02333nas a2200373   4500008004100000022001400041245009100055210006900146260001600215300000800231490000700239520108200246653002401328653002601352653002301378653001101401100003001412700002901442700002801471700002801499700003701527700002401564700003101588700001801619700002701637700002501664700003101689700002401720700002001744700002601764700003201790700002501822856011201847       2021                        eng d  a1471-210500aA comprehensive database for integrated analysis of omics data in autoimmune diseases.0 acomprehensive database for integrated analysis of omics data in   c2021 Jun 24  a3430 v223 a<p><b>BACKGROUND: </b>Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field.</p><p><b>RESULTS: </b>Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.</p><p><b>CONCLUSIONS: </b>This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.</p>10aAutoimmune Diseases10aComputational Biology10aDatabases, Factual10aHumans1 aMartorell-Marugán, Jordi1 aLópez-Domínguez, Raúl1 aGarcía-Moreno, Adrián1 aToro-Domínguez, Daniel1 aVillatoro-García, Juan, Antonio1 aBarturen, Guillermo1 aMartín-Gómez, Adoración1 aTroule, Kevin1 aGómez-López, Gonzalo1 aAl-Shahrour, Fátima1 aGonzález-Rumayor, Víctor1 aPeña-Chilet, Maria1 aDopazo, Joaquin1 aSaez-Rodriguez, Julio1 aAlarcón-Riquelme, Marta, E1 aCarmona-Sáez, Pedro  uhttp://clinbioinfosspa.es/content/comprehensive-database-integrated-analysis-omics-data-autoimmune-diseases02190nas a2200241   4500008004100000022001400041245006800055210006700123260001600190300001200206490000700218520142700225653001901652653002601671653001101697653002301708100002701731700002001758700002201778700002201800700002501822856010101847       2019                        eng d  a1477-405400aPrecision medicine needs pioneering clinical bioinformaticians.0 aPrecision medicine needs pioneering clinical bioinformaticians  c2019 May 21  a752-7660 v203 a<p>Success in precision medicine depends on accessing high-quality genetic and molecular data from large, well-annotated patient cohorts that couple biological samples to comprehensive clinical data, which in conjunction can lead to effective therapies. From such a scenario emerges the need for a new professional profile, an expert bioinformatician with training in clinical areas who can make sense of multi-omics data to improve therapeutic interventions in patients, and the design of optimized basket trials. In this review, we first describe the main policies and international initiatives that focus on precision medicine. Secondly, we review the currently ongoing clinical trials in precision medicine, introducing the concept of 'precision bioinformatics', and we describe current pioneering bioinformatics efforts aimed at implementing tools and computational infrastructures for precision medicine in health institutions around the world. Thirdly, we discuss the challenges related to the clinical training of bioinformaticians, and the urgent need for computational specialists capable of assimilating medical terminologies and protocols to address real clinical questions. We also propose some skills required to carry out common tasks in clinical bioinformatics and some tips for emergent groups. Finally, we explore the future perspectives and the challenges faced by precision medicine bioinformatics.</p>10aCohort Studies10aComputational Biology10aHumans10aPrecision Medicine1 aGómez-López, Gonzalo1 aDopazo, Joaquin1 aCigudosa, Juan, C1 aValencia, Alfonso1 aAl-Shahrour, Fátima  uhttp://clinbioinfosspa.es/content/precision-medicine-needs-pioneering-clinical-bioinformaticians02417nas a2200469   4500008004100000022001400041245007800055210006900133260001300202300001300215490000700228520094000235653001001175653002101185653003001206653004101236653002601277653001101303653002101314653002201335653004401357653001901401653001801420653002701438100002001465700003001485700002701515700002501542700002101567700002401588700002801612700002601640700002701666700002401693700002201717700002101739700001501760700002001775700002301795700002101818856010801839       2009                        eng d  a1029-240300aFunctional signatures identified in B-cell non-Hodgkin lymphoma profiles.0 aFunctional signatures identified in Bcell nonHodgkin lymphoma pr  c2009 Oct  a1699-7080 v503 a<p>Gene-expression profiling in B-cell lymphomas has provided crucial data on specific lymphoma types, which can contribute to the identification of essential lymphoma survival genes and pathways. In this study, the gene-expression profiling data of all major B-cell lymphoma types were analyzed by unsupervised clustering. The transcriptome classification so obtained, was explored using gene set enrichment analysis generating a heatmap for B-cell lymphoma that identifies common lymphoma survival mechanisms and potential therapeutic targets, recognizing sets of coregulated genes and functional pathways expressed in different lymphoma types. Some of the most relevant signatures (stroma, cell cycle, B-cell receptor (BCR)) are shared by multiple lymphoma types or subclasses. A specific attention was paid to the analysis of BCR and coregulated pathways, defining molecular heterogeneity within multiple B-cell lymphoma types.</p>10aAdult10aCluster Analysis10aGene Expression Profiling10aGene Expression Regulation, Leukemic10aGenetic Heterogeneity10aHumans10aLymphoma, B-Cell10aNeoplasm Proteins10aOligonucleotide Array Sequence Analysis10aRNA, Messenger10aRNA, Neoplasm10aTranscription, Genetic1 aAggarwal, Mohit1 aSánchez-Beato, Margarita1 aGómez-López, Gonzalo1 aAl-Shahrour, Fátima1 aMartínez, Nerea1 aRodríguez, Antonia1 aRuiz-Ballesteros, Elena1 aCamacho, Francisca, I1 aPérez-Rosado, Alberto1 ade la Cueva, Paloma1 aArtiga, María, J1 aPisano, David, G1 aKimby, Eva1 aDopazo, Joaquin1 aVilluendas, Raquel1 aPiris, Miguel, A  uhttp://clinbioinfosspa.es/content/functional-signatures-identified-b-cell-non-hodgkin-lymphoma-profiles03382nas a2200325   4500008004100000022001400041245007200055210006900127260001600196300000800212490000700220520234600227653001502573653002102588653004402609653002602653653002802679653001102707653003002718653001302748653001102761653004402772653003002816653003102846100002002877700001902897700002502916700002002941856009502961       2009                        eng d  a1471-216400aGene set internal coherence in the context of functional profiling.0 aGene set internal coherence in the context of functional profili  c2009 Apr 27  a1970 v103 a<p><b>BACKGROUND: </b>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.</p><p><b>RESULTS: </b>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.</p><p><b>CONCLUSION: </b>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.</p>10aAlgorithms10aBreast Neoplasms10aCarcinoma, Intraductal, Noninfiltrating10aComputational Biology10aDatabases, Nucleic Acid10aFemale10aGene Expression Profiling10aGenomics10aHumans10aOligonucleotide Array Sequence Analysis10aPapillomavirus Infections10aReproducibility of Results1 aMontaner, David1 aMinguez, Pablo1 aAl-Shahrour, Fátima1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/gene-set-internal-coherence-context-functional-profiling02023nas a2200349   4500008004100000022001400041245014400055210006900199260001300268300001100281490000700292520085700299653002501156653002101181653001101202653001001213653002201223653003401245653001101279653003601290653001301326653002801339100002001367700002001387700002101407700002601428700002101454700002301475700002501498700002001523856013001543       2009                        eng d  a1362-496200aGene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies.0 aGene setbased analysis of polymorphisms finding pathways or biol  c2009 Jul  aW340-40 v373 a<p>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/.</p>10aBiological Phenomena10aBreast Neoplasms10aFemale10aGenes10aGenetic Variation10aGenome-Wide Association Study10aHumans10aPolymorphism, Single Nucleotide10aSoftware10aUser-Computer Interface1 aMedina, Ignacio1 aMontaner, David1 aBonifaci, Núria1 aPujana, Miguel, Angel1 aCarbonell, José1 aTárraga, Joaquín1 aAl-Shahrour, Fátima1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/gene-set-based-analysis-polymorphisms-finding-pathways-or-biological-processes-associated-001732nas a2200277   4500008004100000022001400041245009700055210006900152260001300221300001200234490000700246520083000253653002201083653003701105653002301142653001101165653001301176653003201189653001301221100001901234700001801253700002001271700002501291700002001316856011801336       2009                        eng d  a1362-496200aSNOW, a web-based tool for the statistical analysis of protein-protein interaction networks.0 aSNOW a webbased tool for the statistical analysis of proteinprot  c2009 Jul  aW109-140 v373 a<p>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.</p>10aComputer Graphics10aData Interpretation, Statistical10aDatabases, Protein10aHumans10aInternet10aProtein Interaction Mapping10aSoftware1 aMinguez, Pablo1 aGötz, Stefan1 aMontaner, David1 aAl-Shahrour, Fátima1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/snow-web-based-tool-statistical-analysis-protein-protein-interaction-networks-001564nas a2200229   4500008004100000022001400041245003200055210003100087260000900118300001100127490000800138520093500146653001201081653002601093653002001119653003001139653001101169653004401180100002001224700002501244856006501269       2008                        eng d  a1064-374500aExpression and microarrays.0 aExpression and microarrays  c2008  a245-550 v4533 a<p>High throughput methodologies have increased by several orders of magnitude the amount of experimental microarray data available. Nevertheless, translating these data into useful biological knowledge remains a challenge. There is a risk of perceiving these methodologies as mere factories that produce never-ending quantities of data if a proper biological interpretation is not provided. Methods of interpreting these data are continuously evolving. Typically, a simple two-step approach has been used, in which genes of interest are first selected based on thresholds for the experimental values, and then enrichment in biologically relevant terms in the annotations of these genes is analyzed in a second step. For various reasons, such methods are quite poor in terms of performance and new procedures inspired by systems biology that directly address sets of functionally related genes are currently under development.</p>10aAnimals10aComputational Biology10agene expression10aGene Expression Profiling10aHumans10aOligonucleotide Array Sequence Analysis1 aDopazo, Joaquin1 aAl-Shahrour, Fátima  uhttp://clinbioinfosspa.es/content/expression-and-microarrays02985nas a2200421   4500008004100000022001400041245007000055210006800125260001600193300000800209490000600217520169900223653003601922653003001958653004301988653001102031653000902042653002302051653002002074653002402094653002202118653001402140653002402154653003202178653001902210653002402229653002002253100002202273700002402295700002002319700002502339700001602364700001702380700002202397700002002419700002602439856009802465       2007                        eng d  a1471-216400aEvidence for systems-level molecular mechanisms of tumorigenesis.0 aEvidence for systemslevel molecular mechanisms of tumorigenesis  c2007 Jun 20  a1850 v83 a<p><b>BACKGROUND: </b>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.</p><p><b>RESULTS: </b>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 "CGPs") 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.</p><p><b>CONCLUSION: </b>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.</p>10aCell Transformation, Neoplastic10aGene Expression Profiling10aGene Expression Regulation, Neoplastic10aHumans10aMale10aModels, Biological10aModels, Genetic10aModels, Statistical10aNeoplasm Proteins10aNeoplasms10aProstatic Neoplasms10aProtein Interaction Mapping10aRNA, Messenger10aSignal Transduction10aSystems biology1 aHernández, Pilar1 aHuerta-Cepas, Jaime1 aMontaner, David1 aAl-Shahrour, Fátima1 aValls, Joan1 aGómez, Laia1 aCapellà, Gabriel1 aDopazo, Joaquin1 aPujana, Miguel, Angel  uhttp://clinbioinfosspa.es/content/evidence-systems-level-molecular-mechanisms-tumorigenesis-002675nas a2200385   4500008004100000022001400041245016600055210006900221260001300290300001000303490000700313520140700320653002201727653001201749653001801761653002601779653003001805653001001835653001301845653001101858653001301869653004401882653002601926653001301952653002401965653002601989100002502015700001902040700002302059700002002082700001602102700002002118700002002138856013102158       2007                        eng d  a1362-496200aFatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments.0 aFatiGO a functional profiling tool for genomic data Integration   c2007 Jul  aW91-60 v353 a<p>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.</p>10aAmino Acid Motifs10aAnimals10aBinding Sites10aComputational Biology10aGene Expression Profiling10aGenes10aGenomics10aHumans10aInternet10aOligonucleotide Array Sequence Analysis10aProgramming Languages10aSoftware10aSystems Integration10aTranscription Factors1 aAl-Shahrour, Fátima1 aMinguez, Pablo1 aTárraga, Joaquín1 aMedina, Ignacio1 aAlloza, Eva1 aMontaner, David1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/fatigo-functional-profiling-tool-genomic-data-integration-functional-annotation-regulatory-002054nas a2200385   4500008004100000022001400041245012300055210006900178260001300247300001000260490000700270520079500277653001201072653002101084653002601105653002201131653003001153653001101183653001301194653002001207653003101227653004401258653002601302653001301328653002401341653002801365100001701393700002001410700002701430700002301457700002001480700002501500700002001525856012301545       2007                        eng d  a1362-496200aISACGH: a web-based environment for the analysis of Array CGH and gene expression which includes functional profiling.0 aISACGH a webbased environment for the analysis of Array CGH and   c2007 Jul  aW81-50 v353 a<p>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.</p>10aAnimals10aCluster Analysis10aComputational Biology10aComputer Graphics10aGene Expression Profiling10aHumans10aInternet10aModels, Genetic10aNucleic Acid Hybridization10aOligonucleotide Array Sequence Analysis10aProgramming Languages10aSoftware10aSystems Integration10aUser-Computer Interface1 aConde, Lucia1 aMontaner, David1 aBurguet-Castell, Jordi1 aTárraga, Joaquín1 aMedina, Ignacio1 aAl-Shahrour, Fátima1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/isacgh-web-based-environment-analysis-array-cgh-and-gene-expression-which-includes-0