interface and a project-oriented architecture to facilitate the analysis of serial gene expression data sets from different perspectives. SEA is available at sea.bioinfo.cipf.es.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Web Server issue</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/20525784?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rattei, Thomas</style></author><author><style face="normal" font="default" size="100%">Tischler, Patrick</style></author><author><style face="normal" font="default" size="100%">Götz, Stefan</style></author><author><style face="normal" font="default" size="100%">Jehl, Marc-André</style></author><author><style face="normal" font="default" size="100%">Hoser, Jonathan</style></author><author><style face="normal" font="default" size="100%">Arnold, Roland</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Mewes, Hans-Werner</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SIMAP–a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic acids research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">D223-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The prediction of protein function as well as the reconstruction of evolutionary genesis employing sequence comparison at large is still the most powerful tool in sequence analysis. Due to the exponential growth of the number of known protein sequences and the subsequent quadratic growth of the similarity matrix, the computation of the Similarity Matrix of Proteins (SIMAP) becomes a computational intensive task. The SIMAP database provides a comprehensive and up-to-date pre-calculation of the protein sequence similarity matrix, sequence-based features and sequence clusters. As of September 2009, SIMAP covers 48 million proteins and more than 23 million non-redundant sequences. Novel features of SIMAP include the expansion of the sequence space by including databases such as ENSEMBL as well as the integration of metagenomes based on their consistent processing and annotation. Furthermore, protein function predictions by Blast2GO are pre-calculated for all sequences in SIMAP and the data access and query functions have been improved. SIMAP assists biologists to query the up-to-date sequence space systematically and facilitates large-scale downstream projects in computational biology. Access to SIMAP is freely provided through the web portal for individuals (http://mips.gsf.de/simap/) and for programmatic access through DAS (http://webclu.bio.wzw.tum.de/das/) and Web-Service (http://mips.gsf.de/webservices/services/SimapService2.0?wsdl).&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jantus Lewintre, E.</style></author><author><style face="normal" font="default" size="100%">Reinoso Martin, C.</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Marin, M.</style></author><author><style face="normal" font="default" size="100%">Jose Terol, M.</style></author><author><style face="normal" font="default" size="100%">Farras, R.</style></author><author><style face="normal" font="default" size="100%">Benet, I.</style></author><author><style face="normal" font="default" size="100%">Calvete, J. J.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Garcia-Conde, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of chronic lymphotic leukemia transcriptomic profile: differences between molecular subgroups</style></title><secondary-title><style face="normal" font="default" size="100%">Leuk Lymphoma</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cancer</style></keyword><keyword><style  face="normal" font="default" size="100%">microarray data analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=19127482</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">68-79</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;B cell chronic lymphocytic leukemia (CLL) is a lymphoproliferative disorder with a variable clinical course. Patients with unmutated IgV(H) gene show a shorter progression-free and overall survival than patients with immunoglobulin heavy chain variable regions (IgV(H)) gene mutated. In addition, BCL6 mutations identify a subgroup of patients with high risk of progression. Gene expression was analysed in 36 early-stage patients using high-density microarrays. Around 150 genes differentially expressed were found according to IgV(H) mutations, whereas no difference was found according to BCL6 mutations. Functional profiling methods allowed us to distinguish KEGG and gene ontology terms showing coordinated gene expression changes across subgroups of CLL. We validated a set of differentially expressed genes according to IgV(H) status, scoring them as putative prognostic markers in CLL. Among them, CRY1, LPL, CD82 and DUSP22 are the ones with at least equal or superior performance to ZAP70 which is actually the most used surrogate marker of IgV(H) status.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Jantus Lewintre, Eloisa Reinoso Martin, Cristina Montaner, David Marin, Miguel Jose Terol, Maria Farras, Rosa Benet, Isabel Calvete, Juan J Dopazo, Joaquin Garcia-Conde, Javier Research Support, Non-U.S. Gov’t England Leukemia &amp;amp; lymphoma Leuk Lymphoma. 2009 Jan;50(1):68-79.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nueda, Maria José</style></author><author><style face="normal" font="default" size="100%">Sebastián, Patricia</style></author><author><style face="normal" font="default" size="100%">Tarazona, Sonia</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Ferrer, Alberto</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Functional assessment of time course microarray data.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Time Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009 Jun 16</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10 Suppl 6</style></volume><pages><style face="normal" font="default" size="100%">S9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;MOTIVATION: &lt;/b&gt;Time-course microarray experiments study the progress of gene expression along time across one or several experimental conditions. Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information. The assessment of the functional aspects of time-course transcriptomics data requires the use of approaches that exploit the activation dynamics of the functional categories to where genes are annotated.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We present three novel methodologies for the functional assessment of time-course microarray data. i) maSigFun derives from the maSigPro method, a regression-based strategy to model time-dependent expression patterns and identify genes with differences across series. maSigFun fits a regression model for groups of genes labeled by a functional class and selects those categories which have a significant model. ii) PCA-maSigFun fits a PCA model of each functional class-defined expression matrix to extract orthogonal patterns of expression change, which are then assessed for their fit to a time-dependent regression model. iii) ASCA-functional uses the ASCA model to rank genes according to their correlation to principal time expression patterns and assess functional enrichment on a GSA fashion. We used simulated and experimental datasets to study these novel approaches. Results were compared to alternative methodologies.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Synthetic and experimental data showed that the different methods are able to capture different aspects of the relationship between genes, functions and co-expression that are biologically meaningful. The methods should not be considered as competitive but they provide different insights into the molecular and functional dynamic events taking place within the biological system under study.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/19534758?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aggarwal, Mohit</style></author><author><style face="normal" font="default" size="100%">Sánchez-Beato, Margarita</style></author><author><style face="normal" font="default" size="100%">Gómez-López, Gonzalo</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">Martínez, Nerea</style></author><author><style face="normal" font="default" size="100%">Rodríguez, Antonia</style></author><author><style face="normal" font="default" size="100%">Ruiz-Ballesteros, Elena</style></author><author><style face="normal" font="default" size="100%">Camacho, Francisca I</style></author><author><style face="normal" font="default" size="100%">Pérez-Rosado, Alberto</style></author><author><style face="normal" font="default" size="100%">de la Cueva, Paloma</style></author><author><style face="normal" font="default" size="100%">Artiga, María J</style></author><author><style face="normal" font="default" size="100%">Pisano, David G</style></author><author><style face="normal" font="default" size="100%">Kimby, Eva</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Villuendas, Raquel</style></author><author><style face="normal" font="default" size="100%">Piris, Miguel A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Functional signatures identified in B-cell non-Hodgkin lymphoma profiles.</style></title><secondary-title><style face="normal" font="default" size="100%">Leuk Lymphoma</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Leuk Lymphoma</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Cluster Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Leukemic</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Heterogeneity</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Lymphoma, B-Cell</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasm Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Neoplasm</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription, Genetic</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009 Oct</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">1699-708</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Gene-expression profiling in B-cell lymphomas has provided crucial data on specific lymphoma types, which can contribute to the identification of essential lymphoma survival genes and pathways. In this study, the gene-expression profiling data of all major B-cell lymphoma types were analyzed by unsupervised clustering. The transcriptome classification so obtained, was explored using gene set enrichment analysis generating a heatmap for B-cell lymphoma that identifies common lymphoma survival mechanisms and potential therapeutic targets, recognizing sets of coregulated genes and functional pathways expressed in different lymphoma types. Some of the most relevant signatures (stroma, cell cycle, B-cell receptor (BCR)) are shared by multiple lymphoma types or subclasses. A specific attention was paid to the analysis of BCR and coregulated pathways, defining molecular heterogeneity within multiple B-cell lymphoma types.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/19863341?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Bonifaci, Núria</style></author><author><style face="normal" font="default" size="100%">Pujana, Miguel Angel</style></author><author><style face="normal" font="default" size="100%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biological Phenomena</style></keyword><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">37</style></volume><pages><style face="normal" font="default" size="100%">W340-4</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Genome-wide association studies have become a popular strategy to find associations of genes to traits of interest. Despite the high-resolution available today to carry out genotyping studies, the success of its application in real studies has been limited by the testing strategy used. As an alternative to brute force solutions involving the use of very large cohorts, we propose the use of the Gene Set Analysis (GSA), a different analysis strategy based on testing the association of modules of functionally related genes. We show here how the Gene Set-based Analysis of Polymorphisms (GeSBAP), which is a simple implementation of the GSA strategy for the analysis of genome-wide association studies, provides a significant increase in the power testing for this type of studies. GeSBAP is freely available at http://bioinfo.cipf.es/gesbap/.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Web Server issue</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/19502494?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Bonifaci, Núria</style></author><author><style face="normal" font="default" size="100%">Pujana, Miguel Angel</style></author><author><style face="normal" font="default" size="100%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies</style></title><secondary-title><style face="normal" font="default" size="100%">Nucl. Acids Res.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword><keyword><style  face="normal" font="default" size="100%">gene set</style></keyword><keyword><style  face="normal" font="default" size="100%">GESBAP</style></keyword><keyword><style  face="normal" font="default" size="100%">pathway-based analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">SNP</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/cgi/content/abstract/37/suppl_2/W340</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">suppl_2</style></number><volume><style face="normal" font="default" size="100%">37</style></volume><pages><style face="normal" font="default" size="100%">W340-344</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Genome-wide association studies have become a popular strategy to find associations of genes to traits of interest. Despite the high-resolution available today to carry out genotyping studies, the success of its application in real studies has been limited by the testing strategy used. As an alternative to brute force solutions involving the use of very large cohorts, we propose the use of the Gene Set Analysis (GSA), a different analysis strategy based on testing the association of modules of functionally related genes. We show here how the Gene Set-based Analysis of Polymorphisms (GeSBAP), which is a simple implementation of the GSA strategy for the analysis of genome-wide association studies, provides a significant increase in the power testing for this type of studies. GeSBAP is freely available at http://bioinfo.cipf.es/gesbap/&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Orti, L.</style></author><author><style face="normal" font="default" size="100%">Carbajo, R. J.</style></author><author><style face="normal" font="default" size="100%">Pieper, U.</style></author><author><style face="normal" font="default" size="100%">Eswar, N.</style></author><author><style face="normal" font="default" size="100%">Maurer, S. M.</style></author><author><style face="normal" font="default" size="100%">Rai, A. K.</style></author><author><style face="normal" font="default" size="100%">Taylor, G.</style></author><author><style face="normal" font="default" size="100%">Todd, M. H.</style></author><author><style face="normal" font="default" size="100%">Pineda-Lucena, A.</style></author><author><style face="normal" font="default" size="100%">Sali, A.</style></author><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A kernel for open source drug discovery in tropical diseases</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS Negl Trop Dis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=19381286</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">e418</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BACKGROUND: Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&amp;D institutions ranging from private-public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues-open source drug discovery-has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such &quot;kernels&quot;. METHODOLOGY/PRINCIPAL FINDINGS: HERE, WE USE A COMPUTATIONAL PIPELINE FOR: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other. CONCLUSIONS/SIGNIFICANCE: The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases.</style></abstract><notes><style face="normal" font="default" size="100%">Orti, Leticia Carbajo, Rodrigo J Pieper, Ursula Eswar, Narayanan Maurer, Stephen M Rai, Arti K Taylor, Ginger Todd, Matthew H Pineda-Lucena, Antonio Sali, Andrej Marti-Renom, Marc A United States PLoS neglected tropical diseases PLoS Negl Trop Dis. 2009;3(4):e418. Epub 2009 Apr 21.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Orti, L.</style></author><author><style face="normal" font="default" size="100%">Carbajo, R. J.</style></author><author><style face="normal" font="default" size="100%">Pieper, U.</style></author><author><style face="normal" font="default" size="100%">Eswar, N.</style></author><author><style face="normal" font="default" size="100%">Maurer, S. M.</style></author><author><style face="normal" font="default" size="100%">Rai, A. K.</style></author><author><style face="normal" font="default" size="100%">Taylor, G.</style></author><author><style face="normal" font="default" size="100%">Todd, M. H.</style></author><author><style face="normal" font="default" size="100%">Pineda-Lucena, A.</style></author><author><style face="normal" font="default" size="100%">Sali, A.</style></author><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A kernel for the Tropical Disease Initiative</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Biotechnol</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=19352362</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">320-1</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">&lt;p&gt;Orti, Leticia Carbajo, Rodrigo J Pieper, Ursula Eswar, Narayanan Maurer, Stephen M Rai, Arti K Taylor, Ginger Todd, Matthew H Pineda-Lucena, Antonio Sali, Andrej Marti-Renom, Marc A P01 AI035707/AI/NIAID NIH HHS/United States P01 GM71790/GM/NIGMS NIH HHS/United States R01 GM54762/GM/NIGMS NIH HHS/United States U54 GM074945/GM/NIGMS NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t United States Nature biotechnology Nat Biotechnol. 2009 Apr;27(4):320-1.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brumos, J.</style></author><author><style face="normal" font="default" size="100%">Colmenero-Flores, J. M.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Izquierdo, P.</style></author><author><style face="normal" font="default" size="100%">Sanchez, G.</style></author><author><style face="normal" font="default" size="100%">Iglesias, D. J.</style></author><author><style face="normal" font="default" size="100%">Lopez-Climent, M. F.</style></author><author><style face="normal" font="default" size="100%">Gomez-Cadenas, A.</style></author><author><style face="normal" font="default" size="100%">Talon, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Membrane transporters and carbon metabolism implicated in chloride homeostasis differentiate salt stress responses in tolerant and sensitive Citrus rootstocks</style></title><secondary-title><style face="normal" font="default" size="100%">Funct Integr Genomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=19190944</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Salinity tolerance in Citrus is strongly related to leaf chloride accumulation. Both chloride homeostasis and specific genetic responses to Cl(-) toxicity are issues scarcely investigated in plants. To discriminate the transcriptomic network related to Cl(-) toxicity and salinity tolerance, we have used two Cl(-) salt treatments (NaCl and KCl) to perform a comparative microarray approach on two Citrus genotypes, the salt-sensitive Carrizo citrange, a poor Cl(-) excluder, and the tolerant Cleopatra mandarin, an efficient Cl(-) excluder. The data indicated that Cl(-) toxicity, rather than Na(+) toxicity and/or the concomitant osmotic perturbation, is the primary factor involved in the molecular responses of citrus plant leaves to salinity. A number of uncharacterized membrane transporter genes, like NRT1-2, were differentially regulated in the tolerant and the sensitive genotypes, suggesting its potential implication in Cl(-) homeostasis. Analyses of enriched functional categories showed that the tolerant rootstock induced wider stress responses in gene expression while repressing central metabolic processes such as photosynthesis and carbon utilization. These features were in agreement with phenotypic changes in the patterns of photosynthesis, transpiration, and stomatal conductance and support the concept that regulation of transpiration and its associated metabolic adjustments configure an adaptive response to salinity that reduces Cl(-) accumulation in the tolerant genotype.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Journal article Functional &amp;amp; integrative genomics Funct Integr Genomics. 2009 Feb 4.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pieper, U.</style></author><author><style face="normal" font="default" size="100%">Eswar, N.</style></author><author><style face="normal" font="default" size="100%">Webb, B. M.</style></author><author><style face="normal" font="default" size="100%">Eramian, D.</style></author><author><style face="normal" font="default" size="100%">Kelly, L.</style></author><author><style face="normal" font="default" size="100%">Barkan, D. T.</style></author><author><style face="normal" font="default" size="100%">Carter, H.</style></author><author><style face="normal" font="default" size="100%">Mankoo, P.</style></author><author><style face="normal" font="default" size="100%">Karchin, R.</style></author><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author><author><style face="normal" font="default" size="100%">Davis, F. P.</style></author><author><style face="normal" font="default" size="100%">Sali, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MODBASE, a database of annotated comparative protein structure models and associated resources</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Mutation Polymorphism</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Genomics Humans Ligands *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein User-Computer Interface</style></keyword><keyword><style  face="normal" font="default" size="100%">Single Nucleotide Protein Folding Protein Interaction Domains and Motifs *Protein Structure</style></keyword><keyword><style  face="normal" font="default" size="100%">Tertiary Proteins/genetics *Structural Homology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=18948282</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">Database issue</style></number><volume><style face="normal" font="default" size="100%">37</style></volume><pages><style face="normal" font="default" size="100%">D347-54</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5,152,695 reliable models for domains in 1,593,209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/).</style></abstract><notes><style face="normal" font="default" size="100%">Pieper, Ursula Eswar, Narayanan Webb, Ben M Eramian, David Kelly, Libusha Barkan, David T Carter, Hannah Mankoo, Parminder Karchin, Rachel Marti-Renom, Marc A Davis, Fred P Sali, Andrej GM08284/GM/NIGMS NIH HHS/United States P01 GM71790/GM/NIGMS NIH HHS/United States R01 GM54762/GM/NIGMS NIH HHS/United States U01 GM61390/GM/NIGMS NIH HHS/United States U54 GM074929/GM/NIGMS NIH HHS/United States U54 GM074945/GM/NIGMS NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, Non-P.H.S. England Nucleic acids research Nucleic Acids Res. 2009 Jan;37(Database issue):D347-54. Epub 2008 Oct 23.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Johan H van Heerden</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Dan J Stein</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Vivienne Russell</style></author><author><style face="normal" font="default" size="100%">Nicola Illing</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Parallel changes in gene expression in peripheral blood mononuclear cells and the brain after maternal separation in the mouse.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Res Notes</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">195</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Silbiger, Vivian</style></author><author><style face="normal" font="default" size="100%">Luchessi, André</style></author><author><style face="normal" font="default" size="100%">Hirata, Rosario</style></author><author><style face="normal" font="default" size="100%">Carracedo, Ángel</style></author><author><style face="normal" font="default" size="100%">Brión, Maria</style></author><author><style face="normal" font="default" size="100%">Lima Neto, Lidio</style></author><author><style face="normal" font="default" size="100%">P. Pastorelli, C</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Garcia, F</style></author><author><style face="normal" font="default" size="100%">P. Sampaio, M</style></author><author><style face="normal" font="default" size="100%">P. Pereira, M</style></author><author><style face="normal" font="default" size="100%">S. Santos, E</style></author><author><style face="normal" font="default" size="100%">Armaganijan, Dikran</style></author><author><style face="normal" font="default" size="100%">Hirata, Mario</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Peripheral blood cells transcriptome to study new biomarkers for myocardial infarction follow up</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Capriotti, Emidio</style></author><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SARA: a server for function annotation of RNA structures</style></title><secondary-title><style face="normal" font="default" size="100%">Nucl. Acids Res.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA structure</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/cgi/content/abstract/gkp433v1</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">gkp433</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Recent interest in non-coding RNA transcripts has resulted in a rapid increase of deposited RNA structures in the Protein Data Bank. However, a characterization and functional classification of the RNA structure and function space have only been partially addressed. Here, we introduce the SARA program for pair-wise alignment of RNA structures as a web server for structure-based RNA function assignment. The SARA server relies on the SARA program, which aligns two RNA structures based on a unit-vector root-mean-square approach. The likely accuracy of the SARA alignments is assessed by three different P-values estimating the statistical significance of the sequence, secondary structure and tertiary structure identity scores, respectively. Our benchmarks, which relied on a set of 419 RNA structures with known SCOR structural class, indicate that at a negative logarithm of mean P-value higher or equal than 2.5, SARA can assign the correct or a similar SCOR class to 81.4% and 95.3% of the benchmark set, respectively. The SARA server is freely accessible via the World Wide Web at http://sgu.bioinfo.cipf.es/services/SARA/.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author><author><style face="normal" font="default" size="100%">E. Capriotti</style></author><author><style face="normal" font="default" size="100%">Shindyalov, I.</style></author><author><style face="normal" font="default" size="100%">Bourne, P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Structural Comparison and Alignment</style></title><secondary-title><style face="normal" font="default" size="100%">Structural Bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Structural Bioinformatics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.amazon.com/gp/product/0470181052/</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">2nd</style></edition><publisher><style face="normal" font="default" size="100%">Wiley-Blackwell</style></publisher><pub-location><style face="normal" font="default" size="100%">New Jersey. USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">E. Capriotti</style></author><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessment of protein structure predictions</style></title><secondary-title><style face="normal" font="default" size="100%">Computational Structural Biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.amazon.com/dp/9812778772/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">World Scientific Publishing Company</style></publisher><pub-location><style face="normal" font="default" size="100%">New Jersey, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Carbonell, J.</style></author><author><style face="normal" font="default" size="100%">Minguez, P.</style></author><author><style face="normal" font="default" size="100%">Goetz, S.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Tarraga, J.</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Alloza, E.</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Babelomics: advanced functional profiling of transcriptomics, proteomics and genomics experiments</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword><keyword><style  face="normal" font="default" size="100%">funtional profiling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/content/36/suppl_2/W341.long</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">W341-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present a new version of Babelomics, a complete suite of web tools for the functional profiling of genome scale experiments, with new and improved methods as well as more types of functional definitions. Babelomics includes different flavours of conventional functional enrichment methods as well as more advanced gene set analysis methods that makes it a unique tool among the similar resources available. In addition to the well-known functional definitions (GO, KEGG), Babelomics includes new ones such as Biocarta pathways or text mining-derived functional terms. Regulatory modules implemented include transcriptional control (Transfac, CisRed) and other levels of regulation such as miRNA-mediated interference. Moreover, Babelomics allows for sub-selection of terms in order to test more focused hypothesis. Also gene annotation correspondence tables can be imported, which allows testing with user-defined functional modules. Finally, a tool for the ’de novo’ functional annotation of sequences has been included in the system. This allows using yet unannotated organisms in the program. Babelomics has been extensively re-engineered and now it includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. Babelomics is available at http://www.babelomics.org.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Al-Shahrour, Fatima Carbonell, Jose Minguez, Pablo Goetz, Stefan Conesa, Ana Tarraga, Joaquin Medina, Ignacio Alloza, Eva Montaner, David Dopazo, Joaquin Research Support, Non-U.S. Gov’t England Nucleic acids research Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W341-6. Epub 2008 May 31.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Gotz, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Blast2GO: A Comprehensive Suite for Functional Analysis in Plant Genomics</style></title><secondary-title><style face="normal" font="default" size="100%">Int J Plant Genomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=18483572</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2008</style></volume><pages><style face="normal" font="default" size="100%">619832</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Functional annotation of novel sequence data is a primary requirement for the utilization of functional genomics approaches in plant research. In this paper, we describe the Blast2GO suite as a comprehensive bioinformatics tool for functional annotation of sequences and data mining on the resulting annotations, primarily based on the gene ontology (GO) vocabulary. Blast2GO optimizes function transfer from homologous sequences through an elaborate algorithm that considers similarity, the extension of the homology, the database of choice, the GO hierarchy, and the quality of the original annotations. The tool includes numerous functions for the visualization, management, and statistical analysis of annotation results, including gene set enrichment analysis. The application supports InterPro, enzyme codes, KEGG pathways, GO direct acyclic graphs (DAGs), and GOSlim. Blast2GO is a suitable tool for plant genomics research because of its versatility, easy installation, and friendly use.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Conesa, Ana Gotz, Stefan Egypt International journal of plant genomics Int J Plant Genomics. 2008;2008:619832.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Valls, J.</style></author><author><style face="normal" font="default" size="100%">Grau, M.</style></author><author><style face="normal" font="default" size="100%">Sole, X.</style></author><author><style face="normal" font="default" size="100%">Hernandez, P.</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Peinado, M. A.</style></author><author><style face="normal" font="default" size="100%">Capella, G.</style></author><author><style face="normal" font="default" size="100%">Moreno, V.</style></author><author><style face="normal" font="default" size="100%">Pujana, M. A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CLEAR-test: combining inference for differential expression and variability in microarray data analysis</style></title><secondary-title><style face="normal" font="default" size="100%">J Biomed Inform</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Algorithms Artificial Intelligence *Data Interpretation</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Gene Expression Profiling/*methods Gene Expression Regulation/*physiology Oligonucleotide Array Sequence Analysis/*methods Proteome/*metabolism Signal Transduction/*physiology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17597009</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">41</style></volume><pages><style face="normal" font="default" size="100%">33-45</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A common goal of microarray experiments is to detect genes that are differentially expressed under distinct experimental conditions. Several statistical tests have been proposed to determine whether the observed changes in gene expression are significant. The t-test assigns a score to each gene on the basis of changes in its expression relative to its estimated variability, in such a way that genes with a higher score (in absolute values) are more likely to be significant. Most variants of the t-test use the complete set of genes to influence the variance estimate for each single gene. However, no inference is made in terms of the variability itself. Here, we highlight the problem of low observed variances in the t-test, when genes with relatively small changes are declared differentially expressed. Alternatively, the z-test could be used although, unlike the t-test, it can declare differentially expressed genes with high observed variances. To overcome this, we propose to combine the z-test, which focuses on large changes, with a chi(2) test to evaluate variability. We call this procedure CLEAR-test and we provide a combined p-value that offers a compromise between both aspects. Analysis of three publicly available microarray datasets reveals the greater performance of the CLEAR-test relative to the t-test and alternative methods. Finally, empirical and simulated data analyses demonstrate the greater reproducibility and statistical power of the CLEAR-test and z-test with respect to current alternative methods. In addition, the CLEAR-test improves the z-test by capturing reproducible genes with high variability.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Valls, Joan Grau, Monica Sole, Xavier Hernandez, Pilar Montaner, David Dopazo, Joaquin Peinado, Miguel A Capella, Gabriel Moreno, Victor Pujana, Miguel Angel Comparative Study Research Support, Non-U.S. Gov’t United States Journal of biomedical informatics J Biomed Inform. 2008 Feb;41(1):33-45. Epub 2007 May 17.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author><author><style face="normal" font="default" size="100%">Bro, Rasmus</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Prats, José Manuel</style></author><author><style face="normal" font="default" size="100%">Götz, Stefan</style></author><author><style face="normal" font="default" size="100%">Kjeldahl, Karin</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Direct functional assessment of the composite phenotype through multivariate projection strategies.</style></title><secondary-title><style face="normal" font="default" size="100%">Genomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genomics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Mathematical Computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Multivariate Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 Dec</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">92</style></volume><pages><style face="normal" font="default" size="100%">373-83</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present a novel approach for the analysis of transcriptomics data that integrates functional annotation of gene sets with expression values in a multivariate fashion, and directly assesses the relation of functional features to a multivariate space of response phenotypical variables. Multivariate projection methods are used to obtain new correlated variables for a set of genes that share a given function. These new functional variables are then related to the response variables of interest. The analysis of the principal directions of the multivariate regression allows for the identification of gene function features correlated with the phenotype. Two different transcriptomics studies are used to illustrate the statistical and interpretative aspects of the methodology. We demonstrate the superiority of the proposed method over equivalent approaches.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/18652888?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Bro, R.</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, F.</style></author><author><style face="normal" font="default" size="100%">Prats, J. M.</style></author><author><style face="normal" font="default" size="100%">Gotz, S.</style></author><author><style face="normal" font="default" size="100%">Kjeldahl, K.</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Direct functional assessment of the composite phenotype through multivariate projection strategies</style></title><secondary-title><style face="normal" font="default" size="100%">Genomics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms/genetics Computational Biology/*methods Databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Female Gene Expression Profiling/*statistics &amp; numerical data Humans Mathematical Computing Multivariate Analysis Phenotype</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=18652888</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">92</style></volume><pages><style face="normal" font="default" size="100%">373-83</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present a novel approach for the analysis of transcriptomics data that integrates functional annotation of gene sets with expression values in a multivariate fashion, and directly assesses the relation of functional features to a multivariate space of response phenotypical variables. Multivariate projection methods are used to obtain new correlated variables for a set of genes that share a given function. These new functional variables are then related to the response variables of interest. The analysis of the principal directions of the multivariate regression allows for the identification of gene function features correlated with the phenotype. Two different transcriptomics studies are used to illustrate the statistical and interpretative aspects of the methodology. We demonstrate the superiority of the proposed method over equivalent approaches.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Conesa, Ana Bro, Rasmus Garcia-Garcia, Francisco Prats, Jose Manuel Gotz, Stefan Kjeldahl, Karin Montaner, David Dopazo, Joaquin Evaluation Studies Research Support, Non-U.S. Gov’t United States Genomics Genomics. 2008 Dec;92(6):373-83. Epub 2008 Sep 13.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tarraga, J.</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Carbonell, J.</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, J.</style></author><author><style face="normal" font="default" size="100%">Minguez, P.</style></author><author><style face="normal" font="default" size="100%">Alloza, E.</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Vegas-Azcarate, S.</style></author><author><style face="normal" font="default" size="100%">Goetz, S.</style></author><author><style face="normal" font="default" size="100%">Escobar, P.</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, F.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">GEPAS, a web-based tool for microarray data analysis and interpretation</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">gepas</style></keyword><keyword><style  face="normal" font="default" size="100%">microarray data analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=18508806</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">W308-14</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Tarraga, Joaquin Medina, Ignacio Carbonell, Jose Huerta-Cepas, Jaime Minguez, Pablo Alloza, Eva Al-Shahrour, Fatima Vegas-Azcarate, Susana Goetz, Stefan Escobar, Pablo Garcia-Garcia, Francisco Conesa, Ana Montaner, David Dopazo, Joaquin Research Support, Non-U.S. Gov’t England Nucleic acids research Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W308-14. Epub 2008 May 28.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, Jaime</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Alloza, Eva</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">Vegas-Azcárate, Susana</style></author><author><style face="normal" font="default" size="100%">Goetz, Stefan</style></author><author><style face="normal" font="default" size="100%">Escobar, Pablo</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">GEPAS, a web-based tool for microarray data analysis and interpretation.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer Graphics</style></keyword><keyword><style  face="normal" font="default" size="100%">Dose-Response Relationship, Drug</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Kinetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 Jul 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">W308-14</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Web Server issue</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/18508806?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Götz, Stefan</style></author><author><style face="normal" font="default" size="100%">García-Gómez, Juan Miguel</style></author><author><style face="normal" font="default" size="100%">Terol, Javier</style></author><author><style face="normal" font="default" size="100%">Williams, Tim D</style></author><author><style face="normal" font="default" size="100%">Nagaraj, Shivashankar H</style></author><author><style face="normal" font="default" size="100%">Nueda, Maria José</style></author><author><style face="normal" font="default" size="100%">Robles, Montserrat</style></author><author><style face="normal" font="default" size="100%">Talon, Manuel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High-throughput functional annotation and data mining with the Blast2GO suite.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Graphics</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Expressed Sequence Tags</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Vocabulary, Controlled</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 Jun</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">3420-35</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/18445632?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wilkinson, Mark D</style></author><author><style face="normal" font="default" size="100%">Senger, Martin</style></author><author><style face="normal" font="default" size="100%">Kawas, Edward</style></author><author><style face="normal" font="default" size="100%">Bruskiewich, Richard</style></author><author><style face="normal" font="default" size="100%">Gouzy, Jerome</style></author><author><style face="normal" font="default" size="100%">Noirot, Celine</style></author><author><style face="normal" font="default" size="100%">Bardou, Philippe</style></author><author><style face="normal" font="default" size="100%">Ng, Ambrose</style></author><author><style face="normal" font="default" size="100%">Haase, Dirk</style></author><author><style face="normal" font="default" size="100%">Saiz, Enrique de Andres</style></author><author><style face="normal" font="default" size="100%">Wang, Dennis</style></author><author><style face="normal" font="default" size="100%">Gibbons, Frank</style></author><author><style face="normal" font="default" size="100%">Gordon, Paul M K</style></author><author><style face="normal" font="default" size="100%">Sensen, Christoph W</style></author><author><style face="normal" font="default" size="100%">Carrasco, Jose Manuel Rodriguez</style></author><author><style face="normal" font="default" size="100%">Fernández, José M</style></author><author><style face="normal" font="default" size="100%">Shen, Lixin</style></author><author><style face="normal" font="default" size="100%">Links, Matthew</style></author><author><style face="normal" font="default" size="100%">Ng, Michael</style></author><author><style face="normal" font="default" size="100%">Opushneva, Nina</style></author><author><style face="normal" font="default" size="100%">Neerincx, Pieter B T</style></author><author><style face="normal" font="default" size="100%">Leunissen, Jack A M</style></author><author><style face="normal" font="default" size="100%">Ernst, Rebecca</style></author><author><style face="normal" font="default" size="100%">Twigger, Simon</style></author><author><style face="normal" font="default" size="100%">Usadel, Bjorn</style></author><author><style face="normal" font="default" size="100%">Good, Benjamin</style></author><author><style face="normal" font="default" size="100%">Wong, Yan</style></author><author><style face="normal" font="default" size="100%">Stein, Lincoln</style></author><author><style face="normal" font="default" size="100%">Crosby, William</style></author><author><style face="normal" font="default" size="100%">Karlsson, Johan</style></author><author><style face="normal" font="default" size="100%">Royo, Romina</style></author><author><style face="normal" font="default" size="100%">Párraga, Iván</style></author><author><style face="normal" font="default" size="100%">Ramírez, Sergio</style></author><author><style face="normal" font="default" size="100%">Gelpi, Josep Lluis</style></author><author><style face="normal" font="default" size="100%">Trelles, Oswaldo</style></author><author><style face="normal" font="default" size="100%">Pisano, David G</style></author><author><style face="normal" font="default" size="100%">Jimenez, Natalia</style></author><author><style face="normal" font="default" size="100%">Kerhornou, Arnaud</style></author><author><style face="normal" font="default" size="100%">Rosset, Roman</style></author><author><style face="normal" font="default" size="100%">Zamacola, Leire</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, Jaime</style></author><author><style face="normal" font="default" size="100%">Carazo, Jose María</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Guigó, Roderic</style></author><author><style face="normal" font="default" size="100%">Navarro, Arcadi</style></author><author><style face="normal" font="default" size="100%">Orozco, Modesto</style></author><author><style face="normal" font="default" size="100%">Valencia, Alfonso</style></author><author><style face="normal" font="default" size="100%">Claros, M Gonzalo</style></author><author><style face="normal" font="default" size="100%">Pérez, Antonio J</style></author><author><style face="normal" font="default" size="100%">Aldana, Jose</style></author><author><style face="normal" font="default" size="100%">Rojano, M  Mar</style></author><author><style face="normal" font="default" size="100%">Fernandez-Santa Cruz, Raul</style></author><author><style face="normal" font="default" size="100%">Navas, Ismael</style></author><author><style face="normal" font="default" size="100%">Schiltz, Gary</style></author><author><style face="normal" font="default" size="100%">Farmer, Andrew</style></author><author><style face="normal" font="default" size="100%">Gessler, Damian</style></author><author><style face="normal" font="default" size="100%">Schoof, Heiko</style></author><author><style face="normal" font="default" size="100%">Groscurth, Andreas</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">BioMoby Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Interoperability with Moby 1.0--it's better than sharing your toothbrush!</style></title><secondary-title><style face="normal" font="default" size="100%">Brief Bioinform</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Brief Bioinform</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Database Management Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Information Storage and Retrieval</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Programming Languages</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems Integration</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 May</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">220-31</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/18238804?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wilkinson, M. D.</style></author><author><style face="normal" font="default" size="100%">Senger, M.</style></author><author><style face="normal" font="default" size="100%">Kawas, E.</style></author><author><style face="normal" font="default" size="100%">Bruskiewich, R.</style></author><author><style face="normal" font="default" size="100%">Gouzy, J.</style></author><author><style face="normal" font="default" size="100%">Noirot, C.</style></author><author><style face="normal" font="default" size="100%">Bardou, P.</style></author><author><style face="normal" font="default" size="100%">Ng, A.</style></author><author><style face="normal" font="default" size="100%">Haase, D.</style></author><author><style face="normal" font="default" size="100%">Saiz Ede, A.</style></author><author><style face="normal" font="default" size="100%">Wang, D.</style></author><author><style face="normal" font="default" size="100%">Gibbons, F.</style></author><author><style face="normal" font="default" size="100%">Gordon, P. M.</style></author><author><style face="normal" font="default" size="100%">Sensen, C. W.</style></author><author><style face="normal" font="default" size="100%">Carrasco, J. M.</style></author><author><style face="normal" font="default" size="100%">Fernandez, J. M.</style></author><author><style face="normal" font="default" size="100%">Shen, L.</style></author><author><style face="normal" font="default" size="100%">Links, M.</style></author><author><style face="normal" font="default" size="100%">Ng, M.</style></author><author><style face="normal" font="default" size="100%">Opushneva, N.</style></author><author><style face="normal" font="default" size="100%">Neerincx, P. B.</style></author><author><style face="normal" font="default" size="100%">Leunissen, J. A.</style></author><author><style face="normal" font="default" size="100%">Ernst, R.</style></author><author><style face="normal" font="default" size="100%">Twigger, S.</style></author><author><style face="normal" font="default" size="100%">Usadel, B.</style></author><author><style face="normal" font="default" size="100%">Good, B.</style></author><author><style face="normal" font="default" size="100%">Wong, Y.</style></author><author><style face="normal" font="default" size="100%">Stein, L.</style></author><author><style face="normal" font="default" size="100%">Crosby, W.</style></author><author><style face="normal" font="default" size="100%">Karlsson, J.</style></author><author><style face="normal" font="default" size="100%">Royo, R.</style></author><author><style face="normal" font="default" size="100%">Parraga, I.</style></author><author><style face="normal" font="default" size="100%">Ramirez, S.</style></author><author><style face="normal" font="default" size="100%">Gelpi, J. L.</style></author><author><style face="normal" font="default" size="100%">Trelles, O.</style></author><author><style face="normal" font="default" size="100%">Pisano, D. G.</style></author><author><style face="normal" font="default" size="100%">Jimenez, N.</style></author><author><style face="normal" font="default" size="100%">Kerhornou, A.</style></author><author><style face="normal" font="default" size="100%">Rosset, R.</style></author><author><style face="normal" font="default" size="100%">Zamacola, L.</style></author><author><style face="normal" font="default" size="100%">Tarraga, J.</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, J.</style></author><author><style face="normal" font="default" size="100%">Carazo, J. M.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">R. Guigo</style></author><author><style face="normal" font="default" size="100%">Navarro, A.</style></author><author><style face="normal" font="default" size="100%">Orozco, M.</style></author><author><style face="normal" font="default" size="100%">Valencia, A.</style></author><author><style face="normal" font="default" size="100%">Claros, M. G.</style></author><author><style face="normal" font="default" size="100%">Perez, A. J.</style></author><author><style face="normal" font="default" size="100%">Aldana, J.</style></author><author><style face="normal" font="default" size="100%">Rojano, M. M.</style></author><author><style face="normal" font="default" size="100%">Fernandez-Santa Cruz, R.</style></author><author><style face="normal" font="default" size="100%">Navas, I.</style></author><author><style face="normal" font="default" size="100%">Schiltz, G.</style></author><author><style face="normal" font="default" size="100%">Farmer, A.</style></author><author><style face="normal" font="default" size="100%">Gessler, D.</style></author><author><style face="normal" font="default" size="100%">Schoof, H.</style></author><author><style face="normal" font="default" size="100%">Groscurth, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interoperability with Moby 1.0–it’s better than sharing your toothbrush!</style></title><secondary-title><style face="normal" font="default" size="100%">Brief Bioinform</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Biology/*methods *Database Management Systems *Databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Factual Information Storage and Retrieval/*methods *Internet *Programming Languages Systems Integration</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=18238804</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">220-31</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;BioMoby Consortium Wilkinson, Mark D Senger, Martin Kawas, Edward Bruskiewich, Richard Gouzy, Jerome Noirot, Celine Bardou, Philippe Ng, Ambrose Haase, Dirk Saiz, Enrique de Andres Wang, Dennis Gibbons, Frank Gordon, Paul M K Sensen, Christoph W Carrasco, Jose Manuel Rodriguez Fernandez, Jose M Shen, Lixin Links, Matthew Ng, Michael Opushneva, Nina Neerincx, Pieter B T Leunissen, Jack A M Ernst, Rebecca Twigger, Simon Usadel, Bjorn Good, Benjamin Wong, Yan Stein, Lincoln Crosby, William Karlsson, Johan Royo, Romina Parraga, Ivan Ramirez, Sergio Gelpi, Josep Lluis Trelles, Oswaldo Pisano, David G Jimenez, Natalia Kerhornou, Arnaud Rosset, Roman Zamacola, Leire Tarraga, Joaquin Huerta-Cepas, Jaime Carazo, Jose Maria Dopazo, Joaquin Guigo, Roderic Navarro, Arcadi Orozco, Modesto Valencia, Alfonso Claros, M Gonzalo Perez, Antonio J Aldana, Jose Rojano, M Mar Fernandez-Santa Cruz, Raul Navas, Ismael Schiltz, Gary Farmer, Andrew Gessler, Damian Schoof, Heiko Groscurth, Andreas Research Support, Non-U.S. Gov’t Review England Briefings in bioinformatics Brief Bioinform. 2008 May;9(3):220-31. Epub 2008 Jan 31.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Reumers, Joke</style></author><author><style face="normal" font="default" size="100%">Conde, Lucia</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Maurer-Stroh, Sebastian</style></author><author><style face="normal" font="default" size="100%">Van Durme, Joost</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Rousseau, Frederic</style></author><author><style face="normal" font="default" size="100%">Schymkowitz, Joost</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Joint annotation of coding and non-coding single nucleotide polymorphisms and mutations in the SNPeffect and PupaSuite databases.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acid Substitution</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Diseases, Inborn</style></keyword><keyword><style  face="normal" font="default" size="100%">HSP70 Heat-Shock Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Rats</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA Splice Sites</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">D825-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Single nucleotide polymorphisms (SNPs) are, together with copy number variation, the primary source of variation in the human genome. SNPs are associated with altered response to drug treatment, susceptibility to disease and other phenotypic variation. Furthermore, during genetic screens for disease-associated mutations in groups of patients and control individuals, the distinction between disease causing mutation and polymorphism is often unclear. Annotation of the functional and structural implications of single nucleotide changes thus provides valuable information to interpret and guide experiments. The SNPeffect and PupaSuite databases are now synchronized to deliver annotations for both non-coding and coding SNP, as well as annotations for the SwissProt set of human disease mutations. In addition, SNPeffect now contains predictions of Tango2: an improved aggregation detector, and Waltz: a novel predictor of amyloid-forming sequences, as well as improved predictors for regions that are recognized by the Hsp70 family of chaperones. The new PupaSuite version incorporates predictions for SNPs in silencers and miRNAs including their targets, as well as additional methods for predicting SNPs in TFBSs and splice sites. Also predictions for mouse and rat genomes have been added. In addition, a PupaSuite web service has been developed to enable data access, programmatically. The combined database holds annotations for 4,965,073 regulatory as well as 133,505 coding human SNPs and 14,935 disease mutations, and phenotypic descriptions of 43,797 human proteins and is accessible via http://snpeffect.vib.be and http://pupasuite.bioinfo.cipf.es/.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Database issue</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/18086700?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Reumers, J.</style></author><author><style face="normal" font="default" size="100%">L. Conde</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Maurer-Stroh, S.</style></author><author><style face="normal" font="default" size="100%">Van Durme, J.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Rousseau, F.</style></author><author><style face="normal" font="default" size="100%">Schymkowitz, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Joint annotation of coding and non-coding single nucleotide polymorphisms and mutations in the SNPeffect and PupaSuite databases</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acid Substitution Animals *Databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Genetic Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Inborn/genetics HSP70 Heat-Shock Proteins/metabolism Humans Internet Mice MicroRNAs/metabolism *Mutation *Polymorphism</style></keyword><keyword><style  face="normal" font="default" size="100%">Single Nucleotide Proteins/chemistry/genetics RNA Splice Sites Rats Transcription Factors/metabolism</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/cgi/content/full/36/suppl_1/D825</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">Database issue</style></number><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">D825-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Single nucleotide polymorphisms (SNPs) are, together with copy number variation, the primary source of variation in the human genome. SNPs are associated with altered response to drug treatment, susceptibility to disease and other phenotypic variation. Furthermore, during genetic screens for disease-associated mutations in groups of patients and control individuals, the distinction between disease causing mutation and polymorphism is often unclear. Annotation of the functional and structural implications of single nucleotide changes thus provides valuable information to interpret and guide experiments. The SNPeffect and PupaSuite databases are now synchronized to deliver annotations for both non-coding and coding SNP, as well as annotations for the SwissProt set of human disease mutations. In addition, SNPeffect now contains predictions of Tango2: an improved aggregation detector, and Waltz: a novel predictor of amyloid-forming sequences, as well as improved predictors for regions that are recognized by the Hsp70 family of chaperones. The new PupaSuite version incorporates predictions for SNPs in silencers and miRNAs including their targets, as well as additional methods for predicting SNPs in TFBSs and splice sites. Also predictions for mouse and rat genomes have been added. In addition, a PupaSuite web service has been developed to enable data access, programmatically. The combined database holds annotations for 4,965,073 regulatory as well as 133,505 coding human SNPs and 14,935 disease mutations, and phenotypic descriptions of 43,797 human proteins and is accessible via http://snpeffect.vib.be and http://pupasuite.bioinfo.cipf.es/.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Reumers, Joke Conde, Lucia Medina, Ignacio Maurer-Stroh, Sebastian Van Durme, Joost Dopazo, Joaquin Rousseau, Frederic Schymkowitz, Joost Research Support, Non-U.S. Gov’t England Nucleic acids research Nucleic Acids Res. 2008 Jan;36(Database issue):D825-9. Epub 2007 Dec 17.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Botton, A.</style></author><author><style face="normal" font="default" size="100%">Galla, G.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Bachem, C.</style></author><author><style face="normal" font="default" size="100%">Ramina, A.</style></author><author><style face="normal" font="default" size="100%">Barcaccia, G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Large-scale Gene Ontology analysis of plant transcriptome-derived sequences retrieved by AFLP technology</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Genomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=18652646</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">347</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;BACKGROUND: After 10-year-use of AFLP (Amplified Fragment Length Polymorphism) technology for DNA fingerprinting and mRNA profiling, large repertories of genome- and transcriptome-derived sequences are available in public databases for model, crop and tree species. AFLP marker systems have been and are being extensively exploited for genome scanning and gene mapping, as well as cDNA-AFLP for transcriptome profiling and differentially expressed gene cloning. The evaluation, annotation and classification of genomic markers and expressed transcripts would be of great utility for both functional genomics and systems biology research in plants. This may be achieved by means of the Gene Ontology (GO), consisting in three structured vocabularies (i.e. ontologies) describing genes, transcripts and proteins of any organism in terms of their associated cellular component, biological process and molecular function in a species-independent manner. In this paper, the functional annotation of about 8,000 AFLP-derived ESTs retrieved in the NCBI databases was carried out by using GO terminology. RESULTS: Descriptive statistics on the type, size and nature of gene sequences obtained by means of AFLP technology were calculated. The gene products associated with mRNA transcripts were then classified according to the three main GO vocabularies. A comparison of the functional content of cDNA-AFLP records was also performed by splitting the sequence dataset into monocots and dicots and by comparing them to all annotated ESTs of Arabidopsis and rice, respectively. On the whole, the statistical parameters adopted for the in silico AFLP-derived transcriptome-anchored sequence analysis proved to be critical for obtaining reliable GO results. Such an exhaustive annotation may offer a suitable platform for functional genomics, particularly useful in non-model species. CONCLUSION: Reliable GO annotations of AFLP-derived sequences can be gathered through the optimization of the experimental steps and the statistical parameters adopted. The Blast2GO software was shown to represent a comprehensive bioinformatics solution for an annotation-based functional analysis. According to the whole set of GO annotations, the AFLP technology generates thorough information for angiosperm gene products and shares common features across angiosperm species and families. The utility of this technology for structural and functional genomics in plants can be implemented by serial annotation analyses of genome-anchored fragments and organ/tissue-specific repertories of transcriptome-derived fragments.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Botton, Alessandro Galla, Giulio Conesa, Ana Bachem, Christian Ramina, Angelo Barcaccia, Gianni England BMC genomics BMC Genomics. 2008 Jul 24;9:347.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Montero-Conde, C.</style></author><author><style face="normal" font="default" size="100%">Martin-Campos, J. M.</style></author><author><style face="normal" font="default" size="100%">Lerma, E.</style></author><author><style face="normal" font="default" size="100%">Gimenez, G.</style></author><author><style face="normal" font="default" size="100%">Martinez-Guitarte, J. L.</style></author><author><style face="normal" font="default" size="100%">Combalia, N.</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Matias-Guiu, X.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">de Leiva, A.</style></author><author><style face="normal" font="default" size="100%">M. Robledo</style></author><author><style face="normal" font="default" size="100%">Mauricio, D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Molecular profiling related to poor prognosis in thyroid carcinoma. Combining gene expression data and biological information</style></title><secondary-title><style face="normal" font="default" size="100%">Oncogene</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adenoma/genetics/metabolism/pathology Adolescent Adult Aged Carcinoma/genetics/metabolism/pathology Carcinoma</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological/*genetics/metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasm/genetics/metabolism Reverse Transcriptase Polymerase Chain Reaction Signal Transduction Thyroid Neoplasms/classification/*genetics/metabolism Tumor Markers</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplastic Humans Male Middle Aged *Oligonucleotide Array Sequence Analysis Prognosis RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Papillary/genetics/metabolism/pathology Cell Differentiation Female *Gene Expression Profiling *Gene Expression Regulation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17873908</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">11</style></number><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">1554-61</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Undifferentiated and poorly differentiated thyroid tumors are responsible for more than half of thyroid cancer patient deaths in spite of their low incidence. Conventional treatments do not obtain substantial benefits, and the lack of alternative approaches limits patient survival. Additionally, the absence of prognostic markers for well-differentiated tumors complicates patient-specific treatments and favors the progression of recurrent forms. In order to recognize the molecular basis involved in tumor dedifferentiation and identify potential markers for thyroid cancer prognosis prediction, we analysed the expression profile of 44 thyroid primary tumors with different degrees of dedifferentiation and aggressiveness using cDNA microarrays. Transcriptome comparison of dedifferentiated and well-differentiated thyroid tumors identified 1031 genes with &amp;gt;2-fold difference in absolute values and false discovery rate of &amp;lt;0.15. According to known molecular interaction and reaction networks, the products of these genes were mainly clustered in the MAPkinase signaling pathway, the TGF-beta signaling pathway, focal adhesion and cell motility, activation of actin polymerization and cell cycle. An exhaustive search in several databases allowed us to identify various members of the matrix metalloproteinase, melanoma antigen A and collagen gene families within the upregulated gene set. We also identified a prognosis classifier comprising just 30 transcripts with an overall accuracy of 95%. These findings may clarify the molecular mechanisms involved in thyroid tumor dedifferentiation and provide a potential prognosis predictor as well as targets for new therapies.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Montero-Conde, C Martin-Campos, J M Lerma, E Gimenez, G Martinez-Guitarte, J L Combalia, N Montaner, D Matias-Guiu, X Dopazo, J de Leiva, A Robledo, M Mauricio, D Research Support, Non-U.S. Gov’t England Oncogene Oncogene. 2008 Mar 6;27(11):1554-61. Epub 2007 Sep 17.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Montero-Conde, C</style></author><author><style face="normal" font="default" size="100%">Martín-Campos, J M</style></author><author><style face="normal" font="default" size="100%">Lerma, E</style></author><author><style face="normal" font="default" size="100%">Gimenez, G</style></author><author><style face="normal" font="default" size="100%">Martínez-Guitarte, J L</style></author><author><style face="normal" font="default" size="100%">Combalía, N</style></author><author><style face="normal" font="default" size="100%">Montaner, D</style></author><author><style face="normal" font="default" size="100%">Matías-Guiu, X</style></author><author><style face="normal" font="default" size="100%">Dopazo, J</style></author><author><style face="normal" font="default" size="100%">de Leiva, A</style></author><author><style face="normal" font="default" size="100%">Robledo, M</style></author><author><style face="normal" font="default" size="100%">Mauricio, D</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Molecular profiling related to poor prognosis in thyroid carcinoma. Combining gene expression data and biological information.</style></title><secondary-title><style face="normal" font="default" size="100%">Oncogene</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Oncogene</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adenoma</style></keyword><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Biomarkers, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Carcinoma</style></keyword><keyword><style  face="normal" font="default" size="100%">Carcinoma, Papillary</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Differentiation</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Prognosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Reverse Transcriptase Polymerase Chain Reaction</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Neoplasm</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Thyroid Neoplasms</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 Mar 06</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">1554-61</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Undifferentiated and poorly differentiated thyroid tumors are responsible for more than half of thyroid cancer patient deaths in spite of their low incidence. Conventional treatments do not obtain substantial benefits, and the lack of alternative approaches limits patient survival. Additionally, the absence of prognostic markers for well-differentiated tumors complicates patient-specific treatments and favors the progression of recurrent forms. In order to recognize the molecular basis involved in tumor dedifferentiation and identify potential markers for thyroid cancer prognosis prediction, we analysed the expression profile of 44 thyroid primary tumors with different degrees of dedifferentiation and aggressiveness using cDNA microarrays. Transcriptome comparison of dedifferentiated and well-differentiated thyroid tumors identified 1031 genes with &gt;2-fold difference in absolute values and false discovery rate of &lt;0.15. According to known molecular interaction and reaction networks, the products of these genes were mainly clustered in the MAPkinase signaling pathway, the TGF-beta signaling pathway, focal adhesion and cell motility, activation of actin polymerization and cell cycle. An exhaustive search in several databases allowed us to identify various members of the matrix metalloproteinase, melanoma antigen A and collagen gene families within the upregulated gene set. We also identified a prognosis classifier comprising just 30 transcripts with an overall accuracy of 95%. These findings may clarify the molecular mechanisms involved in thyroid tumor dedifferentiation and provide a potential prognosis predictor as well as targets for new therapies.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/17873908?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">E. Capriotti</style></author><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">RNA structure alignment by a unit-vector approach</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms Base Sequence Computer Simulation *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Chemical *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Molecular Sequence Data Nucleic Acid Conformation RNA/*chemistry/*ultrastructure Sequence Alignment/*methods Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA/*methods *Software</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=18689811</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">16</style></number><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">i112-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">MOTIVATION: The recent discovery of tiny RNA molecules such as microRNAs and small interfering RNA are transforming the view of RNA as a simple information transfer molecule. Similar to proteins, the native three-dimensional structure of RNA determines its biological activity. Therefore, classifying the current structural space is paramount for functionally annotating RNA molecules. The increasing numbers of RNA structures deposited in the PDB requires more accurate, automatic and benchmarked methods for RNA structure comparison. In this article, we introduce a new algorithm for RNA structure alignment based on a unit-vector approach. The algorithm has been implemented in the SARA program, which results in RNA structure pairwise alignments and their statistical significance. RESULTS: The SARA program has been implemented to be of general applicability even when no secondary structure can be calculated from the RNA structures. A benchmark against the ARTS program using a set of 1275 non-redundant pairwise structure alignments results in inverted approximately 6% extra alignments with at least 50% structurally superposed nucleotides and base pairs. A first attempt to perform RNA automatic functional annotation based on structure alignments indicates that SARA can correctly assign the deepest SCOR classification to &gt;60% of the query structures. AVAILABILITY: The SARA program is freely available through a World Wide Web server http://sgu.bioinfo.cipf.es/services/SARA/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.</style></abstract><notes><style face="normal" font="default" size="100%">Capriotti, Emidio Marti-Renom, Marc A Research Support, Non-U.S. Gov’t England Bioinformatics (Oxford, England) Bioinformatics. 2008 Aug 15;24(16):i112-8.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">K. Saar</style></author><author><style face="normal" font="default" size="100%">A. Beck</style></author><author><style face="normal" font="default" size="100%">M. T. Bihoreau</style></author><author><style face="normal" font="default" size="100%">E. Birney</style></author><author><style face="normal" font="default" size="100%">D. Brocklebank</style></author><author><style face="normal" font="default" size="100%">Y. Chen</style></author><author><style face="normal" font="default" size="100%">E. Cuppen</style></author><author><style face="normal" font="default" size="100%">S. Demonchy</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">P. Flicek</style></author><author><style face="normal" font="default" size="100%">M. Foglio</style></author><author><style face="normal" font="default" size="100%">A. Fujiyama</style></author><author><style face="normal" font="default" size="100%">I. G. Gut</style></author><author><style face="normal" font="default" size="100%">D. Gauguier</style></author><author><style face="normal" font="default" size="100%">R. Guigo</style></author><author><style face="normal" font="default" size="100%">V. Guryev</style></author><author><style face="normal" font="default" size="100%">M. Heinig</style></author><author><style face="normal" font="default" size="100%">O. Hummel</style></author><author><style face="normal" font="default" size="100%">N. Jahn</style></author><author><style face="normal" font="default" size="100%">S. Klages</style></author><author><style face="normal" font="default" size="100%">V. Kren</style></author><author><style face="normal" font="default" size="100%">M. Kube</style></author><author><style face="normal" font="default" size="100%">H. Kuhl</style></author><author><style face="normal" font="default" size="100%">Kuramoto, T.</style></author><author><style face="normal" font="default" size="100%">Kuroki, Y.</style></author><author><style face="normal" font="default" size="100%">Lechner, D.</style></author><author><style face="normal" font="default" size="100%">Lee, Y. A.</style></author><author><style face="normal" font="default" size="100%">Lopez-Bigas, N.</style></author><author><style face="normal" font="default" size="100%">Lathrop, G. M.</style></author><author><style face="normal" font="default" size="100%">Mashimo, T.</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Mott, R.</style></author><author><style face="normal" font="default" size="100%">Patone, G.</style></author><author><style face="normal" font="default" size="100%">Perrier-Cornet, J. A.</style></author><author><style face="normal" font="default" size="100%">Platzer, M.</style></author><author><style face="normal" font="default" size="100%">Pravenec, M.</style></author><author><style face="normal" font="default" size="100%">Reinhardt, R.</style></author><author><style face="normal" font="default" size="100%">Sakaki, Y.</style></author><author><style face="normal" font="default" size="100%">Schilhabel, M.</style></author><author><style face="normal" font="default" size="100%">Schulz, H.</style></author><author><style face="normal" font="default" size="100%">Serikawa, T.</style></author><author><style face="normal" font="default" size="100%">Shikhagaie, M.</style></author><author><style face="normal" font="default" size="100%">Tatsumoto, S.</style></author><author><style face="normal" font="default" size="100%">Taudien, S.</style></author><author><style face="normal" font="default" size="100%">Toyoda, A.</style></author><author><style face="normal" font="default" size="100%">Voigt, B.</style></author><author><style face="normal" font="default" size="100%">Zelenika, D.</style></author><author><style face="normal" font="default" size="100%">Zimdahl, H.</style></author><author><style face="normal" font="default" size="100%">Hubner, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SNP and haplotype mapping for genetic analysis in the rat</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Genet</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals Chromosome Mapping *Databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Genome *Haplotypes Linkage Disequilibrium Phylogeny *Polymorphism</style></keyword><keyword><style  face="normal" font="default" size="100%">Inbred Strains/*genetics Recombination</style></keyword><keyword><style  face="normal" font="default" size="100%">Single Nucleotide *Quantitative Trait Loci Rats/*genetics Rats</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=18443594</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">40</style></volume><pages><style face="normal" font="default" size="100%">560-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The laboratory rat is one of the most extensively studied model organisms. Inbred laboratory rat strains originated from limited Rattus norvegicus founder populations, and the inherited genetic variation provides an excellent resource for the correlation of genotype to phenotype. Here, we report a survey of genetic variation based on almost 3 million newly identified SNPs. We obtained accurate and complete genotypes for a subset of 20,238 SNPs across 167 distinct inbred rat strains, two rat recombinant inbred panels and an F2 intercross. Using 81% of these SNPs, we constructed high-density genetic maps, creating a large dataset of fully characterized SNPs for disease gene mapping. Our data characterize the population structure and illustrate the degree of linkage disequilibrium. We provide a detailed SNP map and demonstrate its utility for mapping of quantitative trait loci. This community resource is openly available and augments the genetic tools for this workhorse of physiological studies.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;STAR Consortium Saar, Kathrin Beck, Alfred Bihoreau, Marie-Therese Birney, Ewan Brocklebank, Denise Chen, Yuan Cuppen, Edwin Demonchy, Stephanie Dopazo, Joaquin Flicek, Paul Foglio, Mario Fujiyama, Asao Gut, Ivo G Gauguier, Dominique Guigo, Roderic Guryev, Victor Heinig, Matthias Hummel, Oliver Jahn, Niels Klages, Sven Kren, Vladimir Kube, Michael Kuhl, Heiner Kuramoto, Takashi Kuroki, Yoko Lechner, Doris Lee, Young-Ae Lopez-Bigas, Nuria Lathrop, G Mark Mashimo, Tomoji Medina, Ignacio Mott, Richard Patone, Giannino Perrier-Cornet, Jeanne-Antide Platzer, Matthias Pravenec, Michal Reinhardt, Richard Sakaki, Yoshiyuki Schilhabel, Markus Schulz, Herbert Serikawa, Tadao Shikhagaie, Medya Tatsumoto, Shouji Taudien, Stefan Toyoda, Atsushi Voigt, Birger Zelenika, Diana Zimdahl, Heike Hubner, Norbert 057733/Z/99/A/Wellcome Trust/United Kingdom 066780/Z/01/Z/Wellcome Trust/United Kingdom Research Support, Non-U.S. Gov’t Technical Report United States Nature genetics Nat Genet. 2008 May;40(5):560-6.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Saar, Kathrin</style></author><author><style face="normal" font="default" size="100%">Beck, Alfred</style></author><author><style face="normal" font="default" size="100%">Bihoreau, Marie-Thérèse</style></author><author><style face="normal" font="default" size="100%">Birney, Ewan</style></author><author><style face="normal" font="default" size="100%">Brocklebank, Denise</style></author><author><style face="normal" font="default" size="100%">Chen, Yuan</style></author><author><style face="normal" font="default" size="100%">Cuppen, Edwin</style></author><author><style face="normal" font="default" size="100%">Demonchy, Stephanie</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Flicek, Paul</style></author><author><style face="normal" font="default" size="100%">Foglio, Mario</style></author><author><style face="normal" font="default" size="100%">Fujiyama, Asao</style></author><author><style face="normal" font="default" size="100%">Gut, Ivo G</style></author><author><style face="normal" font="default" size="100%">Gauguier, Dominique</style></author><author><style face="normal" font="default" size="100%">Guigó, Roderic</style></author><author><style face="normal" font="default" size="100%">Guryev, Victor</style></author><author><style face="normal" font="default" size="100%">Heinig, Matthias</style></author><author><style face="normal" font="default" size="100%">Hummel, Oliver</style></author><author><style face="normal" font="default" size="100%">Jahn, Niels</style></author><author><style face="normal" font="default" size="100%">Klages, Sven</style></author><author><style face="normal" font="default" size="100%">Kren, Vladimir</style></author><author><style face="normal" font="default" size="100%">Kube, Michael</style></author><author><style face="normal" font="default" size="100%">Kuhl, Heiner</style></author><author><style face="normal" font="default" size="100%">Kuramoto, Takashi</style></author><author><style face="normal" font="default" size="100%">Kuroki, Yoko</style></author><author><style face="normal" font="default" size="100%">Lechner, Doris</style></author><author><style face="normal" font="default" size="100%">Lee, Young-Ae</style></author><author><style face="normal" font="default" size="100%">Lopez-Bigas, Nuria</style></author><author><style face="normal" font="default" size="100%">Lathrop, G Mark</style></author><author><style face="normal" font="default" size="100%">Mashimo, Tomoji</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Mott, Richard</style></author><author><style face="normal" font="default" size="100%">Patone, Giannino</style></author><author><style face="normal" font="default" size="100%">Perrier-Cornet, Jeanne-Antide</style></author><author><style face="normal" font="default" size="100%">Platzer, Matthias</style></author><author><style face="normal" font="default" size="100%">Pravenec, Michal</style></author><author><style face="normal" font="default" size="100%">Reinhardt, Richard</style></author><author><style face="normal" font="default" size="100%">Sakaki, Yoshiyuki</style></author><author><style face="normal" font="default" size="100%">Schilhabel, Markus</style></author><author><style face="normal" font="default" size="100%">Schulz, Herbert</style></author><author><style face="normal" font="default" size="100%">Serikawa, Tadao</style></author><author><style face="normal" font="default" size="100%">Shikhagaie, Medya</style></author><author><style face="normal" font="default" size="100%">Tatsumoto, Shouji</style></author><author><style face="normal" font="default" size="100%">Taudien, Stefan</style></author><author><style face="normal" font="default" size="100%">Toyoda, Atsushi</style></author><author><style face="normal" font="default" size="100%">Voigt, Birger</style></author><author><style face="normal" font="default" size="100%">Zelenika, Diana</style></author><author><style face="normal" font="default" size="100%">Zimdahl, Heike</style></author><author><style face="normal" font="default" size="100%">Hubner, Norbert</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">STAR Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">SNP and haplotype mapping for genetic analysis in the rat.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosome Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome</style></keyword><keyword><style  face="normal" font="default" size="100%">Haplotypes</style></keyword><keyword><style  face="normal" font="default" size="100%">Linkage Disequilibrium</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Rats</style></keyword><keyword><style  face="normal" font="default" size="100%">Rats, Inbred Strains</style></keyword><keyword><style  face="normal" font="default" size="100%">Recombination, Genetic</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 May</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">40</style></volume><pages><style face="normal" font="default" size="100%">560-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The laboratory rat is one of the most extensively studied model organisms. Inbred laboratory rat strains originated from limited Rattus norvegicus founder populations, and the inherited genetic variation provides an excellent resource for the correlation of genotype to phenotype. Here, we report a survey of genetic variation based on almost 3 million newly identified SNPs. We obtained accurate and complete genotypes for a subset of 20,238 SNPs across 167 distinct inbred rat strains, two rat recombinant inbred panels and an F2 intercross. Using 81% of these SNPs, we constructed high-density genetic maps, creating a large dataset of fully characterized SNPs for disease gene mapping. Our data characterize the population structure and illustrate the degree of linkage disequilibrium. We provide a detailed SNP map and demonstrate its utility for mapping of quantitative trait loci. This community resource is openly available and augments the genetic tools for this workhorse of physiological studies.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/18443594?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Agudo, M.</style></author><author><style face="normal" font="default" size="100%">Perez-Marin, M. C.</style></author><author><style face="normal" font="default" size="100%">Lonngren, U.</style></author><author><style face="normal" font="default" size="100%">Sobrado, P.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Canovas, I.</style></author><author><style face="normal" font="default" size="100%">Salinas-Navarro, M.</style></author><author><style face="normal" font="default" size="100%">Miralles-Imperial, J.</style></author><author><style face="normal" font="default" size="100%">Hallbook, F.</style></author><author><style face="normal" font="default" size="100%">Vidal-Sanz, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Time course profiling of the retinal transcriptome after optic nerve transection and optic nerve crush</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Vis</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals Cell Death Cluster Analysis Female *Gene Expression Profiling Gene Expression Regulation *Nerve Crush Optic Nerve/*metabolism/*pathology Optic Nerve Injuries/*genetics Rats Rats</style></keyword><keyword><style  face="normal" font="default" size="100%">Sprague-Dawley Reproducibility of Results Retina/*metabolism/*pathology Time Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=18552980</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">1050-63</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">PURPOSE: A time-course analysis of gene regulation in the adult rat retina after intraorbital nerve crush (IONC) and intraorbital nerve transection (IONT). METHODS: RNA was extracted from adult rat retinas undergoing either IONT or IONC at increasing times post-lesion. Affymetrix RAE230.2 arrays were hybridized and analyzed. Statistically regulated genes were annotated and functionally clustered. Arrays were validated by means of quantative reverse transcription polymerase chain reaction (qRT-PCR) on ten regulated genes at two times post-lesion. Western blotting and immunohistofluorescence for four pro-apoptotic proteins were performed on naive and injured retinas. Finally, custom signaling maps for IONT- and IONC-induced death response were generated (MetaCore, Genego Inc.). RESULTS: Here we show that over time, 3,219 sequences were regulated after IONT and 1,996 after IONC. Out of the total of regulated sequences, 1,078 were commonly regulated by both injuries. Interestingly, while IONT mainly triggers a gene upregulation-sustained over time, IONC causes a transitory downregulation. Functional clustering identified the regulation of high interest biologic processes, most importantly cell death wherein apoptosis was the most significant cluster. Ten death-related genes upregulated by both injuries were used for array validation by means of qRT-PCR. In addition, western blotting and immunohistofluorescence of total and active Caspase 3 (Casp3), tumor necrosis factor receptor type 1 associated death domain (TRADD), tumor necrosis factor receptor superfamily member 1a (TNFR1a), and c-fos were performed to confirm their protein regulation and expression pattern in naive and injured retinas. These analyses demonstrated that for these genes, protein regulation followed transcriptional regulation and that these pro-apoptotic proteins were expressed by retinal ganglion cells (RGCs). MetaCore-based death-signaling maps show that several apoptotic cascades were regulated in the retina following optic nerve injury and highlight the similarities and differences between IONT and IONC in cell death profiling. CONCLUSIONS: This comprehensive time course retinal transcriptome study comparing IONT and IONC lesions provides a unique valuable tool to understand the molecular mechanisms underlying optic nerve injury and to design neuroprotective protocols.</style></abstract><notes><style face="normal" font="default" size="100%">Agudo, Marta Perez-Marin, Maria Cruz Lonngren, Ulrika Sobrado, Paloma Conesa, Ana Canovas, Isabel Salinas-Navarro, Manuel Miralles-Imperial, Jaime Hallbook, Finn Vidal-Sanz, Manuel Research Support, Non-U.S. Gov’t United States Molecular vision Mol Vis. 2008 Jun 3;14:1050-63.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hoogerwerf, W. A.</style></author><author><style face="normal" font="default" size="100%">Sinha, M.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Luxon, B. A.</style></author><author><style face="normal" font="default" size="100%">Shahinian, V. B.</style></author><author><style face="normal" font="default" size="100%">Cornelissen, G.</style></author><author><style face="normal" font="default" size="100%">Halberg, F.</style></author><author><style face="normal" font="default" size="100%">Bostwick, J.</style></author><author><style face="normal" font="default" size="100%">Timm, J.</style></author><author><style face="normal" font="default" size="100%">Cassone, V. M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transcriptional profiling of mRNA expression in the mouse distal colon</style></title><secondary-title><style face="normal" font="default" size="100%">Gastroenterology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals Blotting</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Inbred C57BL Microarray Analysis Proteins/*genetics/metabolism RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Messenger/biosynthesis/*genetics Reverse Transcriptase Polymerase Chain Reaction *Transcription</style></keyword><keyword><style  face="normal" font="default" size="100%">Western Cell Proliferation Circadian Rhythm/*genetics Colon/cytology/*metabolism Male Mice Mice</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=18848557</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">135</style></volume><pages><style face="normal" font="default" size="100%">2019-29</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BACKGROUND &amp; AIMS: Intestinal epithelial cells and the myenteric plexus of the mouse gastrointestinal tract contain a circadian clock-based intrinsic time-keeping system. Because disruption of the biological clock has been associated with increased susceptibility to colon cancer and gastrointestinal symptoms, we aimed to identify rhythmically expressed genes in the mouse distal colon. METHODS: Microarray analysis was used to identify genes that were rhythmically expressed over a 24-hour light/dark cycle. The transcripts were then classified according to expression pattern, function, and association with physiologic and pathophysiologic processes of the colon. RESULTS: A circadian gene expression pattern was detected in approximately 3.7% of distal colonic genes. A large percentage of these genes were involved in cell signaling, differentiation, and proliferation and cell death. Of all the rhythmically expressed genes in the mouse colon, approximately 7% (64/906) have been associated with colorectal cancer formation (eg, B-cell leukemia/lymphoma-2 [Bcl2]) and 1.8% (18/906) with various colonic functions such as motility and secretion (eg, vasoactive intestinal polypeptide, cystic fibrosis transmembrane conductance regulator). CONCLUSIONS: A subset of genes in the murine colon follows a rhythmic expression pattern. These findings may have significant implications for colonic physiology and pathophysiology.</style></abstract><notes><style face="normal" font="default" size="100%">Hoogerwerf, Willemijntje A Sinha, Mala Conesa, Ana Luxon, Bruce A Shahinian, Vahakn B Cornelissen, Germaine Halberg, Franz Bostwick, Jonathon Timm, John Cassone, Vincent M R21 DK074477-01A1/DK/NIDDK NIH HHS/United States Comparative Study Research Support, N.I.H., Extramural United States Gastroenterology Gastroenterology. 2008 Dec;135(6):2019-29. Epub 2008 Sep 3.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stierum, R.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Heijne, W.</style></author><author><style face="normal" font="default" size="100%">Ommen, B.</style></author><author><style face="normal" font="default" size="100%">Junker, K.</style></author><author><style face="normal" font="default" size="100%">Scott, M. P.</style></author><author><style face="normal" font="default" size="100%">Price, R. J.</style></author><author><style face="normal" font="default" size="100%">Meredith, C.</style></author><author><style face="normal" font="default" size="100%">Lake, B. G.</style></author><author><style face="normal" font="default" size="100%">Groten, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transcriptome analysis provides new insights into liver changes induced in the rat upon dietary administration of the food additives butylated hydroxytoluene, curcumin, propyl gallate and thiabendazole</style></title><secondary-title><style face="normal" font="default" size="100%">Food Chem Toxicol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals Aryl Hydrocarbon Hydroxylases/metabolism Body Weight/drug effects Butylated Hydroxytoluene/toxicity Curcumin/toxicity Cytochrome P-450 CYP1A2/metabolism Cytochrome P-450 CYP2B1/metabolism DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Complementary/biosynthesis/genetics Data Interpretation</style></keyword><keyword><style  face="normal" font="default" size="100%">Sprague-Dawley Reverse Transcriptase Polymerase Chain Reaction Steroid Hydroxylases/metabolism Thiabendazole/toxicity</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical *Diet Food Additives/*toxicity Gene Expression/drug effects *Gene Expression Profiling Glutathione Transferase/metabolism Liver/*drug effects Male Organ Size/drug effects Oxidation-Reduction Palmitoyl Coenzyme A/metabolism Propyl Gallate/toxi</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=18539377</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">8</style></number><volume><style face="normal" font="default" size="100%">46</style></volume><pages><style face="normal" font="default" size="100%">2616-28</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Transcriptomics was performed to gain insight into mechanisms of food additives butylated hydroxytoluene (BHT), curcumin (CC), propyl gallate (PG), and thiabendazole (TB), additives for which interactions in the liver can not be excluded. Additives were administered in diets for 28 days to Sprague-Dawley rats and cDNA microarray experiments were performed on hepatic RNA. BHT induced changes in the expression of 10 genes, including phase I (CYP2B1/2; CYP3A9; CYP2C6) and phase II metabolism (GST mu2). The CYP2B1/2 and GST expression findings were confirmed by real time RT-PCR, western blotting, and increased GST activity towards DCNB. CC altered the expression of 12 genes. Three out of these were related to peroxisomes (phytanoyl-CoA dioxygenase, enoyl-CoA hydratase; CYP4A3). Increased cyanide insensitive palmitoyl-CoA oxidation was observed, suggesting that CC is a weak peroxisome proliferator. TB changed the expression of 12 genes, including CYP1A2. In line, CYP1A2 protein expression was increased. The expression level of five genes, associated with p53 was found to change upon TB treatment, including p53 itself, GADD45alpha, DN-7, protein kinase C beta and serum albumin. These array experiments led to the novel finding that TB is capable of inducing p53 at the protein level, at least at the highest dose levels employed above the current NOAEL. The expression of eight genes changed upon PG administration. This study shows the value of gene expression profiling in food toxicology in terms of generating novel hypotheses on the mechanisms of action of food additives in relation to pathology.</style></abstract><notes><style face="normal" font="default" size="100%">Stierum, Rob Conesa, Ana Heijne, Wilbert Ommen, Ben van Junker, Karin Scott, Mary P Price, Roger J Meredith, Clive Lake, Brian G Groten, John Research Support, Non-U.S. Gov’t England Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association Food Chem Toxicol. 2008 Aug;46(8):2616-28. Epub 2008 Apr 25.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">E. Capriotti</style></author><author><style face="normal" font="default" size="100%">Arbiza, L.</style></author><author><style face="normal" font="default" size="100%">Casadio, R.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">H. Dopazo</style></author><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Use of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Mutat</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms Codon/genetics Computational Biology/*methods *DNA Mutational Analysis Databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Human Humans Iduronic Acid/analogs &amp; derivatives/metabolism *Point Mutation Polymorphism</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular *Genetic Predisposition to Disease Genetic Variation Genome</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein *Evolution</style></keyword><keyword><style  face="normal" font="default" size="100%">Single Nucleotide Proteins/chemistry/*genetics Tumor Suppressor Protein p53/genetics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17935148</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">29</style></volume><pages><style face="normal" font="default" size="100%">198-204</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Predicting the functional impact of protein variation is one of the most challenging problems in bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data, allowing the application of rigorous statistical approaches for predicting whether a given single point mutation has an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likelihood that a given protein variant is associated with human disease or not. Our method relies on a support vector machine (SVM) classifier trained using three sources of information: protein sequence, multiple protein sequence alignments, and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82% overall accuracy and a correlation coefficient of 0.59, indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at the codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT (available for download at http://sgu.bioinfo.cipf.es/services/Omidios/; last accessed: 24 August 2007), could be used to support clinical studies.</style></abstract><notes><style face="normal" font="default" size="100%">Capriotti, Emidio Arbiza, Leonardo Casadio, Rita Dopazo, Joaquin Dopazo, Hernan Marti-Renom, Marc A Evaluation Studies Research Support, Non-U.S. Gov’t United States Human mutation Hum Mutat. 2008 Jan;29(1):198-204.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Capriotti, Emidio</style></author><author><style face="normal" font="default" size="100%">Arbiza, Leonardo</style></author><author><style face="normal" font="default" size="100%">Casadio, Rita</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Dopazo, Hernán</style></author><author><style face="normal" font="default" size="100%">Marti-Renom, Marc A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Use of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Mutat</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Mutat</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Codon</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Mutational Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Evolution, Molecular</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Iduronic Acid</style></keyword><keyword><style  face="normal" font="default" size="100%">Point Mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Tumor Suppressor Protein p53</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">29</style></volume><pages><style face="normal" font="default" size="100%">198-204</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Predicting the functional impact of protein variation is one of the most challenging problems in bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data, allowing the application of rigorous statistical approaches for predicting whether a given single point mutation has an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likelihood that a given protein variant is associated with human disease or not. Our method relies on a support vector machine (SVM) classifier trained using three sources of information: protein sequence, multiple protein sequence alignments, and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82% overall accuracy and a correlation coefficient of 0.59, indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at the codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT (available for download at http://sgu.bioinfo.cipf.es/services/Omidios/; last accessed: 24 August 2007), could be used to support clinical studies.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/17935148?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Terol, J.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Colmenero, J. M.</style></author><author><style face="normal" font="default" size="100%">Cercos, M.</style></author><author><style face="normal" font="default" size="100%">Tadeo, F.</style></author><author><style face="normal" font="default" size="100%">Agusti, J.</style></author><author><style face="normal" font="default" size="100%">Alos, E.</style></author><author><style face="normal" font="default" size="100%">Andres, F.</style></author><author><style face="normal" font="default" size="100%">Soler, G.</style></author><author><style face="normal" font="default" size="100%">Brumos, J.</style></author><author><style face="normal" font="default" size="100%">Iglesias, D. J.</style></author><author><style face="normal" font="default" size="100%">Gotz, S.</style></author><author><style face="normal" font="default" size="100%">Legaz, F.</style></author><author><style face="normal" font="default" size="100%">Argout, X.</style></author><author><style face="normal" font="default" size="100%">Courtois, B.</style></author><author><style face="normal" font="default" size="100%">Ollitrault, P.</style></author><author><style face="normal" font="default" size="100%">Dossat, C.</style></author><author><style face="normal" font="default" size="100%">Wincker, P.</style></author><author><style face="normal" font="default" size="100%">Morillon, R.</style></author><author><style face="normal" font="default" size="100%">Talon, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of 13000 unique Citrus clusters associated with fruit quality, production and salinity tolerance</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Genomics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Acclimatization/*genetics Amino Acid Motifs Citrus/*genetics Cluster Analysis Expressed Sequence Tags Fruit/genetics Gene Duplication *Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Gene Library Genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Genomics Molecular Sequence Data Multigene Family Phylogeny *Salts/adverse effects</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17254327</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">31</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BACKGROUND: Improvement of Citrus, the most economically important fruit crop in the world, is extremely slow and inherently costly because of the long-term nature of tree breeding and an unusual combination of reproductive characteristics. Aside from disease resistance, major commercial traits in Citrus are improved fruit quality, higher yield and tolerance to environmental stresses, especially salinity. RESULTS: A normalized full length and 9 standard cDNA libraries were generated, representing particular treatments and tissues from selected varieties (Citrus clementina and C. sinensis) and rootstocks (C. reshni, and C. sinenis x Poncirus trifoliata) differing in fruit quality, resistance to abscission, and tolerance to salinity. The goal of this work was to provide a large expressed sequence tag (EST) collection enriched with transcripts related to these well appreciated agronomical traits. Towards this end, more than 54000 ESTs derived from these libraries were analyzed and annotated. Assembly of 52626 useful sequences generated 15664 putative transcription units distributed in 7120 contigs, and 8544 singletons. BLAST annotation produced significant hits for more than 80% of the hypothetical transcription units and suggested that 647 of these might be Citrus specific unigenes. The unigene set, composed of  13000 putative different transcripts, including more than 5000 novel Citrus genes, was assigned with putative functions based on similarity, GO annotations and protein domains CONCLUSION: Comparative genomics with Arabidopsis revealed the presence of putative conserved orthologs and single copy genes in Citrus and also the occurrence of both gene duplication events and increased number of genes for specific pathways. In addition, phylogenetic analysis performed on the ammonium transporter family and glycosyl transferase family 20 suggested the existence of Citrus paralogs. Analysis of the Citrus gene space showed that the most important metabolic pathways known to affect fruit quality were represented in the unigene set. Overall, the similarity analyses indicated that the sequences of the genes belonging to these varieties and rootstocks were essentially identical, suggesting that the differential behaviour of these species cannot be attributed to major sequence divergences. This Citrus EST assembly contributes both crucial information to discover genes of agronomical interest and tools for genetic and genomic analyses, such as the development of new markers and microarrays.</style></abstract><notes><style face="normal" font="default" size="100%">Terol, Javier Conesa, Ana Colmenero, Jose M Cercos, Manuel Tadeo, Francisco Agusti, Javier Alos, Enriqueta Andres, Fernando Soler, Guillermo Brumos, Javier Iglesias, Domingo J Gotz, Stefan Legaz, Francisco Argout, Xavier Courtois, Brigitte Ollitrault, Patrick Dossat, Carole Wincker, Patrick Morillon, Raphael Talon, Manuel Comparative Study Research Support, Non-U.S. Gov’t England BMC genomics BMC Genomics. 2007 Jan 25;8:31.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ruiz-Llorente, S.</style></author><author><style face="normal" font="default" size="100%">Montero-Conde, C.</style></author><author><style face="normal" font="default" size="100%">Milne, R. L.</style></author><author><style face="normal" font="default" size="100%">Moya, C. M.</style></author><author><style face="normal" font="default" size="100%">Cebrian, A.</style></author><author><style face="normal" font="default" size="100%">Leton, R.</style></author><author><style face="normal" font="default" size="100%">Cascon, A.</style></author><author><style face="normal" font="default" size="100%">Mercadillo, F.</style></author><author><style face="normal" font="default" size="100%">Landa, I.</style></author><author><style face="normal" font="default" size="100%">Borrego, S.</style></author><author><style face="normal" font="default" size="100%">Perez de Nanclares, G.</style></author><author><style face="normal" font="default" size="100%">Alvarez-Escola, C.</style></author><author><style face="normal" font="default" size="100%">Diaz-Perez, J. A.</style></author><author><style face="normal" font="default" size="100%">Carracedo, A.</style></author><author><style face="normal" font="default" size="100%">Urioste, M.</style></author><author><style face="normal" font="default" size="100%">Gonzalez-Neira, A.</style></author><author><style face="normal" font="default" size="100%">Benitez, J.</style></author><author><style face="normal" font="default" size="100%">Santisteban, P.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Ponder, B. A.</style></author><author><style face="normal" font="default" size="100%">M. Robledo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Association study of 69 genes in the ret pathway identifies low-penetrance loci in sporadic medullary thyroid carcinoma</style></title><secondary-title><style face="normal" font="default" size="100%">Cancer Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">80 and over Carcinoma</style></keyword><keyword><style  face="normal" font="default" size="100%">Adolescent Adult Aged Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Proto-Oncogene Proteins c-ret/*genetics/metabolism Signal Transduction Thyroid Neoplasms/*genetics/metabolism Transcription</style></keyword><keyword><style  face="normal" font="default" size="100%">Medullary/*genetics/metabolism Case-Control Studies Cyclin-Dependent Kinase Inhibitor p15/biosynthesis/genetics Female Genetic Predisposition to Disease Germ-Line Mutation Haplotypes Humans Male Middle Aged Penetrance Polymorphism</style></keyword><keyword><style  face="normal" font="default" size="100%">Single Nucleotide Promoter Regions</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17909067</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">19</style></number><volume><style face="normal" font="default" size="100%">67</style></volume><pages><style face="normal" font="default" size="100%">9561-7</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">To date, few association studies have been done to better understand the genetic basis for the development of sporadic medullary thyroid carcinoma (sMTC). To identify additional low-penetrance genes, we have done a two-stage case-control study in two European populations using high-throughput genotyping. We selected 417 single nucleotide polymorphisms (SNP) belonging to 69 genes either related to RET signaling pathway/functions or involved in key processes for cancer development. TagSNPs and functional variants were included where possible. These SNPs were initially studied in the largest known series of sMTC cases (n = 266) and controls (n = 422), all of Spanish origin. In stage II, an independent British series of 155 sMTC patients and 531 controls was included to validate the previous results. Associations were assessed by an exhaustive analysis of individual SNPs but also considering gene- and linkage disequilibrium-based haplotypes. This strategy allowed us to identify seven low-penetrance genes, six of them (STAT1, AURKA, BCL2, CDKN2B, CDK6, and COMT) consistently associated with sMTC risk in the two case-control series and a seventh (HRAS) with individual SNPs and haplotypes associated with sMTC in the Spanish data set. The potential role of CDKN2B was confirmed by a functional assay showing a role of a SNP (rs7044859) in the promoter region in altering the binding of the transcription factor HNF1. These results highlight the utility of association studies using homogeneous series of cases for better understanding complex diseases.</style></abstract><notes><style face="normal" font="default" size="100%">Ruiz-Llorente, Sergio Montero-Conde, Cristina Milne, Roger L Moya, Christian M Cebrian, Arancha Leton, Rocio Cascon, Alberto Mercadillo, Fatima Landa, Inigo Borrego, Salud Perez de Nanclares, Guiomar Alvarez-Escola, Cristina Diaz-Perez, Jose Angel Carracedo, Angel Urioste, Miguel Gonzalez-Neira, Anna Benitez, Javier Santisteban, Pilar Dopazo, Joaquin Ponder, Bruce A Robledo, Mercedes Medullary Thyroid Carcinoma Clinical Group Research Support, Non-U.S. Gov’t United States Cancer research Cancer Res. 2007 Oct 1;67(19):9561-7.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nueda, M. J.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Westerhuis, J. A.</style></author><author><style face="normal" font="default" size="100%">Hoefsloot, H. C.</style></author><author><style face="normal" font="default" size="100%">Smilde, A. K.</style></author><author><style face="normal" font="default" size="100%">Talon, M.</style></author><author><style face="normal" font="default" size="100%">Ferrer, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discovering gene expression patterns in time course microarray experiments by ANOVA-SCA</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms *Analysis of Variance Computational Biology/*methods Computer Simulation Data Interpretation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Gene Expression Profiling/*methods Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Oligonucleotide Array Sequence Analysis/*methods Principal Component Analysis Time Factors Transcription</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17519250</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">14</style></number><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">1792-800</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">MOTIVATION: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account. RESULTS: In this work, we develop the application of the Analysis of variance-simultaneous component analysis (ANOVA-SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets. AVAILABILITY: ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.</style></abstract><notes><style face="normal" font="default" size="100%">Nueda, Maria Jose Conesa, Ana Westerhuis, Johan A Hoefsloot, Huub C J Smilde, Age K Talon, Manuel Ferrer, Alberto Research Support, Non-U.S. Gov’t England Bioinformatics (Oxford, England) Bioinformatics. 2007 Jul 15;23(14):1792-800. Epub 2007 May 22.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hernandez, P.</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, J.</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Valls, J.</style></author><author><style face="normal" font="default" size="100%">Gomez, L.</style></author><author><style face="normal" font="default" size="100%">Capella, G.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Pujana, M. A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evidence for systems-level molecular mechanisms of tumorigenesis</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Genomics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Cell Transformation</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Messenger/metabolism Signal Transduction Systems Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplastic *Gene Expression Profiling *Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplastic Humans Male Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Neoplasm Proteins/*physiology Neoplasms/etiology/*genetics Prostatic Neoplasms/genetics Protein Interaction Mapping RNA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17584915</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">185</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BACKGROUND: Cancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth. RESULTS: Here, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as &quot;CGPs&quot;) defined as their topological properties and organization in the interactome network. We show that CGPs are central to information exchange and propagation and that they are specifically organized to promote tumorigenesis. Centrality is identified by both local (degree) and global (betweenness and closeness) measures, and systematically appears in down-regulated CGPs. Up-regulated CGPs do not consistently exhibit centrality, but both types of cancer products determine the overall integrity of the network structure. In addition to centrality, down-regulated CGPs show topological association that correlates with common biological processes and pathways involved in tumorigenesis. CONCLUSION: Given the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.</style></abstract><notes><style face="normal" font="default" size="100%">Hernandez, Pilar Huerta-Cepas, Jaime Montaner, David Al-Shahrour, Fatima Valls, Joan Gomez, Laia Capella, Gabriel Dopazo, Joaquin Pujana, Miguel Angel Research Support, Non-U.S. Gov’t England BMC genomics BMC Genomics. 2007 Jun 20;8:185.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hernández, Pilar</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, Jaime</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">Valls, Joan</style></author><author><style face="normal" font="default" size="100%">Gómez, Laia</style></author><author><style face="normal" font="default" size="100%">Capellà, Gabriel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Pujana, Miguel Angel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evidence for systems-level molecular mechanisms of tumorigenesis.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Genomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Genomics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cell Transformation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Statistical</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasm Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Prostatic Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2007 Jun 20</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">185</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Cancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Here, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as &quot;CGPs&quot;) defined as their topological properties and organization in the interactome network. We show that CGPs are central to information exchange and propagation and that they are specifically organized to promote tumorigenesis. Centrality is identified by both local (degree) and global (betweenness and closeness) measures, and systematically appears in down-regulated CGPs. Up-regulated CGPs do not consistently exhibit centrality, but both types of cancer products determine the overall integrity of the network structure. In addition to centrality, down-regulated CGPs show topological association that correlates with common biological processes and pathways involved in tumorigenesis.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Given the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/17584915?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Conde, Lucia</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Burguet-Castell, Jordi</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Functional profiling and gene expression analysis of chromosomal copy number alterations.</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformation</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Bioinformation</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2007 Apr 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">432-5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Contrarily to the traditional view in which only one or a few key genes were supposed to be the causative factors of diseases, we discuss the importance of considering groups of functionally related genes in the study of pathologies characterised by chromosomal copy number alterations. Recent observations have reported the existence of regions in higher eukaryotic chromosomes (including humans) containing genes of related function that show a high degree of coregulation. Copy number alterations will consequently affect to clusters of functionally related genes, which will be the final causative agents of the diseased phenotype, in many cases. Therefore, we propose that the functional profiling of the regions affected by copy number alterations must be an important aspect to take into account in the understanding of this type of pathologies. To illustrate this, we present an integrated study of DNA copy number variations, gene expression along with the functional profiling of chromosomal regions in a case of multiple myeloma.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/17597935?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">L. Conde</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Burguet-Castell, J.</style></author><author><style face="normal" font="default" size="100%">Tarraga, J.</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Functional profiling and gene expression analysis of chromosomal copy number alterations</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformation</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17597935</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">10</style></number><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">432-5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Contrarily to the traditional view in which only one or a few key genes were supposed to be the causative factors of diseases, we discuss the importance of considering groups of functionally related genes in the study of pathologies characterised by chromosomal copy number alterations. Recent observations have reported the existence of regions in higher eukaryotic chromosomes (including humans) containing genes of related function that show a high degree of coregulation. Copy number alterations will consequently affect to clusters of functionally related genes, which will be the final causative agents of the diseased phenotype, in many cases. Therefore, we propose that the functional profiling of the regions affected by copy number alterations must be an important aspect to take into account in the understanding of this type of pathologies. To illustrate this, we present an integrated study of DNA copy number variations, gene expression along with the functional profiling of chromosomal regions in a case of multiple myeloma.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Conde, Lucia Montaner, David Burguet-Castell, Jordi Tarraga, Joaquin Al-Shahrour, Fatima Dopazo, Joaquin Singapore Bioinformation Bioinformation. 2007 Apr 10;1(10):432-5.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">L. Conde</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Burguet-Castell, J.</style></author><author><style face="normal" font="default" size="100%">Tarraga, J.</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ISACGH: a web-based environment for the analysis of Array CGH and gene expression which includes functional profiling</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals Cluster Analysis Computational Biology/*methods Computer Graphics Gene Expression Profiling/*methods Humans Internet Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic *Nucleic Acid Hybridization Oligonucleotide Array Sequence Analysis/*methods Programming Languages *Software Systems Integration User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17468499</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">Web Server issue</style></number><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">W81-5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present the ISACGH, a web-based system that allows for the combination of genomic data with gene expression values and provides different options for functional profiling of the regions found. Several visualization options offer a convenient representation of the results. Different efficient methods for accurate estimation of genomic copy number from array-CGH hybridization data have been included in the program. Moreover, the connection to the gene expression analysis package GEPAS allows the use of different facilities for data pre-processing and analysis. A DAS server allows exporting the results to the Ensembl viewer where contextual genomic information can be obtained. The program is freely available at: http://isacgh.bioinfo.cipf.es or within http://www.gepas.org.</style></abstract><notes><style face="normal" font="default" size="100%">Conde, Lucia Montaner, David Burguet-Castell, Jordi Tarraga, Joaquin Medina, Ignacio Al-Shahrour, Fatima Dopazo, Joaquin Research Support, Non-U.S. Gov’t England Nucleic acids research Nucleic Acids Res. 2007 Jul;35(Web Server issue):W81-5. Epub 2007 Apr 27.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Conde, Lucia</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Burguet-Castell, Jordi</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ISACGH: a web-based environment for the analysis of Array CGH and gene expression which includes functional profiling.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Cluster Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Graphics</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Nucleic Acid Hybridization</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Programming Languages</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems Integration</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2007 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">W81-5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present the ISACGH, a web-based system that allows for the combination of genomic data with gene expression values and provides different options for functional profiling of the regions found. Several visualization options offer a convenient representation of the results. Different efficient methods for accurate estimation of genomic copy number from array-CGH hybridization data have been included in the program. Moreover, the connection to the gene expression analysis package GEPAS allows the use of different facilities for data pre-processing and analysis. A DAS server allows exporting the results to the Ensembl viewer where contextual genomic information can be obtained. The program is freely available at: http://isacgh.bioinfo.cipf.es or within http://www.gepas.org.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Web Server issue</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/17468499?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">J. Forment</style></author><author><style face="normal" font="default" size="100%">J. Gadea</style></author><author><style face="normal" font="default" size="100%">van Dijk, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Microarray Technology in Agricultural Research</style></title><secondary-title><style face="normal" font="default" size="100%">Microarray Technology Through Applications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">F. Falciani. Publisher: Taylor and Francis Group</style></publisher><pages><style face="normal" font="default" size="100%">173-209</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Levin, A. M.</style></author><author><style face="normal" font="default" size="100%">de Vries, R. P.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">de Bekker, C.</style></author><author><style face="normal" font="default" size="100%">Talon, M.</style></author><author><style face="normal" font="default" size="100%">Menke, H. H.</style></author><author><style face="normal" font="default" size="100%">van Peij, N. N.</style></author><author><style face="normal" font="default" size="100%">Wosten, H. A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatial differentiation in the vegetative mycelium of Aspergillus niger</style></title><secondary-title><style face="normal" font="default" size="100%">Eukaryot Cell</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aspergillus niger/*metabolism Cell Wall/metabolism Fungal Proteins/metabolism *Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological Mycelium/*metabolism Oligonucleotide Array Sequence Analysis RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Fungal Genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Fungal Genome</style></keyword><keyword><style  face="normal" font="default" size="100%">Fungal Glucans/chemistry Maltose/chemistry Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Fungal Time Factors Trans-Activators/metabolism Xylose/chemistry</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17951513</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">12</style></number><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">2311-22</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Fungal mycelia are exposed to heterogenic substrates. The substrate in the central part of the colony has been (partly) degraded, whereas it is still unexplored at the periphery of the mycelium. We here assessed whether substrate heterogeneity is a main determinant of spatial gene expression in colonies of Aspergillus niger. This question was addressed by analyzing whole-genome gene expression in five concentric zones of 7-day-old maltose- and xylose-grown colonies. Expression profiles at the periphery and the center were clearly different. More than 25% of the active genes showed twofold differences in expression between the inner and outermost zones of the colony. Moreover, 9% of the genes were expressed in only one of the five concentric zones, showing that a considerable part of the genome is active in a restricted part of the colony only. Statistical analysis of expression profiles of colonies that had either been or not been transferred to fresh xylose-containing medium showed that differential expression in a colony is due to the heterogeneity of the medium (e.g., genes involved in secretion, genes encoding proteases, and genes involved in xylose metabolism) as well as to medium-independent mechanisms (e.g., genes involved in nitrate metabolism and genes involved in cell wall synthesis and modification). Thus, we conclude that the mycelia of 7-day-old colonies of A. niger are highly differentiated. This conclusion is also indicated by the fact that distinct zones of the colony grow and secrete proteins, even after transfer to fresh medium.</style></abstract><notes><style face="normal" font="default" size="100%">Levin, Ana M de Vries, Ronald P Conesa, Ana de Bekker, Charissa Talon, Manuel Menke, Hildegard H van Peij, Noel N M E Wosten, Han A B Research Support, Non-U.S. Gov’t United States Eukaryotic cell Eukaryot Cell. 2007 Dec;6(12):2311-22. Epub 2007 Oct 19.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gandia, M.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Ancillo, G.</style></author><author><style face="normal" font="default" size="100%">J. Gadea</style></author><author><style face="normal" font="default" size="100%">J. Forment</style></author><author><style face="normal" font="default" size="100%">Pallas, V.</style></author><author><style face="normal" font="default" size="100%">Flores, R.</style></author><author><style face="normal" font="default" size="100%">Duran-Vila, N.</style></author><author><style face="normal" font="default" size="100%">Moreno, P.</style></author><author><style face="normal" font="default" size="100%">Guerri, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transcriptional response of Citrus aurantifolia to infection by Citrus tristeza virus</style></title><secondary-title><style face="normal" font="default" size="100%">Virology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Citrus/*genetics/physiology/virology Closterovirus/genetics/*physiology Genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Oligonucleotide Array Sequence Analysis Reverse Transcriptase Polymerase Chain Reaction *Transcription</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17617431</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">367</style></volume><pages><style face="normal" font="default" size="100%">298-306</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Changes in gene expression of Mexican lime plants in response to infection with a severe (T305) or a mild (T385) isolate of Citrus tristeza virus (CTV) were analyzed using a cDNA microarray containing 12,672 probes to 6875 different citrus genes. Statistically significant (P&lt;0.01) expression changes of 334 genes were detected in response to infection with isolate T305, whereas infection with T385 induced no significant change. Induced genes included 145 without significant similarity with known sequences and 189 that were classified in seven functional categories. Genes related with response to stress and defense were the main category and included 28% of the genes induced. Selected transcription changes detected by microarray analysis were confirmed by quantitative real-time RT-PCR. Changes detected in the transcriptome upon infecting lime with T305 may be associated either with symptom expression, with a strain-specific defense mechanism, or with a general response to stress.</style></abstract><notes><style face="normal" font="default" size="100%">Gandia, Monica Conesa, Ana Ancillo, Gema Gadea, Jose Forment, Javier Pallas, Vicente Flores, Ricardo Duran-Vila, Nuria Moreno, Pedro Guerri, Jose Research Support, Non-U.S. Gov’t United States Virology Virology. 2007 Oct 25;367(2):298-306. Epub 2007 Jul 9.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Minguez, P.</style></author><author><style face="normal" font="default" size="100%">Tarraga, J.</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Alloza, E.</style></author><author><style face="normal" font="default" size="100%">Vaquerizas, J. M.</style></author><author><style face="normal" font="default" size="100%">L. Conde</style></author><author><style face="normal" font="default" size="100%">Blaschke, C.</style></author><author><style face="normal" font="default" size="100%">Vera, J.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword><keyword><style  face="normal" font="default" size="100%">functional profiling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/content/34/suppl_2/W472.long</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">W472-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present a new version of Babelomics, a complete suite of web tools for functional analysis of genome-scale experiments, with new and improved tools. New functionally relevant terms have been included such as CisRed motifs or bioentities obtained by text-mining procedures. An improved indexing has considerably speeded up several of the modules. An improved version of the FatiScan method for studying the coordinate behaviour of groups of functionally related genes is presented, along with a similar tool, the Gene Set Enrichment Analysis. Babelomics is now more oriented to test systems biology inspired hypotheses. Babelomics can be found at http://www.babelomics.org.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Al-Shahrour, Fatima Minguez, Pablo Tarraga, Joaquin Montaner, David Alloza, Eva Vaquerizas, Juan M Conde, Lucia Blaschke, Christian Vera, Javier Dopazo, Joaquin Research Support, Non-U.S. Gov’t England Nucleic acids research Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W472-6.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aparicio, G.</style></author><author><style face="normal" font="default" size="100%">Gotz, S.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Segrelles, D.</style></author><author><style face="normal" font="default" size="100%">Blanquer, I.</style></author><author><style face="normal" font="default" size="100%">Garcia, J. M.</style></author><author><style face="normal" font="default" size="100%">Hernandez, V.</style></author><author><style face="normal" font="default" size="100%">Robles, M.</style></author><author><style face="normal" font="default" size="100%">Talon, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Blast2GO goes grid: developing a grid-enabled prototype for functional genomics analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Stud Health Technol Inform</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=16823138</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">120</style></volume><pages><style face="normal" font="default" size="100%">194-204</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The vast amount in complexity of data generated in Genomic Research implies that new dedicated and powerful computational tools need to be developed to meet their analysis requirements. Blast2GO (B2G) is a bioinformatics tool for Gene Ontology-based DNA or protein sequence annotation and function-based data mining. The application has been developed with the aim of affering an easy-to-use tool for functional genomics research. Typical B2G users are middle size genomics labs carrying out sequencing, ETS and microarray projects, handling datasets up to several thousand sequences. In the current version of B2G. The power and analytical potential of both annotation and function data-mining is somehow restricted to the computational power behind each particular installation. In order to be able to offer the possibility of an enhanced computational capacity within this bioinformatics application, a Grid component is being developed. A prototype has been conceived for the particular problem of speeding up the Blast searches to obtain fast results for large datasets. Many efforts have been done in the literature concerning the speeding up of Blast searches, but few of them deal with the use of large heterogeneous production Grid Infrastructures. These are the infrastructures that could reach the largest number of resources and the best load balancing for data access. The Grid Service under development will analyse requests based on the number of sequences, splitting them accordingly to the available resources. Lower-level computation will be performed through MPIBLAST. The software architecture is based on the WSRF standard.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Aparicio, G Gotz, S Conesa, A Segrelles, D Blanquer, I Garcia, J M Hernandez, V Robles, M Talon, M Netherlands Studies in health technology and informatics Stud Health Technol Inform. 2006;120:194-204.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Williams, T. D.</style></author><author><style face="normal" font="default" size="100%">Diab, A. M.</style></author><author><style face="normal" font="default" size="100%">George, S. G.</style></author><author><style face="normal" font="default" size="100%">Godfrey, R. E.</style></author><author><style face="normal" font="default" size="100%">Sabine, V.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Minchin, S. D.</style></author><author><style face="normal" font="default" size="100%">Watts, P. C.</style></author><author><style face="normal" font="default" size="100%">Chipman, J. K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of the GENIPOL European flounder (Platichthys flesus) microarray and determination of temporal transcriptional responses to cadmium at low dose</style></title><secondary-title><style face="normal" font="default" size="100%">Environ Sci Technol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals Cadmium Chloride/administration &amp; dosage/*pharmacology Dose-Response Relationship</style></keyword><keyword><style  face="normal" font="default" size="100%">Developmental/drug effects Liver/drug effects/growth &amp; development/metabolism Oligonucleotide Array Sequence Analysis/*methods Reverse Transcriptase Polymerase Chain Reaction Transcription</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug Environmental Monitoring/methods Flounder/*genetics/growth &amp; development Gene Expression Profiling Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic/*drug effects</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17120584</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">20</style></number><volume><style face="normal" font="default" size="100%">40</style></volume><pages><style face="normal" font="default" size="100%">6479-88</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We have constructed a high density, 13 270-clone cDNA array for the sentinel fish species European flounder (Platichthys flesus), combining clones from suppressive subtractive hybridization and a liver cDNA library; DNA sequences of 5211 clones were determined. Fish were treated by single intraperitoneal injection with 50 micrograms cadmium chloride per kilogram body weight, a dose relevant to environmental exposures, and hepatic gene expression changes were determined at 1, 2, 4, 8, and 16 days postinjection in comparison to saline-treated controls. Gene expression responses were confirmed by real-time reverse transcription polymerase chain reaction (RT-PCR). Blast2GO gene ontology analysis highlighted a general induction of the unfolded protein response, response to oxidative stress, protein synthesis, transport, and degradation pathways, while apoptosis, cell cycle, cytoskeleton, and cytokine genes were also affected. Transcript levels of cytochrome P450 1A (CYP1A) were repressed and vitellogenin altered, real-time PCR showed induction of metallothionein. We thus describe the establishment of a useful resource for ecotoxicogenomics and the determination of the temporal molecular responses to cadmium, a prototypical heavy metal pollutant.</style></abstract><notes><style face="normal" font="default" size="100%">Williams, Tim D Diab, Amer M George, Stephen G Godfrey, Rita E Sabine, Victoria Conesa, Ana Minchin, Steven D Watts, Phil C Chipman, James K Research Support, Non-U.S. Gov’t United States Environmental science &amp; technology Environ Sci Technol. 2006 Oct 15;40(20):6479-88.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Largo, C.</style></author><author><style face="normal" font="default" size="100%">Alvarez, S.</style></author><author><style face="normal" font="default" size="100%">Saez, B.</style></author><author><style face="normal" font="default" size="100%">Blesa, D.</style></author><author><style face="normal" font="default" size="100%">Martin-Subero, J. I.</style></author><author><style face="normal" font="default" size="100%">Gonzalez-Garcia, I.</style></author><author><style face="normal" font="default" size="100%">Brieva, J. A.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Siebert, R.</style></author><author><style face="normal" font="default" size="100%">Calasanz, M. J.</style></author><author><style face="normal" font="default" size="100%">Cigudosa, J. C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identification of overexpressed genes in frequently gained/amplified chromosome regions in multiple myeloma</style></title><secondary-title><style face="normal" font="default" size="100%">Haematologica</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">B-Cell</style></keyword><keyword><style  face="normal" font="default" size="100%">Caspases Cell Line</style></keyword><keyword><style  face="normal" font="default" size="100%">Human *Gene Amplification Gene Dosage Gene Expression Profiling *Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Marginal Zone/genetics Multiple Myeloma/*genetics Neoplasm Proteins/genetics Proto-Oncogene Proteins c-bcl-2/genetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasm Humans Immunoglobulin Heavy Chains/genetics Lymphoma</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplastic Gene Rearrangement *Genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Tumor *Chromosomes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=16461302</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">91</style></volume><pages><style face="normal" font="default" size="100%">184-91</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BACKGROUND AND OBJECTIVES: Multiple myeloma (MM) is a malignancy characterized by clonal expansion of plasma cells. In 50% of the cases, the neoplastic transformation begins with a chromosomal translocation that juxtaposes the IGH gene locus to an oncogene. Gene copy number changes are also frequent in MM but less characterized than in other neoplasias. We aimed to characterize genes that are amplified and overexpressed in human myeloma cell lines (HMCL) to provide putative molecular targets for MM therapy. DESIGN AND METHODS: Nine HMCL were characterized by fluorescent in situ hybridization, comparative genomic hybridization (CGH) and cDNA microarrays for gene expression profiling and copy number changes. RESULTS: After defining the IGH-translocations present in the cell lines, we conducted expression-profiling analysis. Supervised analysis identified 166 genes with significantly different expression among the cell lines harboring MMSET/FGFR3 (4p16), MAF (16q) and CCND1 (11q13) rearrangements. Array-CGH was then performed. Five chromosomes recurrently affected by gains/amplifications in primary samples and cell lines were analyzed in detail. Sixty amplified and overexpressed genes were found and 25 (42%) of them were only overexpressed when amplified; moreover, six showed a significant association between overexpression and gain/amplification. We also found co-amplification and overexpression for genes located within the same amplicons, such as MALT1 and BCL2. INTERPRETATION AND CONCLUSIONS: Parallel analysis of gene copy numbers and expression levels by cDNA microarray in MM allowed efficient identification of genes whose expression levels are elevated because of increased copy number. This is the first time that MALT1 and BCL2 have been shown to be overexpressed and amplified in MM.</style></abstract><notes><style face="normal" font="default" size="100%">Largo, Cristina Alvarez, Sara Saez, Borja Blesa, David Martin-Subero, Jose I Gonzalez-Garcia, Ines Brieva, Jose A Dopazo, Joaquin Siebert, Reiner Calasanz, Maria J Cigudosa, Juan C Research Support, Non-U.S. Gov’t Italy Haematologica Haematologica. 2006 Feb;91(2):184-91.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Nueda, M. J.</style></author><author><style face="normal" font="default" size="100%">Ferrer, A.</style></author><author><style face="normal" font="default" size="100%">Talon, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Algorithms Computer Simulation Gene Expression/*physiology Gene Expression Profiling/*methods *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Oligonucleotide Array Sequence Analysis/*methods *Software Time Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=16481333</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">9</style></number><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">1096-102</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">MOTIVATION: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis. RESULTS: In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset.</style></abstract><notes><style face="normal" font="default" size="100%">Conesa, Ana Nueda, Maria Jose Ferrer, Alberto Talon, Manuel England Bioinformatics (Oxford, England) Bioinformatics. 2006 May 1;22(9):1096-102. Epub 2006 Feb 15.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Tarraga, J.</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, J.</style></author><author><style face="normal" font="default" size="100%">Burguet, J.</style></author><author><style face="normal" font="default" size="100%">Vaquerizas, J. M.</style></author><author><style face="normal" font="default" size="100%">L. Conde</style></author><author><style face="normal" font="default" size="100%">Minguez, P.</style></author><author><style face="normal" font="default" size="100%">Vera, J.</style></author><author><style face="normal" font="default" size="100%">Mukherjee, S.</style></author><author><style face="normal" font="default" size="100%">Valls, J.</style></author><author><style face="normal" font="default" size="100%">Pujana, M. A.</style></author><author><style face="normal" font="default" size="100%">Alloza, E.</style></author><author><style face="normal" font="default" size="100%">Herrero, J.</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Next station in microarray data analysis: GEPAS</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">gepas</style></keyword><keyword><style  face="normal" font="default" size="100%">microarray data analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=16845056</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">W486-91</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Gene Expression Profile Analysis Suite (GEPAS) has been running for more than four years. During this time it has evolved to keep pace with the new interests and trends in the still changing world of microarray data analysis. GEPAS has been designed to provide an intuitive although powerful web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-colour microarray experiments and other preprocessing options), to the final step of the functional annotation of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), and include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management. GEPAS is extensively used by researchers of many countries and its records indicate an average usage rate of 400 experiments per day. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Montaner, David Tarraga, Joaquin Huerta-Cepas, Jaime Burguet, Jordi Vaquerizas, Juan M Conde, Lucia Minguez, Pablo Vera, Javier Mukherjee, Sach Valls, Joan Pujana, Miguel A G Alloza, Eva Herrero, Javier Al-Shahrour, Fatima Dopazo, Joaquin Research Support, Non-U.S. Gov’t England Nucleic acids research Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W486-91.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">L. Conde</style></author><author><style face="normal" font="default" size="100%">Vaquerizas, J. M.</style></author><author><style face="normal" font="default" size="100%">H. Dopazo</style></author><author><style face="normal" font="default" size="100%">Arbiza, L.</style></author><author><style face="normal" font="default" size="100%">Reumers, J.</style></author><author><style face="normal" font="default" size="100%">Rousseau, F.</style></author><author><style face="normal" font="default" size="100%">Schymkowitz, J.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PupaSuite: finding functional single nucleotide polymorphisms for large-scale genotyping purposes</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms Computer Graphics Databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Genotype Haplotypes Internet Linkage Disequilibrium *Polymorphism</style></keyword><keyword><style  face="normal" font="default" size="100%">Nucleic Acid Evolution</style></keyword><keyword><style  face="normal" font="default" size="100%">Single Nucleotide *Software User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/cgi/content/full/34/suppl_2/W621</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">Web Server issue</style></number><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">W621-5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We have developed a web tool, PupaSuite, for the selection of single nucleotide polymorphisms (SNPs) with potential phenotypic effect, specifically oriented to help in the design of large-scale genotyping projects. PupaSuite uses a collection of data on SNPs from heterogeneous sources and a large number of pre-calculated predictions to offer a flexible and intuitive interface for selecting an optimal set of SNPs. It improves the functionality of PupaSNP and PupasView programs and implements new facilities such as the analysis of user’s data to derive haplotypes with functional information. A new estimator of putative effect of polymorphisms has been included that uses evolutionary information. Also SNPeffect database predictions have been included. The PupaSuite web interface is accessible through http://pupasuite.bioinfo.cipf.es and through http://www.pupasnp.org.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Conde, Lucia Vaquerizas, Juan M Dopazo, Hernan Arbiza, Leonardo Reumers, Joke Rousseau, Frederic Schymkowitz, Joost Dopazo, Joaquin Research Support, Non-U.S. Gov’t England Nucleic acids research Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W621-5.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Topf, M.</style></author><author><style face="normal" font="default" size="100%">Baker, M. L.</style></author><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author><author><style face="normal" font="default" size="100%">Chiu, W.</style></author><author><style face="normal" font="default" size="100%">Sali, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Refinement of protein structures by iterative comparative modeling and CryoEM density fitting</style></title><secondary-title><style face="normal" font="default" size="100%">J Mol Biol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acid Sequence Cryoelectron Microscopy *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Molecular Sequence Data Plant Viruses/chemistry *Protein Conformation Software Viral Proteins/*chemistry/genetics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=16490207</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">357</style></volume><pages><style face="normal" font="default" size="100%">1655-68</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We developed a method for structure characterization of assembly components by iterative comparative protein structure modeling and fitting into cryo-electron microscopy (cryoEM) density maps. Specifically, we calculate a comparative model of a given component by considering many alternative alignments between the target sequence and a related template structure while optimizing the fit of a model into the corresponding density map. The method relies on the previously developed Moulder protocol that iterates over alignment, model building, and model assessment. The protocol was benchmarked using 20 varied target-template pairs of known structures with less than 30% sequence identity and corresponding simulated density maps at resolutions from 5A to 25A. Relative to the models based on the best existing sequence profile alignment methods, the percentage of C(alpha) atoms that are within 5A of the corresponding C(alpha) atoms in the superposed native structure increases on average from 52% to 66%, which is half-way between the starting models and the models from the best possible alignments (82%). The test also reveals that despite the improvements in the accuracy of the fitness function, this function is still the bottleneck in reducing the remaining errors. To demonstrate the usefulness of the protocol, we applied it to the upper domain of the P8 capsid protein of rice dwarf virus that has been studied by cryoEM at 6.8A. The C(alpha) root-mean-square deviation of the model based on the remotely related template, bluetongue virus VP7, improved from 8.7A to 6.0A, while the best possible model has a C(alpha) RMSD value of 5.3A. Moreover, the resulting model fits better into the cryoEM density map than the initial template structure. The method is being implemented in our program MODELLER for protein structure modeling by satisfaction of spatial restraints and will be applicable to the rapidly increasing number of cryoEM density maps of macromolecular assemblies.</style></abstract><notes><style face="normal" font="default" size="100%">Topf, Maya Baker, Matthew L Marti-Renom, Marc A Chiu, Wah Sali, Andrej 2 PN2 EY016525-02/EY/NEI NIH HHS/United States P20RR020647/RR/NCRR NIH HHS/United States P41RR02250/RR/NCRR NIH HHS/United States R01 GM54762/GM/NIGMS NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, Non-P.H.S. England Journal of molecular biology J Mol Biol. 2006 Apr 14;357(5):1655-68. Epub 2006 Feb 2.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Minguez, P.</style></author><author><style face="normal" font="default" size="100%">Vaquerizas, J. M.</style></author><author><style face="normal" font="default" size="100%">L. Conde</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword><keyword><style  face="normal" font="default" size="100%">functional profiling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/content/33/suppl_2/W460.long</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">W460-4</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present Babelomics, a complete suite of web tools for the functional analysis of groups of genes in high-throughput experiments, which includes the use of information on Gene Ontology terms, interpro motifs, KEGG pathways, Swiss-Prot keywords, analysis of predicted transcription factor binding sites, chromosomal positions and presence in tissues with determined histological characteristics, through five integrated modules: FatiGO (fast assignment and transference of information), FatiWise, transcription factor association test, GenomeGO and tissues mining tool, respectively. Additionally, another module, FatiScan, provides a new procedure that integrates biological information in combination with experimental results in order to find groups of genes with modest but coordinate significant differential behaviour. FatiScan is highly sensitive and is capable of finding significant asymmetries in the distribution of genes of common function across a list of ordered genes even if these asymmetries were not extreme. The strong multiple-testing nature of the contrasts made by the tools is taken into account. All the tools are integrated in the gene expression analysis package GEPAS. Babelomics is the natural evolution of our tool FatiGO (which analysed almost 22,000 experiments during the last year) to include more sources on information and new modes of using it. Babelomics can be found at http://www.babelomics.org.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Al-Shahrour, Fatima Minguez, Pablo Vaquerizas, Juan M Conde, Lucia Dopazo, Joaquin Research Support, Non-U.S. Gov’t England Nucleic acids research Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W460-4.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Gotz, S.</style></author><author><style face="normal" font="default" size="100%">Garcia-Gomez, J. M.</style></author><author><style face="normal" font="default" size="100%">Terol, J.</style></author><author><style face="normal" font="default" size="100%">Talon, M.</style></author><author><style face="normal" font="default" size="100%">Robles, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=16081474</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">18</style></number><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">3674-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;SUMMARY: We present here Blast2GO (B2G), a research tool designed with the main purpose of enabling Gene Ontology (GO) based data mining on sequence data for which no GO annotation is yet available. B2G joints in one application GO annotation based on similarity searches with statistical analysis and highlighted visualization on directed acyclic graphs. This tool offers a suitable platform for functional genomics research in non-model species. B2G is an intuitive and interactive desktop application that allows monitoring and comprehension of the whole annotation and analysis process. AVAILABILITY: Blast2GO is freely available via Java Web Start at http://www.blast2go.de. SUPPLEMENTARY MATERIAL: http://www.blast2go.de -&amp;gt; Evaluation.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Conesa, Ana Gotz, Stefan Garcia-Gomez, Juan Miguel Terol, Javier Talon, Manuel Robles, Montserrat Research Support, Non-U.S. Gov’t England Bioinformatics (Oxford, England) Bioinformatics. 2005 Sep 15;21(18):3674-6. Epub 2005 Aug 4.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Forment</style></author><author><style face="normal" font="default" size="100%">J. Gadea</style></author><author><style face="normal" font="default" size="100%">Huerta, L.</style></author><author><style face="normal" font="default" size="100%">Abizanda, L.</style></author><author><style face="normal" font="default" size="100%">Agusti, J.</style></author><author><style face="normal" font="default" size="100%">Alamar, S.</style></author><author><style face="normal" font="default" size="100%">Alos, E.</style></author><author><style face="normal" font="default" size="100%">Andres, F.</style></author><author><style face="normal" font="default" size="100%">Arribas, R.</style></author><author><style face="normal" font="default" size="100%">Beltran, J. P.</style></author><author><style face="normal" font="default" size="100%">Berbel, A.</style></author><author><style face="normal" font="default" size="100%">Blazquez, M. A.</style></author><author><style face="normal" font="default" size="100%">Brumos, J.</style></author><author><style face="normal" font="default" size="100%">Canas, L. A.</style></author><author><style face="normal" font="default" size="100%">Cercos, M.</style></author><author><style face="normal" font="default" size="100%">Colmenero-Flores, J. M.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Estables, B.</style></author><author><style face="normal" font="default" size="100%">Gandia, M.</style></author><author><style face="normal" font="default" size="100%">Garcia-Martinez, J. L.</style></author><author><style face="normal" font="default" size="100%">Gimeno, J.</style></author><author><style face="normal" font="default" size="100%">Gisbert, A.</style></author><author><style face="normal" font="default" size="100%">Gomez, G.</style></author><author><style face="normal" font="default" size="100%">Gonzalez-Candelas, L.</style></author><author><style face="normal" font="default" size="100%">Granell, A.</style></author><author><style face="normal" font="default" size="100%">Guerri, J.</style></author><author><style face="normal" font="default" size="100%">Lafuente, M. T.</style></author><author><style face="normal" font="default" size="100%">Madueno, F.</style></author><author><style face="normal" font="default" size="100%">Marcos, J. F.</style></author><author><style face="normal" font="default" size="100%">Marques, M. C.</style></author><author><style face="normal" font="default" size="100%">Martinez, F.</style></author><author><style face="normal" font="default" size="100%">Martinez-Godoy, M. A.</style></author><author><style face="normal" font="default" size="100%">Miralles, S.</style></author><author><style face="normal" font="default" size="100%">Moreno, P.</style></author><author><style face="normal" font="default" size="100%">Navarro, L.</style></author><author><style face="normal" font="default" size="100%">Pallas, V.</style></author><author><style face="normal" font="default" size="100%">Perez-Amador, M. A.</style></author><author><style face="normal" font="default" size="100%">Perez-Valle, J.</style></author><author><style face="normal" font="default" size="100%">Pons, C.</style></author><author><style face="normal" font="default" size="100%">Rodrigo, I.</style></author><author><style face="normal" font="default" size="100%">Rodriguez, P. L.</style></author><author><style face="normal" font="default" size="100%">Royo, C.</style></author><author><style face="normal" font="default" size="100%">Serrano, R.</style></author><author><style face="normal" font="default" size="100%">Soler, G.</style></author><author><style face="normal" font="default" size="100%">Tadeo, F.</style></author><author><style face="normal" font="default" size="100%">Talon, M.</style></author><author><style face="normal" font="default" size="100%">Terol, J.</style></author><author><style face="normal" font="default" size="100%">Trenor, M.</style></author><author><style face="normal" font="default" size="100%">Vaello, L.</style></author><author><style face="normal" font="default" size="100%">Vicente, O.</style></author><author><style face="normal" font="default" size="100%">Vidal, Ch</style></author><author><style face="normal" font="default" size="100%">Zacarias, L.</style></author><author><style face="normal" font="default" size="100%">Conejero, V.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of a citrus genome-wide EST collection and cDNA microarray as resources for genomic studies</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Mol Biol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Citrus/*genetics DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Complementary/chemistry/genetics *Expressed Sequence Tags Gene Expression Profiling Gene Library *Genome</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Genomics/*methods Molecular Sequence Data Oligonucleotide Array Sequence Analysis/*methods RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant/genetics/metabolism Reproducibility of Results Sequence Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=15830128</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">57</style></volume><pages><style face="normal" font="default" size="100%">375-91</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A functional genomics project has been initiated to approach the molecular characterization of the main biological and agronomical traits of citrus. As a key part of this project, a citrus EST collection has been generated from 25 cDNA libraries covering different tissues, developmental stages and stress conditions. The collection includes a total of 22,635 high-quality ESTs, grouped in 11,836 putative unigenes, which represent at least one third of the estimated number of genes in the citrus genome. Functional annotation of unigenes which have Arabidopsis orthologues (68% of all unigenes) revealed gene representation in every major functional category, suggesting that a genome-wide EST collection was obtained. A Citrus clementina Hort. ex Tan. cv. Clemenules genomic library, that will contribute to further characterization of relevant genes, has also been constructed. To initiate the analysis of citrus transcriptome, we have developed a cDNA microarray containing 12,672 probes corresponding to 6875 putative unigenes of the collection. Technical characterization of the microarray showed high intra- and inter-array reproducibility, as well as a good range of sensitivity. We have also validated gene expression data achieved with this microarray through an independent technique such as RNA gel blot analysis.</style></abstract><notes><style face="normal" font="default" size="100%">Forment, J Gadea, J Huerta, L Abizanda, L Agusti, J Alamar, S Alos, E Andres, F Arribas, R Beltran, J P Berbel, A Blazquez, M A Brumos, J Canas, L A Cercos, M Colmenero-Flores, J M Conesa, A Estables, B Gandia, M Garcia-Martinez, J L Gimeno, J Gisbert, A Gomez, G Gonzalez-Candelas, L Granell, A Guerri, J Lafuente, M T Madueno, F Marcos, J F Marques, M C Martinez, F Martinez-Godoy, M A Miralles, S Moreno, P Navarro, L Pallas, V Perez-Amador, M A Perez-Valle, J Pons, C Rodrigo, I Rodriguez, P L Royo, C Serrano, R Soler, G Tadeo, F Talon, M Terol, J Trenor, M Vaello, L Vicente, O Vidal, Ch Zacarias, L Conejero, V Comparative Study Research Support, U.S. Gov’t, Non-P.H.S. Netherlands Plant molecular biology Plant Mol Biol. 2005 Feb;57(3):375-91.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vaquerizas, J. M.</style></author><author><style face="normal" font="default" size="100%">L. Conde</style></author><author><style face="normal" font="default" size="100%">Yankilevich, P.</style></author><author><style face="normal" font="default" size="100%">Cabezon, A.</style></author><author><style face="normal" font="default" size="100%">Minguez, P.</style></author><author><style face="normal" font="default" size="100%">Diaz-Uriarte, R.</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Herrero, J.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">gepas</style></keyword><keyword><style  face="normal" font="default" size="100%">microarray data analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=15980548</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">W616-20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Gene Expression Profile Analysis Suite, GEPAS, has been running for more than three years. With &amp;gt;76,000 experiments analysed during the last year and a daily average of almost 300 analyses, GEPAS can be considered a well-established and widely used platform for gene expression microarray data analysis. GEPAS is oriented to the analysis of whole series of experiments. Its design and development have been driven by the demands of the biomedical community, probably the most active collective in the field of microarray users. Although clustering methods have obviously been implemented in GEPAS, our interest has focused more on methods for finding genes differentially expressed among distinct classes of experiments or correlated to diverse clinical outcomes, as well as on building predictors. There is also a great interest in CGH-arrays which fostered the development of the corresponding tool in GEPAS: InSilicoCGH. Much effort has been invested in GEPAS for developing and implementing efficient methods for functional annotation of experiments in the proper statistical framework. Thus, the popular FatiGO has expanded to a suite of programs for functional annotation of experiments, including information on transcription factor binding sites, chromosomal location and tissues. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Vaquerizas, Juan M Conde, Lucia Yankilevich, Patricio Cabezon, Amaya Minguez, Pablo Diaz-Uriarte, Ramon Al-Shahrour, Fatima Herrero, Javier Dopazo, Joaquin Research Support, Non-U.S. Gov’t England Nucleic acids research Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W616-20.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hoffmann, R.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Cigudosa, J. C.</style></author><author><style face="normal" font="default" size="100%">Valencia, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">HCAD, closing the gap between breakpoints and genes</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Chromosome Breakage Chromosome Disorders/diagnosis/*genetics *Databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Genes *Genetic Predisposition to Disease Humans PubMed Systems Integration</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=15608250</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">Database issue</style></number><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">D511-3</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Recurrent chromosome aberrations are an important resource when associating human pathologies to specific genes. However, for technical reasons a large number of chromosome breakpoints are defined only at the level of cytobands and many of the genes involved remain unidentified. We developed a web-based information system that mines the scientific literature and generates textual and comprehensive information on all human breakpoints. We show that the statistical analysis of this textual information and its combination with genomic data can identify genes directly involved in DNA rearrangements. The Human Chromosome Aberration Database (HCAD) is publicly accessible at http://www.pdg.cnb.uam.es/UniPub/HCAD/.</style></abstract><notes><style face="normal" font="default" size="100%">Hoffmann, Robert Dopazo, Joaquin Cigudosa, Juan C Valencia, Alfonso Research Support, Non-U.S. Gov’t England Nucleic acids research Nucleic Acids Res. 2005 Jan 1;33(Database issue):D511-3.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cascon, A.</style></author><author><style face="normal" font="default" size="100%">Ruiz-Llorente, S.</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Perales, S.</style></author><author><style face="normal" font="default" size="100%">Honrado, E.</style></author><author><style face="normal" font="default" size="100%">Martinez-Ramirez, A.</style></author><author><style face="normal" font="default" size="100%">Leton, R.</style></author><author><style face="normal" font="default" size="100%">Montero-Conde, C.</style></author><author><style face="normal" font="default" size="100%">Benitez, J.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Cigudosa, J. C.</style></author><author><style face="normal" font="default" size="100%">M. Robledo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A novel candidate region linked to development of both pheochromocytoma and head/neck paraganglioma</style></title><secondary-title><style face="normal" font="default" size="100%">Genes Chromosomes Cancer</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">80 and over Child Chromosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Adolescent Adrenal Gland Neoplasms/*genetics Adult Aged Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological/*genetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Pair 1/genetics Chromosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Pair 11/genetics Chromosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Pair 3/genetics Chromosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Pair 8/genetics Female Gene Deletion Head and Neck Neoplasms/*genetics Humans Male Middle Aged Nucleic Acid Hybridization Paraganglioma/*genetics Pheochromocytoma/*genetics Tumor Markers</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=15609347</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">260-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Although the histologic distinction between pheochromocytomas and head and neck paragangliomas is clear, little is known about the genetic differences between them. To date, various sets of genes have been found to be involved in inherited susceptibility to developing both tumor types, but the genes involved in sporadic pathogenesis are still unknown. To define new candidate regions, we performed CGH analysis on 29 pheochromocytomas and on 24 paragangliomas mainly of head and neck origin (20 of 24), which allowed us to differentiate between the two tumor types. Loss of 3q was significantly more frequent in pheochromocytomas, and loss of 1q appeared only in paragangliomas. We also found gain of 11q13 to be a significantly frequent alteration in malignant cases of both types. In addition, recurrent loss of 8p22-23 was found in 62% of pheochromocytomas (including all malignant cases) versus in 33% of paragangliomas, suggesting that this region contains candidate genes involved in the pathogenesis of this abnormality. Using FISH analysis on tissue microarrays, we confirmed genomic deletion of this region in 55% of pheochromocytomas compared to 12% of paragangliomas. Loss of 8p22-23 appears to be an important event in the sporadic development of these tumors, and additional molecular studies are necessary to identify candidate genes in this chromosomal region.</style></abstract><notes><style face="normal" font="default" size="100%">Cascon, Alberto Ruiz-Llorente, Sergio Rodriguez-Perales, Sandra Honrado, Emiliano Martinez-Ramirez, Angel Leton, Rocio Montero-Conde, Cristina Benitez, Javier Dopazo, Joaquin Cigudosa, Juan C Robledo, Mercedes Research Support, Non-U.S. Gov’t United States Genes, chromosomes &amp; cancer Genes Chromosomes Cancer. 2005 Mar;42(3):260-8.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Palacios, J.</style></author><author><style face="normal" font="default" size="100%">Honrado, E.</style></author><author><style face="normal" font="default" size="100%">Osorio, A.</style></author><author><style face="normal" font="default" size="100%">Cazorla, A.</style></author><author><style face="normal" font="default" size="100%">Sarrio, D.</style></author><author><style face="normal" font="default" size="100%">Barroso, A.</style></author><author><style face="normal" font="default" size="100%">Rodriguez, S.</style></author><author><style face="normal" font="default" size="100%">Cigudosa, J. C.</style></author><author><style face="normal" font="default" size="100%">Diez, O.</style></author><author><style face="normal" font="default" size="100%">Alonso, C.</style></author><author><style face="normal" font="default" size="100%">Lerma, E.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Rivas, C.</style></author><author><style face="normal" font="default" size="100%">Benitez, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Phenotypic characterization of BRCA1 and BRCA2 tumors based in a tissue microarray study with 37 immunohistochemical markers</style></title><secondary-title><style face="normal" font="default" size="100%">Breast Cancer Res Treat</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult Apoptosis Breast Neoplasms/*genetics/*pathology Cell Cycle Proteins Cluster Analysis Female *Genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological/genetics/metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">BRCA1 *Genes</style></keyword><keyword><style  face="normal" font="default" size="100%">BRCA2 Humans Immunohistochemistry In Situ Hybridization</style></keyword><keyword><style  face="normal" font="default" size="100%">Fluorescence Phenotype Spain *Tissue Array Analysis *Tumor Markers</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=15770521</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">90</style></volume><pages><style face="normal" font="default" size="100%">5-14</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Familial breast cancers that are associated with BRCA1 or BRCA2 germline mutations differ in both their morphological and immunohistochemical characteristics. To further characterize the molecular difference between genotypes, the authors evaluated the expression of 37 immunohistochemical markers in a tissue microarray (TMA) containing cores from 20 BRCA1, 14 BRCA2, and 59 sporadic age-matched breast carcinomas. Markers analyzed included, amog others, common markers in breast cancer, such as hormone receptors, p53 and HER2, along with 15 molecules involved in cell cycle regulation, such as cyclins, cyclin dependent kinases (CDK) and CDK inhibitors (CDKI), apoptosis markers, such as BCL2 and active caspase 3, and two basal/myoepithelial markers (CK 5/6 and P-cadherin). In addition, we analyzed the amplification of CCND1, CCNE, HER2 and MYC by FISH.Unsupervised cluster data analysis of both hereditary and sporadic cases using the complete set of immunohistochemical markers demonstrated that most BRCA1-associated carcinomas grouped in a branch of ER-, HER2-negative tumors that expressed basal cell markers and/or p53 and had higher expression of activated caspase 3. The cell cycle proteins associated with these tumors were E2F6, cyclins A, B1 and E, SKP2 and Topo IIalpha. In contrast, most BRCA2-associated carcinomas grouped in a branch composed by ER/PR/BCL2-positive tumors with a higher expression of the cell cycle proteins cyclin D1, cyclin D3, p27, p16, p21, CDK4, CDK2 and CDK1. In conclusion, our study in hereditary breast cancer tumors analyzing 37 immunohistochemical markers, define the molecular differences between BRCA1 and BRCA2 tumors with respect to hormonal receptors, cell cycle, apoptosis and basal cell markers.</style></abstract><notes><style face="normal" font="default" size="100%">Palacios, Jose Honrado, Emiliano Osorio, Ana Cazorla, Alicia Sarrio, David Barroso, Alicia Rodriguez, Sandra Cigudosa, Juan C Diez, Orland Alonso, Carmen Lerma, Enrique Dopazo, Joaquin Rivas, Carmen Benitez, Javier Research Support, Non-U.S. Gov’t Netherlands Breast cancer research and treatment Breast Cancer Res Treat. 2005 Mar;90(1):5-14.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alvarez, S.</style></author><author><style face="normal" font="default" size="100%">Diaz-Uriarte, R.</style></author><author><style face="normal" font="default" size="100%">Osorio, A.</style></author><author><style face="normal" font="default" size="100%">Barroso, A.</style></author><author><style face="normal" font="default" size="100%">Melchor, L.</style></author><author><style face="normal" font="default" size="100%">Paz, M. F.</style></author><author><style face="normal" font="default" size="100%">Honrado, E.</style></author><author><style face="normal" font="default" size="100%">Rodriguez, R.</style></author><author><style face="normal" font="default" size="100%">Urioste, M.</style></author><author><style face="normal" font="default" size="100%">Valle, L.</style></author><author><style face="normal" font="default" size="100%">Diez, O.</style></author><author><style face="normal" font="default" size="100%">Cigudosa, J. C.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Esteller, M.</style></author><author><style face="normal" font="default" size="100%">Benitez, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A predictor based on the somatic genomic changes of the BRCA1/BRCA2 breast cancer tumors identifies the non-BRCA1/BRCA2 tumors with BRCA1 promoter hypermethylation</style></title><secondary-title><style face="normal" font="default" size="100%">Clin Cancer Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">BRCA1 Protein/*genetics BRCA2 Protein/*genetics Breast Neoplasms/*genetics/pathology Chromosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic/*genetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Human Humans Male Mutation Nucleic Acid Hybridization/methods Promoter Regions</style></keyword><keyword><style  face="normal" font="default" size="100%">Pair 12/genetics Chromosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Pair 15/genetics Chromosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Pair 18/genetics Chromosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Pair 2/genetics Chromosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Pair 8/genetics *DNA Methylation Female Genome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=15709182</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">1146-53</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The genetic changes underlying in the development and progression of familial breast cancer are poorly understood. To identify a somatic genetic signature of tumor progression for each familial group, BRCA1, BRCA2, and non-BRCA1/BRCA2 (BRCAX) tumors, by high-resolution comparative genomic hybridization, we have analyzed 77 tumors previously characterized for BRCA1 and BRCA2 germ line mutations. Based on a combination of the somatic genetic changes observed at the six most different chromosomal regions and the status of the estrogen receptor, we developed using random forests a molecular classifier, which assigns to a given tumor a probability to belong either to the BRCA1 or to the BRCA2 class. Because 76.5% (26 of 34) of the BRCAX cases were classified with our predictor to the BRCA1 class with a probability of &gt;50%, we analyzed the BRCA1 promoter region for aberrant methylation in all the BRCAX cases. We found that 15 of the 34 BRCAX analyzed tumors had hypermethylation of the BRCA1 gene. When we considered the predictor, we observed that all the cases with this epigenetic event were assigned to the BRCA1 class with a probability of &gt;50%. Interestingly, 84.6% of the cases (11 of 13) assigned to the BRCA1 class with a probability &gt;80% had an aberrant methylation of the BRCA1 promoter. This fact suggests that somatic BRCA1 inactivation could modify the profile of tumor progression in most of the BRCAX cases.</style></abstract><notes><style face="normal" font="default" size="100%">Alvarez, Sara Diaz-Uriarte, Ramon Osorio, Ana Barroso, Alicia Melchor, Lorenzo Paz, Maria Fe Honrado, Emiliano Rodriguez, Raquel Urioste, Miguel Valle, Laura Diez, Orland Cigudosa, Juan Cruz Dopazo, Joaquin Esteller, Manel Benitez, Javier Comparative Study Research Support, Non-U.S. Gov’t United States Clinical cancer research : an official journal of the American Association for Cancer Research Clin Cancer Res. 2005 Feb 1;11(3):1146-53.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">L. Conde</style></author><author><style face="normal" font="default" size="100%">Vaquerizas, J. M.</style></author><author><style face="normal" font="default" size="100%">Ferrer-Costa, C.</style></author><author><style face="normal" font="default" size="100%">de la Cruz, X.</style></author><author><style face="normal" font="default" size="100%">Orozco, M.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PupasView: a visual tool for selecting suitable SNPs, with putative pathological effect in genes, for genotyping purposes</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer Graphics Genes *Genetic Predisposition to Disease Genotype Internet Phenotype *Polymorphism</style></keyword><keyword><style  face="normal" font="default" size="100%">Single Nucleotide *Software User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=15980522</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">Web Server issue</style></number><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">W501-5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We have developed a web tool, PupasView, for the selection of single nucleotide polymorphisms (SNPs) with potential phenotypic effect. PupasView constitutes an interactive environment in which functional information and population frequency data can be used as sequential filters over linkage disequilibrium parameters to obtain a final list of SNPs optimal for genotyping purposes. PupasView is the first resource that integrates phenotypic effects caused by SNPs at both the translational and the transcriptional level. PupasView retrieves SNPs that could affect conserved regions that the cellular machinery uses for the correct processing of genes (intron/exon boundaries or exonic splicing enhancers), predicted transcription factor binding sites and changes in amino acids in the proteins for which a putative pathological effect is calculated. The program uses the mapping of SNPs in the genome provided by Ensembl. PupasView will be of much help in studies of multifactorial disorders, where the use of functional SNPs will increase the sensitivity of the identification of the genes responsible for the disease. The PupasView web interface is accessible through http://pupasview.ochoa.fib.es and through http://www.pupasnp.org.</style></abstract><notes><style face="normal" font="default" size="100%">Conde, Lucia Vaquerizas, Juan M Ferrer-Costa, Carles de la Cruz, Xavier Orozco, Modesto Dopazo, Joaquin Research Support, Non-U.S. Gov’t England Nucleic acids research Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W501-5.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martinez-Delgado, Beatriz</style></author><author><style face="normal" font="default" size="100%">Meléndez, Barbara</style></author><author><style face="normal" font="default" size="100%">Cuadros, Marta</style></author><author><style face="normal" font="default" size="100%">Alvarez, Javier</style></author><author><style face="normal" font="default" size="100%">Castrillo, Jose Maria</style></author><author><style face="normal" font="default" size="100%">Ruiz De La Parte, Ana</style></author><author><style face="normal" font="default" size="100%">Mollejo, Manuela</style></author><author><style face="normal" font="default" size="100%">Bellas, Carmen</style></author><author><style face="normal" font="default" size="100%">Diaz, Ramon</style></author><author><style face="normal" font="default" size="100%">Lombardía, Luis</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Domínguez, Orlando</style></author><author><style face="normal" font="default" size="100%">Cascon, Alberto</style></author><author><style face="normal" font="default" size="100%">Robledo, Mercedes</style></author><author><style face="normal" font="default" size="100%">Rivas, Carmen</style></author><author><style face="normal" font="default" size="100%">Benitez, Javier</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expression profiling of T-cell lymphomas differentiates peripheral and lymphoblastic lymphomas and defines survival related genes.</style></title><secondary-title><style face="normal" font="default" size="100%">Clinical cancer research : an official journal of the American Association for Cancer Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2004 Aug 1</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://clincancerres.aacrjournals.org/content/10/15/4971.long</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">4971-82</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;PURPOSE: T-Cell lymphomas constitute heterogeneous and aggressive tumors in which pathogenic alterations remain largely unknown. Expression profiling has demonstrated to be a useful tool for molecular classification of tumors. EXPERIMENTAL DESIGN: Using DNA microarrays (CNIO-OncoChip) containing 6386 cancer-related genes, we established the expression profiling of T-cell lymphomas and compared them to normal lymphocytes and lymph nodes. RESULTS: We found significant differences between the peripheral and lymphoblastic T-cell lymphomas, which include a deregulation of nuclear factor-kappaB signaling pathway. We also identify differentially expressed genes between peripheral T-cell lymphoma tumors and normal T lymphocytes or reactive lymph nodes, which could represent candidate tumor markers of these lymphomas. Additionally, a close relationship between genes associated to survival and those that differentiate among the stages of disease and responses to therapy was found. CONCLUSIONS: Our results reflect the value of gene expression profiling to gain insight about the molecular alterations involved in the pathogenesis of T-cell lymphomas.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Melendez, B.</style></author><author><style face="normal" font="default" size="100%">Diaz-Uriarte, R.</style></author><author><style face="normal" font="default" size="100%">Cuadros, M.</style></author><author><style face="normal" font="default" size="100%">Martinez-Ramirez, A.</style></author><author><style face="normal" font="default" size="100%">Fernandez-Piqueras, J.</style></author><author><style face="normal" font="default" size="100%">Dopazo, A.</style></author><author><style face="normal" font="default" size="100%">Cigudosa, J. C.</style></author><author><style face="normal" font="default" size="100%">Rivas, C.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Martinez-Delgado, B.</style></author><author><style face="normal" font="default" size="100%">Benitez, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gene expression analysis of chromosomal regions with gain or loss of genetic material detected by comparative genomic hybridization</style></title><secondary-title><style face="normal" font="default" size="100%">Genes Chromosomes Cancer</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Chromosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Fluorescence Lymphoma</style></keyword><keyword><style  face="normal" font="default" size="100%">Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Pair 13/*genetics Chromosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Pair 19/*genetics Chromosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Pair 6/*genetics Expressed Sequence Tags *Gene Dosage Gene Expression Profiling Humans In Situ Hybridization</style></keyword><keyword><style  face="normal" font="default" size="100%">T-Cell/*genetics Nucleic Acid Hybridization Oligonucleotide Array Sequence Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=15382261</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">41</style></volume><pages><style face="normal" font="default" size="100%">353-65</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Comparative genomic hybridization (CGH) has been widely used to detect copy number alterations in cancer and to identify regions containing candidate tumor-responsible genes; however, gene expression changes have been described only in highly amplified regions (amplicons). To study the overall impact of slight copy number changes on gene expression, we analyzed 16 T-cell lymphomas by using CGH and a custom-designed cDNA microarray containing 7,657 genes and expressed sequence tags related to tumorigenesis. We evaluated mean gene expression and variability within CGH-altered regions and explored the relationship between the effects of the gene and its position within these regions. Minimally overlapping CGH candidate areas (6q25, 13q21-q22, and 19q13.1) revealed a weak relationship between altered genomic content and gene expression. However, some candidate genes showed modified expression within these regions in the majority of tumors; these candidate genes were evaluated and confirmed in another independent series of 23 T-cell lymphomas by use of the same cDNA microarray and by FISH on a tissue microarray. When all the CGH regions detected for each tumor were considered, we found a significant increase or decrease in the mean expression of the genes contained in gained or lost regions, respectively. In addition, we found that the expression of a gene was dependent not only on its position within an altered region but also on its own mechanism of regulation: genes in the same altered region responded very differently to the gain or loss of genetic material. Supplementary material for this article can be found on the Genes, Chromosomes, and Cancer website at http://www.interscience.wiley.com/jpages/1045-2257/suppmat/index.html.</style></abstract><notes><style face="normal" font="default" size="100%">Melendez, Barbara Diaz-Uriarte, Ramon Cuadros, Marta Martinez-Ramirez, Angel Fernandez-Piqueras, Jose Dopazo, Ana Cigudosa, Juan-Cruz Rivas, Carmen Dopazo, Joaquin Martinez-Delgado, Beatriz Benitez, Javier Research Support, Non-U.S. Gov’t United States Genes, chromosomes &amp; cancer Genes Chromosomes Cancer. 2004 Dec;41(4):353-65.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Herrero, J.</style></author><author><style face="normal" font="default" size="100%">Vaquerizas, J. M.</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">L. Conde</style></author><author><style face="normal" font="default" size="100%">A. Mateos</style></author><author><style face="normal" font="default" size="100%">Diaz-Uriarte, J. S.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">New challenges in gene expression data analysis and the extended GEPAS</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">gepas</style></keyword><keyword><style  face="normal" font="default" size="100%">microarray data analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=15215434</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">W485-91</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Since the first papers published in the late nineties, including, for the first time, a comprehensive analysis of microarray data, the number of questions that have been addressed through this technique have both increased and diversified. Initially, interest focussed on genes coexpressing across sets of experimental conditions, implying, essentially, the use of clustering techniques. Recently, however, interest has focussed more on finding genes differentially expressed among distinct classes of experiments, or correlated to diverse clinical outcomes, as well as in building predictors. In addition to this, the availability of accurate genomic data and the recent implementation of CGH arrays has made mapping expression and genomic data on the chromosomes possible. There is also a clear demand for methods that allow the automatic transfer of biological information to the results of microarray experiments. Different initiatives, such as the Gene Ontology (GO) consortium, pathways databases, protein functional motifs, etc., provide curated annotations for genes. Whereas many resources on the web focus mainly on clustering methods, GEPAS has evolved to cope with the aforementioned new challenges that have recently arisen in the field of microarray data analysis. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://gepas.bioinfo.cnio.es.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Herrero, Javier Vaquerizas, Juan M Al-Shahrour, Fatima Conde, Lucia Mateos, Alvaro Diaz-Uriarte, Javier Santoyo Ramon Dopazo, Joaquin England Nucleic acids research Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W485-91.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">L. Conde</style></author><author><style face="normal" font="default" size="100%">Vaquerizas, J. M.</style></author><author><style face="normal" font="default" size="100%">J. Santoyo</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Ruiz-Llorente, S.</style></author><author><style face="normal" font="default" size="100%">M. Robledo</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PupaSNP Finder: a web tool for finding SNPs with putative effect at transcriptional level</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acid Substitution Binding Sites Humans Internet Phenotype *Polymorphism</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Single Nucleotide RNA Splicing *Software Transcription Factors/metabolism *Transcription</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=15215388</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">Web Server issue</style></number><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">W242-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We have developed a web tool, PupaSNP Finder (PupaSNP for short), for high-throughput searching for single nucleotide polymorphisms (SNPs) with potential phenotypic effect. PupaSNP takes as its input lists of genes (or generates them from chromosomal coordinates) and retrieves SNPs that could affect the conserved regions that the cellular machinery uses for the correct processing of genes (intron/exon boundaries or exonic splicing enhancers), predicted transcription factor binding sites (TFBS) and changes in amino acids in the proteins. The program uses the mapping of SNPs in the genome provided by Ensembl. Additionally, user-defined SNPs (not yet mapped in the genome) can be easily provided to the program. Also, additional functional information from Gene Ontology, OMIM and homologies in other model organisms is provided. In contrast to other programs already available, which focus only on SNPs with possible effect in the protein, PupaSNP includes SNPs with possible transcriptional effect. PupaSNP will be of significant help in studies of multifactorial disorders, where the use of functional SNPs will increase the sensitivity of identification of the genes responsible for the disease. The PupaSNP web interface is accessible through http://pupasnp.bioinfo.cnio.es.</style></abstract><notes><style face="normal" font="default" size="100%">Conde, Lucia Vaquerizas, Juan M Santoyo, Javier Al-Shahrour, Fatima Ruiz-Llorente, Sergio Robledo, Mercedes Dopazo, Joaquin England Nucleic acids research Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W242-8.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yi, X.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Punt, P. J.</style></author><author><style face="normal" font="default" size="100%">Hager, L. P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Examining the role of glutamic acid 183 in chloroperoxidase catalysis</style></title><secondary-title><style face="normal" font="default" size="100%">J Biol Chem</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aspergillus niger/metabolism Catalase/metabolism Catalysis Chloride Peroxidase/*chemistry/*metabolism Chlorine/metabolism Chromatography</style></keyword><keyword><style  face="normal" font="default" size="100%">Ion Exchange Circular Dichroism Crystallography</style></keyword><keyword><style  face="normal" font="default" size="100%">Polyacrylamide Gel Fungi/enzymology Glutamic Acid/*chemistry Histidine/chemistry/metabolism Hydrogen-Ion Concentration Immunoblotting Isoelectric Focusing Mutation Oxidoreductases/metabolism Plasmids/metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">X-Ray Electrophoresis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=12576477</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">16</style></number><volume><style face="normal" font="default" size="100%">278</style></volume><pages><style face="normal" font="default" size="100%">13855-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Site-directed mutagenesis has been used to investigate the role of glutamic acid 183 in chloroperoxidase catalysis. Based on the x-ray crystallographic structure of chloroperoxidase, Glu-183 is postulated to function on distal side of the heme prosthetic group as an acid-base catalyst in facilitating the reaction between the peroxidase and hydrogen peroxide with the formation of Compound I. In contrast, the other members of the heme peroxidase family use a histidine residue in this role. Plasmids have now been constructed in which the codon for Glu-183 is replaced with a histidine codon. The mutant recombinant gene has been expressed in Aspergillus niger. An analysis of the produced mutant gene shows that the substitution of Glu-183 with a His residue is detrimental to the chlorination and dismutation activity of chloroperoxidase. The activity is reduced by 85 and 50% of wild type activity, respectively. However, quite unexpectedly, the epoxidation activity of the mutant enzyme is significantly enhanced approximately 2.5-fold. These results show that Glu-183 is important but not essential for the chlorination activity of chloroperoxidase. It is possible that the increased epoxidation of the mutant enzyme is based on an increase in the hydrophobicity of the active site.</style></abstract><notes><style face="normal" font="default" size="100%">Yi, Xianwen Conesa, Ana Punt, Peter J Hager, Lowell P GM 07768/GM/NIGMS NIH HHS/United States Research Support, U.S. Gov’t, P.H.S. United States The Journal of biological chemistry J Biol Chem. 2003 Apr 18;278(16):13855-9. Epub 2003 Feb 7.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Tamames</style></author><author><style face="normal" font="default" size="100%">Clark, D.</style></author><author><style face="normal" font="default" size="100%">Herrero, J.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Blaschke, C.</style></author><author><style face="normal" font="default" size="100%">Fernandez, J. M.</style></author><author><style face="normal" font="default" size="100%">Oliveros, J. C.</style></author><author><style face="normal" font="default" size="100%">Valencia, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bioinformatics methods for the analysis of expression arrays: data clustering and information extraction</style></title><secondary-title><style face="normal" font="default" size="100%">J Biotechnol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Abstracting and Indexing as Topic/methods *Cluster Analysis *Database Management Systems Databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer-Assisted/methods Information Storage and Retrieval/*methods Internet Medline National Library of Medicine (U.S.) Oligonucleotide Array Sequence Analysis/*methods United States</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Gene Expression Gene Expression Profiling/*methods Image Processing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=12141992</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2-3</style></number><volume><style face="normal" font="default" size="100%">98</style></volume><pages><style face="normal" font="default" size="100%">269-83</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Expression arrays facilitate the monitoring of changes in the expression patterns of large collections of genes. The analysis of expression array data has become a computationally-intensive task that requires the development of bioinformatics technology for a number of key stages in the process, such as image analysis, database storage, gene clustering and information extraction. Here, we review the current trends in each of these areas, with particular emphasis on the development of the related technology being carried out within our groups.</style></abstract><notes><style face="normal" font="default" size="100%">Tamames, Javier Clark, Dominic Herrero, Javier Dopazo, Joaquin Blaschke, Christian Fernandez, Jose M Oliveros, Juan C Valencia, Alfonso Review Netherlands Journal of biotechnology J Biotechnol. 2002 Sep 25;98(2-3):269-83.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Jeenes, D.</style></author><author><style face="normal" font="default" size="100%">Archer, D. B.</style></author><author><style face="normal" font="default" size="100%">van den Hondel, C. A.</style></author><author><style face="normal" font="default" size="100%">Punt, P. J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Calnexin overexpression increases manganese peroxidase production in Aspergillus niger</style></title><secondary-title><style face="normal" font="default" size="100%">Appl Environ Microbiol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aspergillus niger/*enzymology/genetics Calcium-Binding Proteins/*metabolism Calnexin Culture Media *Fungal Proteins HSP70 Heat-Shock Proteins/metabolism Heme/metabolism Peroxidases/*biosynthesis/genetics Phanerochaete/enzymology/genetics Transformation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=11823227</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">68</style></volume><pages><style face="normal" font="default" size="100%">846-51</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Heme-containing peroxidases from white rot basidiomycetes, in contrast to most proteins of fungal origin, are poorly produced in industrial filamentous fungal strains. Factors limiting peroxidase production are believed to operate at the posttranslational level. In particular, insufficient availability of the prosthetic group which is required for peroxidase biosynthesis has been proposed to be an important bottleneck. In this work, we analyzed the role of two components of the secretion pathway, the chaperones calnexin and binding protein (BiP), in the production of a fungal peroxidase. Expression of the Phanerochaete chrysosporium manganese peroxidase (MnP) in Aspergillus niger resulted in an increase in the expression level of the clxA and bipA genes. In a heme-supplemented medium, where MnP was shown to be overproduced to higher levels, induction of clxA and bipA was also higher. Overexpression of these two chaperones in an MnP-producing strain was analyzed for its effect on MnP production. Whereas bipA overexpression seriously reduced MnP production, overexpression of calnexin resulted in a four- to fivefold increase in the extracellular MnP levels. However, when additional heme was provided in the culture medium, calnexin overexpression had no synergistic effect on MnP production. The possible function of these two chaperones in MnP maturation and production is discussed.</style></abstract><notes><style face="normal" font="default" size="100%">Conesa, Ana Jeenes, David Archer, David B van den Hondel, Cees A M J J Punt, Peter J United States Applied and environmental microbiology Appl Environ Microbiol. 2002 Feb;68(2):846-51.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Punt, P. J.</style></author><author><style face="normal" font="default" size="100%">van Biezen, N.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Albers, A.</style></author><author><style face="normal" font="default" size="100%">Mangnus, J.</style></author><author><style face="normal" font="default" size="100%">van den Hondel, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Filamentous fungi as cell factories for heterologous protein production</style></title><secondary-title><style face="normal" font="default" size="100%">Trends Biotechnol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Fermentation/genetics/physiology Fungi/*genetics/*metabolism Humans Interleukin-6/analysis/*biosynthesis/genetics Peroxidases/analysis/*biosynthesis/genetics Protein Conformation Recombinant Proteins/analysis/*biosynthesis/genetics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=11943375</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">200-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Filamentous fungi have been used as sources of metabolites and enzymes for centuries. For about two decades, molecular genetic tools have enabled us to use these organisms to express extra copies of both endogenous and exogenous genes. This review of current practice reveals that molecular tools have enabled several new developments. But it has been process development that has driven the final breakthrough to achieving commercially relevant quantities of protein. Recent research into gene expression in filamentous fungi has explored their wealth of genetic diversity with a view to exploiting them as expression hosts and as a source of new genes. Inevitably, the progress in the ’genomics’ technology will further develop high-throughput technologies for these organisms.</style></abstract><notes><style face="normal" font="default" size="100%">Punt, Peter J van Biezen, Nick Conesa, Ana Albers, Alwin Mangnus, Jeroen van den Hondel, Cees Review England Trends in biotechnology Trends Biotechnol. 2002 May;20(5):200-6.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Punt, P. J.</style></author><author><style face="normal" font="default" size="100%">van den Hondel, C. A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fungal peroxidases: molecular aspects and applications</style></title><secondary-title><style face="normal" font="default" size="100%">J Biotechnol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acid Sequence Binding Sites Biotechnology Catalysis Fungi/*enzymology Molecular Sequence Data Peroxidases/chemistry/*genetics/metabolism Recombinant Proteins Sequence Homology Substrate Specificity</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=11738721</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">93</style></volume><pages><style face="normal" font="default" size="100%">143-58</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Peroxidases are oxidoreductases that utilize hydrogen peroxide to catalyze oxidative reactions. A large number of peroxidases have been identified in fungal species and are being characterized at the molecular level. In this manuscript we review the current knowledge on the molecular aspects of this type of enzymes. We present an overview of the research efforts undertaken in deciphering the structural basis of the catalytic properties of fungal peroxidases and discuss molecular genetics and protein homology aspects of this enzyme class. Finally, we summarize the potential biotechnological applications of these enzymes and evaluate recent advances on their expression in heterologous systems for production purposes.</style></abstract><notes><style face="normal" font="default" size="100%">Conesa, Ana Punt, Peter J van den Hondel, Cees A M J J Review Netherlands Journal of biotechnology J Biotechnol. 2002 Feb 14;93(2):143-58.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Conde, L.</style></author><author><style face="normal" font="default" size="100%">Mateos, A.</style></author><author><style face="normal" font="default" size="100%">Herrero, J.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data</style></title><secondary-title><style face="normal" font="default" size="100%">Neural Networks for Signal Processing XII. 2002 IEEE Signal Processing Society WorkshopProceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/document/1030019/http://xplorestaging.ieee.org/ielx5/8007/22134/01030019.pdf?arnumber=1030019</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Martigny, Switzerland</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Iverson, G. M.</style></author><author><style face="normal" font="default" size="100%">Reddel, S.</style></author><author><style face="normal" font="default" size="100%">Victoria, E. J.</style></author><author><style face="normal" font="default" size="100%">Cockerill, K. A.</style></author><author><style face="normal" font="default" size="100%">Wang, Y. X.</style></author><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author><author><style face="normal" font="default" size="100%">Sali, A.</style></author><author><style face="normal" font="default" size="100%">Marquis, D. M.</style></author><author><style face="normal" font="default" size="100%">Krilis, S. A.</style></author><author><style face="normal" font="default" size="100%">Linnik, M. D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Use of single point mutations in domain I of beta 2-glycoprotein I to determine fine antigenic specificity of antiphospholipid autoantibodies</style></title><secondary-title><style face="normal" font="default" size="100%">J Immunol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acid Substitution/genetics Antibodies</style></keyword><keyword><style  face="normal" font="default" size="100%">Antibody/genetics Binding</style></keyword><keyword><style  face="normal" font="default" size="100%">Antiphospholipid/blood/*metabolism Antibodies</style></keyword><keyword><style  face="normal" font="default" size="100%">Competitive/genetics/immunology Enzyme-Linked Immunosorbent Assay/methods Epitopes/analysis/*immunology/metabolism Glycine/genetics Glycoproteins/biosynthesis/*genetics/*immunology/isolation &amp; purification/metabolism Humans Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Peptide Fragments/genetics/immunology/isolation &amp; purification/metabolism *Point Mutation Protein Structure</style></keyword><keyword><style  face="normal" font="default" size="100%">Monoclonal/blood/metabolism Antiphospholipid Syndrome/immunology Arginine/genetics *Binding Sites</style></keyword><keyword><style  face="normal" font="default" size="100%">Tertiary/genetics Recombinant Proteins/biosynthesis/immunology/isolation &amp; purification/metabolism Static Electricity beta 2-Glycoprotein I</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=12471146</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">12</style></number><volume><style face="normal" font="default" size="100%">169</style></volume><pages><style face="normal" font="default" size="100%">7097-103</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Autoantibodies against beta(2)-glycoprotein I (beta(2)GPI) appear to be a critical feature of the antiphospholipid syndrome (APS). As determined using domain deletion mutants, human autoantibodies bind to the first of five domains present in beta(2)GPI. In this study the fine detail of the domain I epitope has been examined using 10 selected mutants of whole beta(2)GPI containing single point mutations in the first domain. The binding to beta(2)GPI was significantly affected by a number of single point mutations in domain I, particularly by mutations in the region of aa 40-43. Molecular modeling predicted these mutations to affect the surface shape and electrostatic charge of a facet of domain I. Mutation K19E also had an effect, albeit one less severe and involving fewer patients. Similar results were obtained in two different laboratories using affinity-purified anti-beta(2)GPI in a competitive inhibition ELISA and with whole serum in a direct binding ELISA. This study confirms that anti-beta(2)GPI autoantibodies bind to domain I, and that the charged surface patch defined by residues 40-43 contributes to a dominant target epitope.</style></abstract><notes><style face="normal" font="default" size="100%">Iverson, G Michael Reddel, Stephen Victoria, Edward J Cockerill, Keith A Wang, Ying-Xia Marti-Renom, Marc A Sali, Andrej Marquis, David M Krilis, Steven A Linnik, Matthew D GM54762/GM/NIGMS NIH HHS/United States Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, P.H.S. United States Journal of immunology (Baltimore, Md. : 1950) J Immunol. 2002 Dec 15;169(12):7097-103.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Mendoza, A.</style></author><author><style face="normal" font="default" size="100%">Herrero, J.</style></author><author><style face="normal" font="default" size="100%">Caldara, F.</style></author><author><style face="normal" font="default" size="100%">Humbert, Y.</style></author><author><style face="normal" font="default" size="100%">Friedli, L.</style></author><author><style face="normal" font="default" size="100%">Guerrier, M.</style></author><author><style face="normal" font="default" size="100%">Grand-Schenk, E.</style></author><author><style face="normal" font="default" size="100%">Gandin, C.</style></author><author><style face="normal" font="default" size="100%">de Francesco, M.</style></author><author><style face="normal" font="default" size="100%">Polissi, A.</style></author><author><style face="normal" font="default" size="100%">Buell, G.</style></author><author><style face="normal" font="default" size="100%">Feger, G.</style></author><author><style face="normal" font="default" size="100%">Garcia, E.</style></author><author><style face="normal" font="default" size="100%">Peitsch, M.</style></author><author><style face="normal" font="default" size="100%">Garcia-Bustos, J. F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Annotated draft genomic sequence from a Streptococcus pneumoniae type 19F clinical isolate</style></title><secondary-title><style face="normal" font="default" size="100%">Microb Drug Resist</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bacterial Molecular Sequence Data Pneumococcal Infections/*microbiology Prokaryotic Cells RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Bacterial/chemistry/genetics Genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Bacterial/genetics *Genome</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Transfer/metabolism Streptococcus pneumoniae/*genetics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=11442348</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">99-125</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The public availability of numerous microbial genomes is enabling the analysis of bacterial biology in great detail and with an unprecedented, organism-wide and taxon-wide, broad scope. Streptococcus pneumoniae is one of the most important bacterial pathogens throughout the world. We present here sequences and functional annotations for 2.1-Mbp of pneumococcal DNA, covering more than 90% of the total estimated size of the genome. The sequenced strain is a clinical isolate resistant to macrolides and tetracycline. It carries a type 19F capsular locus, but multilocus sequence typing for several conserved genetic loci suggests that the strain sequenced belongs to a pneumococcal lineage that most often expresses a serotype 15 capsular polysaccharide. A total of 2,046 putative open reading frames (ORFs) longer than 100 amino acids were identified (average of 1,009 bp per ORF), including all described two-component systems and aminoacyl tRNA synthetases. Comparisons to other complete, or nearly complete, bacterial genomes were made and are presented in a graphical form for all the predicted proteins.</style></abstract><notes><style face="normal" font="default" size="100%">Dopazo, J Mendoza, A Herrero, J Caldara, F Humbert, Y Friedli, L Guerrier, M Grand-Schenk, E Gandin, C de Francesco, M Polissi, A Buell, G Feger, G Garcia, E Peitsch, M Garcia-Bustos, J F United States Microbial drug resistance (Larchmont, N.Y.) Microb Drug Resist. 2001 Summer;7(2):99-125.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Weelink, G.</style></author><author><style face="normal" font="default" size="100%">van den Hondel, C. A.</style></author><author><style face="normal" font="default" size="100%">Punt, P. J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">C-terminal propeptide of the Caldariomyces fumago chloroperoxidase: an intramolecular chaperone?</style></title><secondary-title><style face="normal" font="default" size="100%">FEBS Lett</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acid Sequence Ascomycota/*enzymology/genetics Aspergillus niger/genetics Base Sequence Chloride Peroxidase/biosynthesis/*chemistry/genetics DNA Primers/genetics Enzyme Precursors/biosynthesis/chemistry/genetics Gene Expression Molecular Chaperones/b</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=11513866</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2-3</style></number><volume><style face="normal" font="default" size="100%">503</style></volume><pages><style face="normal" font="default" size="100%">117-20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Caldariomyces fumago chloroperoxidase (CPO) is synthesised as a 372-aa precursor which undergoes two proteolytic processing events: removal of a 21-aa N-terminal signal peptide and of a 52-aa C-terminal propeptide. The Aspergillus niger expression system developed for CPO was used to get insight into the function of this C-terminal propeptide. A. niger transformants expressing a CPO protein from which the C-terminal propeptide was deleted failed in producing any extracellular CPO activity, although the CPO polypeptide was synthesised. Expression of the full-length gene in an A. niger strain lacking the KEX2-like protease PclA also resulted in the production of CPO cross-reactive material into the culture medium, but no CPO activity. Based on these results, a function of the C-terminal propeptide in CPO maturation is indicated.</style></abstract><notes><style face="normal" font="default" size="100%">Conesa, A Weelink, G van den Hondel, C A Punt, P J Netherlands FEBS letters FEBS Lett. 2001 Aug 17;503(2-3):117-20.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">van De Velde, F.</style></author><author><style face="normal" font="default" size="100%">van Rantwijk, F.</style></author><author><style face="normal" font="default" size="100%">Sheldon, R. A.</style></author><author><style face="normal" font="default" size="100%">van den Hondel, C. A.</style></author><author><style face="normal" font="default" size="100%">Punt, P. J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expression of the Caldariomyces fumago chloroperoxidase in Aspergillus niger and characterization of the recombinant enzyme</style></title><secondary-title><style face="normal" font="default" size="100%">J Biol Chem</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aspergillus niger/enzymology/genetics Catalysis Chloride Peroxidase/biosynthesis/*genetics Fungal Proteins/biosynthesis/*genetics Recombinant Proteins/biosynthesis/genetics Substrate Specificity</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=11278701</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">21</style></number><volume><style face="normal" font="default" size="100%">276</style></volume><pages><style face="normal" font="default" size="100%">17635-40</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Caldariomyces fumago chloroperoxidase was successfully expressed in Aspergillus niger. The recombinant enzyme was produced in the culture medium as an active protein and could be purified by a three-step purification procedure. The catalytic behavior of recombinant chloroperoxidase (rCPO) was studied and compared with that of native CPO. The specific chlorination activity (47 units/nmol) of rCPO and its pH optimum (pH 2.75) were very similar to those of native CPO. rCPO catalyzes the oxidation of various substrates in comparable yields and selectivities to native CPO. Indole was oxidized to 2-oxindole with 99% selectivity and thioanisole to the corresponding R-sulfoxide (enantiomeric excess &gt;98%). Incorporation of (18)O from labeled H(2)18O(2) into the oxidized products was 100% in both cases.</style></abstract><notes><style face="normal" font="default" size="100%">Conesa, A van De Velde, F van Rantwijk, F Sheldon, R A van Den Hondel, C A Punt, P J Research Support, Non-U.S. Gov’t United States The Journal of biological chemistry J Biol Chem. 2001 May 25;276(21):17635-40. Epub 2001 Feb 22.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nunez, J. I.</style></author><author><style face="normal" font="default" size="100%">Martin, M. J.</style></author><author><style face="normal" font="default" size="100%">Piccone, M. E.</style></author><author><style face="normal" font="default" size="100%">Carrillo, E.</style></author><author><style face="normal" font="default" size="100%">Palma, E. L.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Sobrino, F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identification of optimal regions for phylogenetic studies on VP1 gene of foot-and-mouth disease virus: analysis of types A and O Argentinean viruses</style></title><secondary-title><style face="normal" font="default" size="100%">Vet Res</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acid Sequence Animals Aphthovirus/classification/*genetics Base Sequence Capsid/chemistry/*genetics Capsid Proteins DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Complementary/chemistry Molecular Sequence Data *Phylogeny Polymerase Chain Reaction RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Viral/chemistry/genetics Serotyping Viral Proteins/analysis/*genetics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=11254175</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">31-45</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">An analysis of the informative content of sequence stretches on the foot-and-mouth disease virus (FMDV) VPI gene was applied to two important viral serotypes: A and O. Several sequence regions were identified to allow the reconstruction of phylogenetic trees equivalent to those derived from the whole VPI gene. The optimal informative regions for sequence windows of 150 to 250 nt were predicted between positions 250 and 550 of the gene. The sequences spanning the 250 nt of the 3’ end (positions 400 to 650), extensively used for FMDV phylogenetic analyses, showed a lower informative content. In spite of this, the use of sequences from this region allowed the derivation of phylogenetic trees for type A and type O FMDVs which showed topologies similar to those previously reported for the whole VP1 gene. When the sequences determined for viruses isolated in Argentina, between 1990 and 1993, were included in these analyses, the results obtained revealed features of the circulation of type A and type O viruses in the field, in the months that preceded the eradication of the disease in this country. Type A viruses were closely related to an Argentinean vaccine strain, and defined an independent cluster within this serotype. Among the type O viruses analysed, two groups were distinguished; one was closely related to the South American vaccine strains, while the other was grouped with viruses of the O3 subtype. In addition, a detailed phylogeny for type A FMDV is presented.</style></abstract><notes><style face="normal" font="default" size="100%">Nunez, J I Martin, M J Piccone, M E Carrillo, E Palma, E L Dopazo, J Sobrino, F Research Support, Non-U.S. Gov’t France Veterinary research Vet Res. 2001 Jan-Feb;32(1):31-45.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Punt, P. J.</style></author><author><style face="normal" font="default" size="100%">van Luijk, N.</style></author><author><style face="normal" font="default" size="100%">van den Hondel, C. A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The secretion pathway in filamentous fungi: a biotechnological view</style></title><secondary-title><style face="normal" font="default" size="100%">Fungal Genet Biol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals Biotechnology/*methods Fungal Proteins/*genetics/*metabolism Fungi/*genetics/*metabolism Humans Recombinant Proteins/metabolism</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=11495573</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">155-71</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The high capacity of the secretion machinery of filamentous fungi has been widely exploited for the production of homologous and heterologous proteins; however, our knowledge of the fungal secretion pathway is still at an early stage. Most of the knowledge comes from models developed in yeast and higher eukaryotes, which have served as reference for the studies on fungal species. In this review we compile the data accumulated in recent years on the molecular basis of fungal secretion, emphasizing the relevance of these data for the biotechnological use of the fungal cell and indicating how this information has been applied in attempts to create improved production strains. We also present recent emerging approaches that promise to provide answers to fundamental questions on the molecular genetics of the fungal secretory pathway.</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Conesa, A Punt, P J van Luijk, N van den Hondel, C A Review United States Fungal genetics and biology : FG &amp;amp; B Fungal Genet Biol. 2001 Aug;33(3):155-71.&lt;/p&gt;</style></notes></record></records></xml>