<?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%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Panadero, Joaquin</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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrated gene set analysis for microRNA studies.</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%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</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%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</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 Sep 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">2809-16</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;MOTIVATION: &lt;/b&gt;Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis Nevertheless, major limitations of this approach have already been described at the gene level, while some newer arise in the miRNA scenario.Here, we propose an enhanced methodology that builds on the well-established gene set analysis paradigm. Evidence for differential expression at the miRNA level is transferred to a gene differential inhibition score which is easily interpretable in terms of gene sets or pathways. Such transferred indexes account for the additive effect of several miRNAs targeting the same gene, and also incorporate cancellation effects between cases and controls. Together, these two desirable characteristics allow for more accurate modeling of regulatory processes.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We analyze high-throughput sequencing data from 20 different cancer types and provide exhaustive reports of gene and Gene Ontology-term deregulation by miRNA action.&lt;/p&gt;&lt;p&gt;&lt;b&gt;AVAILABILITY AND IMPLEMENTATION: &lt;/b&gt;The proposed methodology was implemented in the Bioconductor library mdgsa http://bioconductor.org/packages/mdgsa For the purpose of reproducibility all of the scripts are available at https://github.com/dmontaner-papers/gsa4mirna&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONTACT: &lt;/b&gt;: david.montaner@gmail.com&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%">18</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/27324197?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%">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%">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%">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%">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%">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%">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%">Ventoso, Iván</style></author><author><style face="normal" font="default" size="100%">Kochetov, Alex</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%">Santoyo, Javier</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extensive translatome remodeling during ER stress response in mammalian cells.</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%">Endoplasmic Reticulum Stress</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Jurkat Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">NIH 3T3 Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Biosynthesis</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription, Genetic</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%">e35915</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 this work we have described the translatome of two mammalian cell lines, NIH3T3 and Jurkat, by scoring the relative polysome association of ∼10,000 mRNA under normal and ER stress conditions. We have found that translation efficiencies of mRNA correlated poorly with transcript abundance, although a general tendency was observed so that the highest translation efficiencies were found in abundant mRNA. Despite the differences found between mouse (NIH3T3) and human (Jurkat) cells, both cell types share a common translatome composed by ∼800-900 mRNA that encode proteins involved in basic cellular functions. Upon stress, an extensive remodeling in translatomes was observed so that translation of ∼50% of mRNA was inhibited in both cell types, this effect being more dramatic for those mRNA that accounted for most of the cell translation. Interestingly, we found two subsets comprising 1000-1500 mRNA whose translation resisted or was induced by stress. Translation arrest resistant class includes many mRNA encoding aminoacyl tRNA synthetases, ATPases and enzymes involved in DNA replication and stress response such as BiP. This class of mRNA is characterized by high translation rates in both control and stress conditions. Translation inducible class includes mRNA whose translation was relieved after stress, showing a high enrichment in early response transcription factors of bZIP and zinc finger C2H2 classes. Unlike yeast, a general coordination between changes in translation and transcription upon stress (potentiation) was not observed in mammalian cells. Among the different features of mRNA analyzed, we found a relevant association of translation efficiency with the presence of upstream ATG in the 5'UTR and with the length of coding sequence of mRNA, and a looser association with other parameters such as the length and the G+C content of 5'UTR. A model for translatome remodeling during the acute phase of stress response in mammalian cells is proposed.&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/22574127?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 Ma</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Núñez-Torres, Rocío</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</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%">Torroglosa, Ana</style></author><author><style face="normal" font="default" size="100%">Marbà, Martina</style></author><author><style face="normal" font="default" size="100%">Enguix-Riego, Ma Valle</style></author><author><style face="normal" font="default" size="100%">Montaner, David</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%">Four new loci associations discovered by pathway-based and network analyses of the genome-wide variability profile of 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%">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%">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></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Dec 28</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">103</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Finding gene associations in rare diseases is frequently hampered by the reduced numbers of patients accessible. Conventional gene-based association tests rely on the availability of large cohorts, which constitutes a serious limitation for its application in this scenario. To overcome this problem we have used here a combined strategy in which a pathway-based analysis (PBA) has been initially conducted to prioritize candidate genes in a Spanish cohort of 53 trios of short-segment Hirschsprung's disease. Candidate genes have been further validated in an independent population of 106 trios. The study revealed a strong association of 11 gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other HSCR-related processes. Among the preselected candidates, a total of 4 loci, RASGEF1A, IQGAP2, DLC1 and CHRNA7, related to signal transduction and migration processes, were found to be significantly associated to HSCR. Network analysis also confirms their involvement in the network of already known disease genes. This approach, based on the study of functionally-related gene sets, requires of lower sample sizes and opens new opportunities for the study of rare diseases.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/23270508?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 Ma</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Núñez-Torres, Rocío</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</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%">Torroglosa, Ana</style></author><author><style face="normal" font="default" size="100%">Marbà, Martina</style></author><author><style face="normal" font="default" size="100%">Enguix-Riego, Ma Valle</style></author><author><style face="normal" font="default" size="100%">Montaner, David</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%">Four new loci associations discovered by pathway-based and network analyses of the genome-wide variability profile of Hirschsprung’s disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Orphanet journal of rare diseases</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 Dec 28</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ojrd.com/content/7/1/103/abstract</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">103</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">ABSTRACT: Finding gene associations in rare diseases is frequently hampered by the reduced numbers of patients accessible. Conventional gene-based association tests rely on the availability of large cohorts, which constitutes a serious limitation for its application in this scenario. To overcome this problem we have used here a combined strategy in which a pathway-based analysis (PBA) has been initially conducted to prioritize candidate genes in a Spanish cohort of 53 trios of short-segment Hirschsprung’s disease. Candidate genes have been further validated in an independent population of 106 trios. The study revealed a strong association of 11 gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other HSCR-related processes. Among the preselected candidates, a total of 4 loci, RASGEF1A, IQGAP2, DLC1 and CHRNA7, related to signal transduction and migration processes, were found to be significantly associated to HSCR. Network analysis also confirms their involvement in the network of already known disease genes. This approach, based on the study of functionally-related gene sets, requires of lower sample sizes and opens new opportunities for the study of rare 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%">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%">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%">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%">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%">Bediaga, Naiara G</style></author><author><style face="normal" font="default" size="100%">Acha-Sagredo, Amelia</style></author><author><style face="normal" font="default" size="100%">Guerra, Isabel</style></author><author><style face="normal" font="default" size="100%">Viguri, Amparo</style></author><author><style face="normal" font="default" size="100%">Albaina, Carmen</style></author><author><style face="normal" font="default" size="100%">Ruiz Diaz, Irune</style></author><author><style face="normal" font="default" size="100%">Rezola, Ricardo</style></author><author><style face="normal" font="default" size="100%">Alberdi, Maria Jesus</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%">Renobales, Mertxe</style></author><author><style face="normal" font="default" size="100%">Fernandez, Agustin F</style></author><author><style face="normal" font="default" size="100%">Field, John K</style></author><author><style face="normal" font="default" size="100%">Fraga, Mario F</style></author><author><style face="normal" font="default" size="100%">Liloglou, Triantafillos</style></author><author><style face="normal" font="default" size="100%">de Pancorbo, Marian M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DNA methylation epigenotypes in breast cancer molecular subtypes.</style></title><secondary-title><style face="normal" font="default" size="100%">Breast Cancer Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Breast Cancer Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">CpG Islands</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Methylation</style></keyword><keyword><style  face="normal" font="default" size="100%">Epigenesis, 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%">Genes, p53</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%">Ki-67 Antigen</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 Grading</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptor, ErbB-2</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%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">R77</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;Identification of gene expression based breast cancer subtypes is considered as a critical means of prognostication. Genetic mutations along with epigenetic alterations contribute to gene expression changes occurring in breast cancer. So far, these epigenetic contributions to sporadic breast cancer subtypes have not been well characterized, and there is only a limited understanding of the epigenetic mechanisms affected in those particular breast cancer subtypes. The present study was undertaken to dissect the breast cancer methylome and deliver specific epigenotypes associated with particular breast cancer subtypes.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Using a microarray approach we analyzed DNA methylation in regulatory regions of 806 cancer related genes in 28 breast cancer paired samples. We subsequently performed substantial technical and biological validation by Pyrosequencing, investigating the top qualifying 19 CpG regions in independent cohorts encompassing 47 basal-like, 44 ERBB2+ overexpressing, 48 luminal A and 48 luminal B paired breast cancer/adjacent tissues. Using all-subset selection method, we identified the most subtype predictive methylation profiles in multivariable logistic regression analysis.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;The approach efficiently recognized 15 individual CpG loci differentially methylated in breast cancer tumor subtypes. We further identify novel subtype specific epigenotypes which clearly demonstrate the differences in the methylation profiles of basal-like and human epidermal growth factor 2 (HER2)-overexpressing tumors.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Our results provide evidence that well defined DNA methylation profiles enables breast cancer subtype prediction and support the utilization of this biomarker for prognostication and therapeutic stratification of patients with breast cancer.&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/20920229?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%">Moreno-Manzano, Victoria</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Jiménez, Francisco J</style></author><author><style face="normal" font="default" size="100%">Aceña-Bonilla, Jose L</style></author><author><style face="normal" font="default" size="100%">Fustero-Lardíes, Santos</style></author><author><style face="normal" font="default" size="100%">Erceg, Slaven</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%">Stojkovic, Miodrag</style></author><author><style face="normal" font="default" size="100%">Sánchez-Puelles, Jose M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">FM19G11, a new hypoxia-inducible factor (HIF) modulator, affects stem cell differentiation status.</style></title><secondary-title><style face="normal" font="default" size="100%">The Journal of biological chemistry</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 8</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">285</style></volume><pages><style face="normal" font="default" size="100%">1333-42</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 biology of the alpha subunits of hypoxia-inducible factors (HIFalpha) has expanded from their role in angiogenesis to their current position in the self-renewal and differentiation of stem cells. The results reported in this article show the discovery of FM19G11, a novel chemical entity that inhibits HIFalpha proteins that repress target genes of the two alpha subunits, in various tumor cell lines as well as in adult and embryonic stem cell models from rodents and humans, respectively. FM19G11 inhibits at nanomolar range the transcriptional and protein expression of Oct4, Sox2, Nanog, and Tgf-alpha undifferentiating factors, in adult rat and human embryonic stem cells, FM19G11 activity occurs in ependymal progenitor stem cells from rats (epSPC), a cell model reported for spinal cord regeneration, which allows the progression of oligodendrocyte cell differentiation in a hypoxic environment, has created interest in its characterization for pharmacological research. Experiments using small interfering RNA showed a significant depletion in Sox2 protein only in the case of HIF2alpha silencing, but not in HIF1alpha-mediated ablation. Moreover, chromatin immunoprecipitation data, together with the significant presence of functional hypoxia response element consensus sequences in the promoter region of Sox2, strongly validated that this factor behaves as a target gene of HIF2alpha in epSPCs. FM19G11 causes a reduction of overall histone acetylation with significant repression of p300, a histone acetyltransferase required as a co-factor for HIF-transcription activation. Arrays carried out in the presence and absence of the inhibitor showed the predominant involvement of epigenetic-associated events mediated by the drug.&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%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Póo, M Eugenia</style></author><author><style face="normal" font="default" size="100%">Valbuena, Diana</style></author><author><style face="normal" font="default" size="100%">Ruiz, Veronica</style></author><author><style face="normal" font="default" size="100%">Aguilar, Cristóbal</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</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%">Functional genomics of 5- to 8-cell stage human embryos by blastomere single-cell cDNA analysis.</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%">Blastomeres</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA, Complementary</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%">Oligonucleotide Array Sequence Analysis</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 Oct 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">e13615</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Blastomere fate and embryonic genome activation (EGA) during human embryonic development are unsolved areas of high scientific and clinical interest. Forty-nine blastomeres from 5- to 8-cell human embryos have been investigated following an efficient single-cell cDNA amplification protocol to provide a template for high-density microarray analysis. The previously described markers, characteristic of Inner Cell Mass (ICM) (n = 120), stemness (n = 190) and Trophectoderm (TE) (n = 45), were analyzed, and a housekeeping pattern of 46 genes was established. All the human blastomeres from the 5- to 8-cell stage embryo displayed a common gene expression pattern corresponding to ICM markers (e.g., DDX3, FOXD3, LEFTY1, MYC, NANOG, POU5F1), stemness (e.g., POU5F1, DNMT3B, GABRB3, SOX2, ZFP42, TERT), and TE markers (e.g., GATA6, EOMES, CDX2, LHCGR). The EGA profile was also investigated between the 5-6- and 8-cell stage embryos, and compared to the blastocyst stage. Known genes (n = 92) such as depleted maternal transcripts (e.g., CCNA1, CCNB1, DPPA2) and embryo-specific activation (e.g., POU5F1, CDH1, DPPA4), as well as novel genes, were confirmed. In summary, the global single-cell cDNA amplification microarray analysis of the 5- to 8-cell stage human embryos reveals that blastomere fate is not committed to ICM or TE. Finally, new EGA features in human embryogenesis are presented.&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/21049019?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%">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%">Multidimensional gene set analysis 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%">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%">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%">Genomics</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></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Apr 27</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">e10348</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 functional implications of changes in gene expression, mutations, etc., is the aim of most genomic experiments. To achieve this, several functional profiling methods have been proposed. Such methods study the behaviour of different gene modules (e.g. gene ontology terms) in response to one particular variable (e.g. differential gene expression). In spite to the wealth of information provided by functional profiling methods, a common limitation to all of them is their inherent unidimensional nature. In order to overcome this restriction we present a multidimensional logistic model that allows studying the relationship of gene modules with different genome-scale measurements (e.g. differential expression, genotyping association, methylation, copy number alterations, heterozygosity, etc.) simultaneously. Moreover, the relationship of such functional modules with the interactions among the variables can also be studied, which produces novel results impossible to be derived from the conventional unidimensional functional profiling methods. We report sound results of gene sets associations that remained undetected by the conventional one-dimensional gene set analysis in several examples. Our findings demonstrate the potential of the proposed approach for the discovery of new cell functionalities with complex dependences on more than one variable.&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/20436964?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%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</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 internal coherence in the context of functional profiling.</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%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Carcinoma, Intraductal, Noninfiltrating</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Nucleic Acid</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%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Papillomavirus Infections</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%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009 Apr 27</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">197</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;Functional profiling methods have been extensively used in the context of high-throughput experiments and, in particular, in microarray data analysis. Such methods use available biological information to define different types of functional gene modules (e.g. gene ontology -GO-, KEGG pathways, etc.) whose representation in a pre-defined list of genes is further studied. In the most popular type of microarray experimental designs (e.g. up- or down-regulated genes, clusters of co-expressing genes, etc.) or in other genomic experiments (e.g. Chip-on-chip, epigenomics, etc.) these lists are composed by genes with a high degree of co-expression. Therefore, an implicit assumption in the application of functional profiling methods within this context is that the genes corresponding to the modules tested are effectively defining sets of co-expressing genes. Nevertheless not all the functional modules are biologically coherent entities in terms of co-expression, which will eventually hinder its detection with conventional methods of functional enrichment.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Using a large collection of microarray data we have carried out a detailed survey of internal correlation in GO terms and KEGG pathways, providing a coherence index to be used for measuring functional module co-regulation. An unexpected low level of internal correlation was found among the modules studied. Only around 30% of the modules defined by GO terms and 57% of the modules defined by KEGG pathways display an internal correlation higher than the expected by chance.This information on the internal correlation of the genes within the functional modules can be used in the context of a logistic regression model in a simple way to improve their detection in gene expression experiments.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;For the first time, an exhaustive study on the internal co-expression of the most popular functional categories has been carried out. Interestingly, the real level of coexpression within many of them is lower than expected (or even inexistent), which will preclude its detection by means of most conventional functional profiling methods. If the gene-to-function correlation information is used in functional profiling methods, the results obtained improve the ones obtained by conventional enrichment methods.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/19397819?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%">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%">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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Gotz, S.</style></author><author><style face="normal" font="default" size="100%">Montaner, David</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%">SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks</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%">interactome</style></keyword><keyword><style  face="normal" font="default" size="100%">network</style></keyword><keyword><style  face="normal" font="default" size="100%">snow</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/content/early/2009/05/19/nar.gkp402.full</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">37</style></volume><pages><style face="normal" font="default" size="100%">W109-114</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding the structure and the dynamics of the complex intercellular network of interactions that contributes to the structure and function of a living cell is one of the main challenges of today’s biology. SNOW inputs a collection of protein (or gene) identifiers and, by using the interactome as scaffold, draws the connections among them, calculates several relevant network parameters and, as a novelty among the rest of tools of its class, it estimates their statistical significance. The parameters calculated for each node are: connectivity, betweenness and clustering coefficient. It also calculates the number of components, number of bicomponents and articulation points. An interactive network viewer is also available to explore the resulting network. SNOW is available at http://snow.bioinfo.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%">Minguez, Pablo</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%">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%">SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks.</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%">Data Interpretation, Statistical</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Protein</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%">Protein Interaction Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</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%">W109-14</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding the structure and the dynamics of the complex intercellular network of interactions that contributes to the structure and function of a living cell is one of the main challenges of today's biology. SNOW inputs a collection of protein (or gene) identifiers and, by using the interactome as scaffold, draws the connections among them, calculates several relevant network parameters and, as a novelty among the rest of tools of its class, it estimates their statistical significance. The parameters calculated for each node are: connectivity, betweenness and clustering coefficient. It also calculates the number of components, number of bicomponents and articulation points. An interactive network viewer is also available to explore the resulting network. SNOW is available at http://snow.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/19454602?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%">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%">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%">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%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</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%">Alloza, Eva</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%">FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments.</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 Motifs</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Binding Sites</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</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%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</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%">Transcription Factors</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%">W91-6</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 ultimate goal of any genome-scale experiment is to provide a functional interpretation of the data, relating the available information with the hypotheses that originated the experiment. Thus, functional profiling methods have become essential in diverse scenarios such as microarray experiments, proteomics, etc. We present the FatiGO+, a web-based tool for the functional profiling of genome-scale experiments, specially oriented to the interpretation of microarray experiments. In addition to different functional annotations (gene ontology, KEGG pathways, Interpro motifs, Swissprot keywords and text-mining based bioentities related to diseases and chemical compounds) FatiGO+ includes, as a novelty, regulatory and structural information. The regulatory information used includes predictions of targets for distinct regulatory elements (obtained from the Transfac and CisRed databases). Additionally FatiGO+ uses predictions of target motifs of miRNA to infer which of these can be activated or deactivated in the sample of genes studied. Finally, properties of gene products related to their relative location and connections in the interactome have also been used. Also, enrichment of any of these functional terms can be directly analysed on chromosomal coordinates. FatiGO+ can be found at: http://www.fatigoplus.org and within the Babelomics environment http://www.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/17478504?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%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">Arbiza, Leonardo</style></author><author><style face="normal" font="default" size="100%">Dopazo, Hernán</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%">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%">From genes to functional classes in the study of biological systems.</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%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosome Mapping</style></keyword><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%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Multigene Family</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%">Systems biology</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 Apr 03</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">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;BACKGROUND: &lt;/b&gt;With the popularization of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://www.babelomics.org.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/17407596?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%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</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%">Functional profiling of microarray experiments using text-mining derived bioentities.</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%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Information Storage and Retrieval</style></keyword><keyword><style  face="normal" font="default" size="100%">Natural Language Processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Research Design</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems Integration</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 Nov 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">3098-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;MOTIVATION: &lt;/b&gt;The increasing use of microarray technologies brought about a parallel demand in methods for the functional interpretation of the results. Beyond the conventional functional annotations for genes, such as gene ontology, pathways, etc. other sources of information are still to be exploited. Text-mining methods allow extracting informative terms (bioentities) with different functional, chemical, clinical, etc. meanings, that can be associated to genes. We show how to use these associations within an appropriate statistical framework and how to apply them through easy-to-use, web-based environments to the functional interpretation of microarray experiments. Functional enrichment and gene set enrichment tests using bioentities are presented.&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/17855415?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%">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></records></xml>