02097nas a2200241 4500008004100000245015300041210006900194260000900263300001100272490000600283520122100289100002001510700002301530700002101553700002401574700002101598700001801619700002201637700002001659700002301679700002401702856012901726 2011 eng d00aAnalysis of normal-tumour tissue interaction in tumours: prediction of prostate cancer features from the molecular profile of adjacent normal cells.0 aAnalysis of normaltumour tissue interaction in tumours predictio c2011 ae164920 v63 a

Statistical modelling, in combination with genome-wide expression profiling techniques, has demonstrated that the molecular state of the tumour is sufficient to infer its pathological state. These studies have been extremely important in diagnostics and have contributed to improving our understanding of tumour biology. However, their importance in in-depth understanding of cancer patho-physiology may be limited since they do not explicitly take into consideration the fundamental role of the tissue microenvironment in specifying tumour physiology. Because of the importance of normal cells in shaping the tissue microenvironment we formulate the hypothesis that molecular components of the profile of normal epithelial cells adjacent the tumour are predictive of tumour physiology. We addressed this hypothesis by developing statistical models that link gene expression profiles representing the molecular state of adjacent normal epithelial cells to tumour features in prostate cancer. Furthermore, network analysis showed that predictive genes are linked to the activity of important secreted factors, which have the potential to influence tumor biology, such as IL1, IGF1, PDGF BB, AGT, and TGFβ.

1 aTrevino, Victor1 aTadesse, Mahlet, G1 aVannucci, Marina1 aAl-Shahrour, Fatima1 aAntczak, Philipp1 aDurant, Sarah1 aBikfalvi, Andreas1 aDopazo, Joaquin1 aCampbell, Moray, J1 aFalciani, Francesco uhttps://www.clinbioinfosspa.es/content/analysis-normal-tumour-tissue-interaction-tumours-prediction-prostate-cancer-features02349nas a2200181 4500008004100000022001400041245015400055210006900209260001500278300000700293490000600300520173800306100001502044700002402059700002102083700001502104856004802119 2011 eng d a1755-879400aA large scale survey reveals that chromosomal copy-number alterations significantly affect gene modules involved in cancer initiation and progression0 alarge scale survey reveals that chromosomal copynumber alteratio c06/05/2011 a370 v43 a

Background

Recent observations point towards the existence of a large number of neighborhoods composed of functionally-related gene modules that lie together in the genome. This local component in the distribution of the functionality across chromosomes is probably affecting the own chromosomal architecture by limiting the possibilities in which genes can be arranged and distributed across the genome. As a direct consequence of this fact it is therefore presumable that diseases such as cancer, harboring DNA copy number alterations (CNAs), will have a symptomatology strongly dependent on modules of functionally-related genes rather than on a unique "important" gene.

Methods

We carried out a systematic analysis of more than 140,000 observations of CNAs in cancers and searched by enrichments in gene functional modules associated to high frequencies of loss or gains.

Results

The analysis of CNAs in cancers clearly demonstrates the existence of a significant pattern of loss of gene modules functionally related to cancer initiation and progression along with the amplification of modules of genes related to unspecific defense against xenobiotics (probably chemotherapeutical agents). With the extension of this analysis to an Array-CGH dataset (glioblastomas) from The Cancer Genome Atlas we demonstrate the validity of this approach to investigate the functional impact of CNAs.

Conclusions

The presented results indicate promising clinical and therapeutic implications. Our findings also directly point out to the necessity of adopting a function-centric, rather a gene-centric, view in the understanding of phenotypes or diseases harboring CNAs.

1 aAlloza, E.1 aAl-Shahrour, Fatima1 aCigudosa, J., C.1 aDopazo, J. uhttp://www.biomedcentral.com/1755-8794/4/3702778nas a2200385 4500008004100000245011100041210006900152260001300221300001000234490000700244520152200251100002301773700002601796700001901822700002401841700002701865700002901892700002001921700002001941700002801961700001901989700002302008700002402031700002002055700001602075700002302091700001902114700002002133700002002153700002002173700002002193700002502213700002302238856013102261 2010 eng d00aChanges in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosus.0 aChanges in the pattern of DNA methylation associate with twin di c2010 Feb a170-90 v203 a

Monozygotic (MZ) twins are partially concordant for most complex diseases, including autoimmune disorders. Whereas phenotypic concordance can be used to study heritability, discordance suggests the role of non-genetic factors. In autoimmune diseases, environmentally driven epigenetic changes are thought to contribute to their etiology. Here we report the first high-throughput and candidate sequence analyses of DNA methylation to investigate discordance for autoimmune disease in twins. We used a cohort of MZ twins discordant for three diseases whose clinical signs often overlap: systemic lupus erythematosus (SLE), rheumatoid arthritis, and dermatomyositis. Only MZ twins discordant for SLE featured widespread changes in the DNA methylation status of a significant number of genes. Gene ontology analysis revealed enrichment in categories associated with immune function. Individual analysis confirmed the existence of DNA methylation and expression changes in genes relevant to SLE pathogenesis. These changes occurred in parallel with a global decrease in the 5-methylcytosine content that was concomitantly accompanied with changes in DNA methylation and expression levels of ribosomal RNA genes, although no changes in repetitive sequences were found. Our findings not only identify potentially relevant DNA methylation markers for the clinical characterization of SLE patients but also support the notion that epigenetic changes may be critical in the clinical manifestations of autoimmune disease.

1 aJavierre, Biola, M1 aFernandez, Agustin, F1 aRichter, Julia1 aAl-Shahrour, Fatima1 aMartin-Subero, Ignacio1 aRodriguez-Ubreva, Javier1 aBerdasco, Maria1 aFraga, Mario, F1 aO’Hanlon, Terrance, P1 aRider, Lisa, G1 aJacinto, Filipe, V1 aLopez-Longo, Javier1 aDopazo, Joaquin1 aForn, Marta1 aPeinado, Miguel, A1 aCarreño, Luis1 aSawalha, Amr, H1 aHarley, John, B1 aSiebert, Reiner1 aEsteller, Manel1 aMiller, Frederick, W1 aBallestar, Esteban uhttps://www.clinbioinfosspa.es/content/changes-pattern-dna-methylation-associate-twin-discordance-systemic-lupus-erythematosus03290nas a2200205 4500008004100000245014400041210006900185300001100254490000800265520221900273653002002492653002302512653017902535653018702714100001802901700002402919700001502943700002002958856010602978 2009 eng d00aExploring the antimicrobial action of a carbon monoxide-releasing compound through whole-genome transcription profiling of Escherichia coli0 aExploring the antimicrobial action of a carbon monoxidereleasing a813-240 v1553 a

We recently reported that carbon monoxide (CO) has bactericidal activity. To understand its mode of action we analysed the gene expression changes occurring when Escherichia coli, grown aerobically and anaerobically, is treated with the CO-releasing molecule CORM-2 (tricarbonyldichlororuthenium(II) dimer). Microarray analysis shows that the E. coli CORM-2 response is multifaceted, with a high number of differentially regulated genes spread through several functional categories, namely genes involved in inorganic ion transport and metabolism, regulators, and genes implicated in post-translational modification, such as chaperones. CORM-2 has a higher impact in E. coli cells grown anaerobically, as judged by the repression of genes belonging to eight functional classes which are not seen in the response of aerobically CORM-2-treated cells. The biological relevance of the variations caused by CORM-2 was substantiated by studying the CORM-2 sensitivity of selected E. coli mutants. The results show that the deletion of redox-sensing regulators SoxS and OxyR increased the sensitivity to CORM-2 and suggest that while SoxS plays an important role in protection against CORM-2 under both growth conditions, OxyR seems to participate only in the aerobic CORM-2 response. Under anaerobic conditions, we found that the heat-shock proteins IbpA and IbpB contribute to CORM-2 defence since the deletion of these genes increases the sensitivity of the strain. The induction of several met genes and the hypersensitivity to CORM-2 of the DeltametR, DeltametI and DeltametN mutant strains suggest that CO has effects on the methionine metabolism of E. coli. CORM-2 also affects the transcription of several E. coli biofilm-related genes and increases biofilm formation in E. coli. In particular, the absence of tqsA or bhsA increases the resistance of E. coli to CORM-2, and deletion of tsqA leads to a strain that has lost its capacity to form biofilm upon treatment with CORM-2. In spite of the relatively stable nature of the CO molecule, our results show that CO is able to trigger a significant alteration in the transcriptome of E. coli which necessarily has effects in several key metabolic pathways.

10aBacterial Genes10aBacterial/genetics10aBiofilms Carbon Monoxide/*metabolism Escherichia coli/*genetics/metabolism Escherichia coli Proteins/genetics/metabolism *Gene Expression Profiling Gene Expression Regulation10aRegulator Genetic Complementation Test Methionine/metabolism Microbial Viability Mutation Oligonucleotide Array Sequence Analysis Organometallic Compounds/*pharmacology Phenotype RNA1 aNobre, L., S.1 aAl-Shahrour, Fatima1 aDopazo, J.1 aSaraiva, L., M. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1924675201715nas a2200265 4500008004100000245014300041210006900184300001300253490000700266520085600273653001501129653001301144653001101157653002701168653000801195100002001203700002001223700002101243700002601264700002101290700002301311700002401334700002001358856007101378 2009 eng d00aGene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies0 aGene setbased analysis of polymorphisms finding pathways or biol aW340-3440 v373 a

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/

10ababelomics10agene set10aGESBAP10apathway-based analysis10aSNP1 aMedina, Ignacio1 aMontaner, David1 aBonifaci, Núria1 aPujana, Miguel, Angel1 aCarbonell, José1 aTárraga, Joaquín1 aAl-Shahrour, Fatima1 aDopazo, Joaquin uhttp://nar.oxfordjournals.org/cgi/content/abstract/37/suppl_2/W34001472nas a2200205 4500008004100000245009600041210006900137300001300206490000700219520083200226653001601058653001201074653000901086100001901095700001301114700002001127700002401147700002001171856007501191 2009 eng d00aSNOW, a web-based tool for the statistical analysis of protein-protein interaction networks0 aSNOW a webbased tool for the statistical analysis of proteinprot aW109-1140 v373 a

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.

10ainteractome10anetwork10asnow1 aMinguez, Pablo1 aGotz, S.1 aMontaner, David1 aAl-Shahrour, Fatima1 aDopazo, Joaquin uhttp://nar.oxfordjournals.org/content/early/2009/05/19/nar.gkp402.full02142nas a2200253 4500008004100000245010200041210006900143300001100212490000700223520138600230653001501616653002401631100002401655700001801679700001601697700001401713700001501727700001601742700002001758700001501778700001701793700001501810856006301825 2008 eng d00aBabelomics: advanced functional profiling of transcriptomics, proteomics and genomics experiments0 aBabelomics advanced functional profiling of transcriptomics prot aW341-60 v363 a

We present a new version of Babelomics, a complete suite of web tools for the functional profiling of genome scale experiments, with new and improved methods as well as more types of functional definitions. Babelomics includes different flavours of conventional functional enrichment methods as well as more advanced gene set analysis methods that makes it a unique tool among the similar resources available. In addition to the well-known functional definitions (GO, KEGG), Babelomics includes new ones such as Biocarta pathways or text mining-derived functional terms. Regulatory modules implemented include transcriptional control (Transfac, CisRed) and other levels of regulation such as miRNA-mediated interference. Moreover, Babelomics allows for sub-selection of terms in order to test more focused hypothesis. Also gene annotation correspondence tables can be imported, which allows testing with user-defined functional modules. Finally, a tool for the ’de novo’ functional annotation of sequences has been included in the system. This allows using yet unannotated organisms in the program. Babelomics has been extensively re-engineered and now it includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. Babelomics is available at http://www.babelomics.org.

10ababelomics10afuntional profiling1 aAl-Shahrour, Fatima1 aCarbonell, J.1 aMinguez, P.1 aGoetz, S.1 aConesa, A.1 aTarraga, J.1 aMedina, Ignacio1 aAlloza, E.1 aMontaner, D.1 aDopazo, J. uhttp://nar.oxfordjournals.org/content/36/suppl_2/W341.long02205nas a2200301 4500008004100000245007600041210006900117300001200186490000700198520130100205653001001506653002901516100001601545700002001561700001801581700002101599700001601620700001501636700002401651700002301675700001401698700001601712700002201728700001501750700001701765700001501782856010601797 2008 eng d00aGEPAS, a web-based tool for microarray data analysis and interpretation0 aGEPAS a webbased tool for microarray data analysis and interpret aW308-140 v363 a

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.

10agepas10amicroarray data analysis1 aTarraga, J.1 aMedina, Ignacio1 aCarbonell, J.1 aHuerta-Cepas, J.1 aMinguez, P.1 aAlloza, E.1 aAl-Shahrour, Fatima1 aVegas-Azcarate, S.1 aGoetz, S.1 aEscobar, P.1 aGarcia-Garcia, F.1 aConesa, A.1 aMontaner, D.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1850880602500nas a2200253 4500008004100000245008800041210006900129300000700198490001400205520139900219653007701618653005701695653019901752653003901951653002701990100002402017700001402041700002402055700001802079700001502097700001502112700001302127856010602140 2007 eng d00aThe AnnoLite and AnnoLyze programs for comparative annotation of protein structures0 aAnnoLite and AnnoLyze programs for comparative annotation of pro aS40 v8 Suppl 43 aBACKGROUND: Advances in structural biology, including structural genomics, have resulted in a rapid increase in the number of experimentally determined protein structures. However, about half of the structures deposited by the structural genomics consortia have little or no information about their biological function. Therefore, there is a need for tools for automatically and comprehensively annotating the function of protein structures. We aim to provide such tools by applying comparative protein structure annotation that relies on detectable relationships between protein structures to transfer functional annotations. Here we introduce two programs, AnnoLite and AnnoLyze, which use the structural alignments deposited in the DBAli database. DESCRIPTION: AnnoLite predicts the SCOP, CATH, EC, InterPro, PfamA, and GO terms with an average sensitivity of 90% and average precision of 80%. AnnoLyze predicts ligand binding site and domain interaction patches with an average sensitivity of 70% and average precision of 30%, correctly localizing binding sites for small molecules in 95% of its predictions. CONCLUSION: The AnnoLite and AnnoLyze programs for comparative annotation of protein structures can reliably and automatically annotate new protein structures. The programs are fully accessible via the Internet as part of the DBAli suite of tools at http://salilab.org/DBAli/.10a*Algorithms Amino Acid Sequence Confidence Intervals Data Interpretation10aAmino Acid *Software Structure-Activity Relationship10aProtein Information Storage and Retrieval/methods Molecular Sequence Data Proteins/*chemistry/classification/*metabolism Sensitivity and Specificity Sequence Alignment/*methods Sequence Analysis10aProtein/*methods Sequence Homology10aStatistical *Databases1 aMarti-Renom, M., A.1 aRossi, A.1 aAl-Shahrour, Fatima1 aDavis, F., P.1 aPieper, U.1 aDopazo, J.1 aSali, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1757014702151nas a2200277 4500008004100000245005200041210005100093300001100144490000700155520104000162653008701202653005701289653019401346653003901540653002701579100002401606700001501630700002401645700001401669700001401683700001801697700002401715700001501739700001301754856010601767 2007 eng d00aDBAli tools: mining the protein structure space0 aDBAli tools mining the protein structure space aW393-70 v353 aThe DBAli tools use a comprehensive set of structural alignments in the DBAli database to leverage the structural information deposited in the Protein Data Bank (PDB). These tools include (i) the DBAlit program that allows users to input the 3D coordinates of a protein structure for comparison by MAMMOTH against all chains in the PDB; (ii) the AnnoLite and AnnoLyze programs that annotate a target structure based on its stored relationships to other structures; (iii) the ModClus program that clusters structures by sequence and structure similarities; (iv) the ModDom program that identifies domains as recurrent structural fragments and (v) an implementation of the COMPARER method in the SALIGN command in MODELLER that creates a multiple structure alignment for a set of related protein structures. Thus, the DBAli tools, which are freely accessible via the World Wide Web at http://salilab.org/DBAli/, allow users to mine the protein structure space by establishing relationships between protein structures and their functions.10a*Algorithms Amino Acid Sequence Computational Biology/*methods Data Interpretation10aAmino Acid *Software Structure-Activity Relationship10aProtein Internet Molecular Sequence Data Protein Conformation Proteins/*chemistry/classification/*metabolism Pseudomonas aeruginosa/*metabolism Sequence Alignment/*methods Sequence Analysis10aProtein/*methods Sequence Homology10aStatistical *Databases1 aMarti-Renom, M., A.1 aPieper, U.1 aMadhusudhan, M., S.1 aRossi, A.1 aEswar, N.1 aDavis, F., P.1 aAl-Shahrour, Fatima1 aDopazo, J.1 aSali, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1747851302784nas a2200301 4500008004100000245006900041210006800110300000800178490000600186520165900192653002501851653002201876653001901898653006101917653007001978653003402048653013602082100001802218700002102236700001702257700002402274700001402298700001402312700001602326700001502342700001902357856010602376 2007 eng d00aEvidence for systems-level molecular mechanisms of tumorigenesis0 aEvidence for systemslevel molecular mechanisms of tumorigenesis a1850 v83 aBACKGROUND: Cancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth. RESULTS: Here, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as "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. CONCLUSION: Given the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.10a*Cell Transformation10aBiological Models10aGenetic Models10aMessenger/metabolism Signal Transduction Systems Biology10aNeoplastic *Gene Expression Profiling *Gene Expression Regulation10aNeoplastic Humans Male Models10aStatistical Neoplasm Proteins/*physiology Neoplasms/etiology/*genetics Prostatic Neoplasms/genetics Protein Interaction Mapping RNA1 aHernandez, P.1 aHuerta-Cepas, J.1 aMontaner, D.1 aAl-Shahrour, Fatima1 aValls, J.1 aGomez, L.1 aCapella, G.1 aDopazo, J.1 aPujana, M., A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1758491502196nas a2200217 4500008004100000245016500041210006900206300001000275490000700285520140700292653001501699653003501714100002401749700001601773700001601789700002001805700001501825700001701840700001501857856010601872 2007 eng d00aFatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments0 aFatiGO a functional profiling tool for genomic data Integration  aW91-60 v353 a

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.

10ababelomics10afunctional enrichment analysys1 aAl-Shahrour, Fatima1 aMinguez, P.1 aTarraga, J.1 aMedina, Ignacio1 aAlloza, E.1 aMontaner, D.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1747850402367nas a2200229 4500008004100000245007200041210006900113300000800182490000600190520145600196653010501652653001501757653013601772100002401908700001501932700001501947700002101962700001601983700001701999700001502016856010602031 2007 eng d00aFrom genes to functional classes in the study of biological systems0 aFrom genes to functional classes in the study of biological syst a1140 v83 a

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.

10aAlgorithms Chromosome Mapping/*methods Computer Simulation Gene Expression Profiling/methods *Models10ababelomics10aBiological Multigene Family/*physiology Signal Transduction/*physiology *Software Systems Biology/*methods *User-Computer Interface1 aAl-Shahrour, Fatima1 aArbiza, L.1 aDopazo, H.1 aHuerta-Cepas, J.1 aMinguez, P.1 aMontaner, D.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1740759600431nas a2200109 4500008004100000245005200041210005200093260004900145100001500194700002400209856008800233 2007 eng d00aFunctional annotation of microarray experiments0 aFunctional annotation of microarray experiments aNew York, USAbTaylor & Francis, F. Falciani1 aDopazo, J.1 aAl-Shahrour, Fatima uhttps://www.clinbioinfosspa.es/content/functional-annotation-microarray-experiments01696nas a2200193 4500008004100000245009300041210006900134300001000203490000600213520105200219653001501271100001401286700001701300700002401317700001601341700002401357700001501381856010601396 2007 eng d00aFunctional profiling and gene expression analysis of chromosomal copy number alterations0 aFunctional profiling and gene expression analysis of chromosomal a432-50 v13 a

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.

10ababelomics1 aConde, L.1 aMontaner, D.1 aBurguet-Castell, J.1 aTarraga, J.1 aAl-Shahrour, Fatima1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1759793501608nas a2200193 4500008004100000245008900041210006900130300001100199490000700210520077100217653003900988653001501027653019401042100001601236700002401252700001701276700001501293856010601308 2007 eng d00aFunctional profiling of microarray experiments using text-mining derived bioentities0 aFunctional profiling of microarray experiments using textmining  a3098-90 v233 a

MOTIVATION: 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.

10aArtificial Intelligence *Databases10ababelomics10aProtein Gene Expression Profiling/*methods Information Storage and Retrieval/*methods *Natural Language Processing Proteins/*classification/*metabolism Research/*methods Systems Integration1 aMinguez, P.1 aAl-Shahrour, Fatima1 aMontaner, D.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1785541501792nas a2200217 4500008004100000245012200041210006900163300001000232490000700242520078800249653013601037653016501173100001401338700001701352700002401369700001601393700002001409700002401429700001501453856010601468 2007 eng d00aISACGH: a web-based environment for the analysis of Array CGH and gene expression which includes functional profiling0 aISACGH a webbased environment for the analysis of Array CGH and  aW81-50 v353 aWe 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.10aAnimals Cluster Analysis Computational Biology/*methods Computer Graphics Gene Expression Profiling/*methods Humans Internet Models10aGenetic *Nucleic Acid Hybridization Oligonucleotide Array Sequence Analysis/*methods Programming Languages *Software Systems Integration User-Computer Interface1 aConde, L.1 aMontaner, D.1 aBurguet-Castell, J.1 aTarraga, J.1 aMedina, Ignacio1 aAl-Shahrour, Fatima1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1746849900491nas a2200157 4500008004100000022002200041245005800063210005800121260002100179300001200200490000700212100001700219700002400236700001500260856005800275 2007 eng d a978-3-540-71991-500aNew Trends in the Analysis of Functional Genomic Data0 aNew Trends in the Analysis of Functional Genomic Data aBerlinbSpringer a576-5800 v121 aMontaner, D.1 aAl-Shahrour, Fatima1 aDopazo, J. uhttp://www.springerlink.com/content/m62p07r8111004vr/01445nas a2200253 4500008004100000245010300041210006900144300001100213490000700224520068700231653001500918653002500933100002400958700001600982700001600998700001701014700001501031700002301046700001401069700001701083700001301100700001501113856006301128 2006 eng d00aBABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments0 aBABELOMICS a systems biology perspective in the functional annot aW472-60 v343 a

We present a new version of Babelomics, a complete suite of web tools for functional analysis of genome-scale experiments, with new and improved tools. New functionally relevant terms have been included such as CisRed motifs or bioentities obtained by text-mining procedures. An improved indexing has considerably speeded up several of the modules. An improved version of the FatiScan method for studying the coordinate behaviour of groups of functionally related genes is presented, along with a similar tool, the Gene Set Enrichment Analysis. Babelomics is now more oriented to test systems biology inspired hypotheses. Babelomics can be found at http://www.babelomics.org.

10ababelomics10afunctional profiling1 aAl-Shahrour, Fatima1 aMinguez, P.1 aTarraga, J.1 aMontaner, D.1 aAlloza, E.1 aVaquerizas, J., M.1 aConde, L.1 aBlaschke, C.1 aVera, J.1 aDopazo, J. uhttp://nar.oxfordjournals.org/content/34/suppl_2/W472.long02404nas a2200157 4500008004100000245009100041210006900132300001000201490000700211520184000218653001502058100001602073700002402089700001502113856011802128 2006 eng d00aA function-centric approach to the biological interpretation of microarray time-series0 afunctioncentric approach to the biological interpretation of mic a57-660 v173 a

The interpretation of microarray experiments is commonly addressed by means a two-step approach in which the relevant genes are firstly selected uniquely on the basis of their experimental values (ignoring their coordinate behaviors) and in a second step their functional properties are studied to hypothesize about the biological roles they are fulfilling in the cell. Recently, different methods (e.g. GSEA or FatiScan) have been proposed to study the coordinate behavior of blocks of functionally-related genes. These methods study the distribution of functional information across lists of genes ranked according their different experimental values in a static situation, such as the comparison between two classes (e.g. healthy controls versus diseased cases). Nevertheless there is no an equivalent way of studying a dynamic situation from a functional point of view. We present a method for the functional analysis of microarrays series in which the experiments display autocorrelation between successive points (e.g. time series, dose-response experiments, etc.) The method allows to recover the dynamics of the molecular roles fulfilled by the genes along the series which provides a novel approach to functional interpretation of such experiments. The method finds blocks of functionally-related genes which are significantly and coordinately over-expressed at different points of the series. This method draws inspiration from systems biology given that the analysis does not focus on individual properties of genes but on collective behaving blocks of functionally-related genes. The FatiScan algorithm used in the method proposed is available at: http://fatiscan.bioinfo.cipf.es, or within the Babelomics suite: http://www.babelomics.org. Additional material is available at: http://bioinfo.cipf.es/data/plasmodium.

10ababelomics1 aMinguez, P.1 aAl-Shahrour, Fatima1 aDopazo, J. uhttps://www.clinbioinfosspa.es/content/function-centric-approach-biological-interpretation-microarray-time-series01882nas a2200313 4500008004100000245005200041210005100093300001200144490000700156520100300163653001001166653002901176100001701205700001601222700002101238700001601259700002301275700001401298700001601312700001301328700001801341700001401359700001901373700001501392700001601407700002401423700001501447856010601462 2006 eng d00aNext station in microarray data analysis: GEPAS0 aNext station in microarray data analysis GEPAS aW486-910 v343 a

The Gene Expression Profile Analysis Suite (GEPAS) has been running for more than four years. During this time it has evolved to keep pace with the new interests and trends in the still changing world of microarray data analysis. GEPAS has been designed to provide an intuitive although powerful web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-colour microarray experiments and other preprocessing options), to the final step of the functional annotation of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), and include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management. GEPAS is extensively used by researchers of many countries and its records indicate an average usage rate of 400 experiments per day. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.

10agepas10amicroarray data analysis1 aMontaner, D.1 aTarraga, J.1 aHuerta-Cepas, J.1 aBurguet, J.1 aVaquerizas, J., M.1 aConde, L.1 aMinguez, P.1 aVera, J.1 aMukherjee, S.1 aValls, J.1 aPujana, M., A.1 aAlloza, E.1 aHerrero, J.1 aAl-Shahrour, Fatima1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1684505601646nas a2200217 4500008004100000245007200041210006900113300001000182490000800192520083200200653001501032653002101047653007301068653002601141653004201167653005901209100001501268700002401283700001501307856010601322 2006 eng d00aOntology-driven approaches to analyzing data in functional genomics0 aOntologydriven approaches to analyzing data in functional genomi a67-860 v3163 a

Ontologies are fundamental knowledge representations that provide not only standards for annotating and indexing biological information, but also the basis for implementing functional classification and interpretation models. This chapter discusses the application of gene ontology (GO) for predictive tasks in functional genomics. It focuses on the problem of analyzing functional patterns associated with gene products. This chapter is divided into two main parts. The first part overviews GO and its applications for the development of functional classification models. The second part presents two methods for the characterization of genomic information using GO. It discusses methods for measuring functional similarity of gene products, and a tool for supporting gene expression clustering analysis and validation.

10ababelomics10aCluster Analysis10aCluster Analysis Computational Biology/*methods *Data Interpretation10aComputational Biology10aStatistical Gene Expression Profiling10aStatistical Gene Expression Profiling *Genomics Humans1 aAzuaje, F.1 aAl-Shahrour, Fatima1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1667140102090nas a2200193 4500008004100000245012600041210006900167300001100236490000700247520144700254653001501701653002501716100002401741700001601765700002301781700001401804700001501818856006301833 2005 eng d00aBABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments0 aBABELOMICS a suite of web tools for functional annotation and an aW460-40 v333 a

We present Babelomics, a complete suite of web tools for the functional analysis of groups of genes in high-throughput experiments, which includes the use of information on Gene Ontology terms, interpro motifs, KEGG pathways, Swiss-Prot keywords, analysis of predicted transcription factor binding sites, chromosomal positions and presence in tissues with determined histological characteristics, through five integrated modules: FatiGO (fast assignment and transference of information), FatiWise, transcription factor association test, GenomeGO and tissues mining tool, respectively. Additionally, another module, FatiScan, provides a new procedure that integrates biological information in combination with experimental results in order to find groups of genes with modest but coordinate significant differential behaviour. FatiScan is highly sensitive and is capable of finding significant asymmetries in the distribution of genes of common function across a list of ordered genes even if these asymmetries were not extreme. The strong multiple-testing nature of the contrasts made by the tools is taken into account. All the tools are integrated in the gene expression analysis package GEPAS. Babelomics is the natural evolution of our tool FatiGO (which analysed almost 22,000 experiments during the last year) to include more sources on information and new modes of using it. Babelomics can be found at http://www.babelomics.org.

10ababelomics10afunctional profiling1 aAl-Shahrour, Fatima1 aMinguez, P.1 aVaquerizas, J., M.1 aConde, L.1 aDopazo, J. uhttp://nar.oxfordjournals.org/content/33/suppl_2/W460.long02842nas a2200205 4500008004100000245013300041210006900174300001200243490000700255520183200262653001502094653013902109653003602248653010202284653008402386100002402470700002102494700001502515856010602530 2005 eng d00aDiscovering molecular functions significantly related to phenotypes by combining gene expression data and biological information0 aDiscovering molecular functions significantly related to phenoty a2988-930 v213 a

MOTIVATION: The analysis of genome-scale data from different high throughput techniques can be used to obtain lists of genes ordered according to their different behaviours under distinct experimental conditions corresponding to different phenotypes (e.g. differential gene expression between diseased samples and controls, different response to a drug, etc.). The order in which the genes appear in the list is a consequence of the biological roles that the genes play within the cell, which account, at molecular scale, for the macroscopic differences observed between the phenotypes studied. Typically, two steps are followed for understanding the biological processes that differentiate phenotypes at molecular level: first, genes with significant differential expression are selected on the basis of their experimental values and subsequently, the functional properties of these genes are analysed. Instead, we present a simple procedure which combines experimental measurements with available biological information in a way that genes are simultaneously tested in groups related by common functional properties. The method proposed constitutes a very sensitive tool for selecting genes with significant differential behaviour in the experimental conditions tested. RESULTS: We propose the use of a method to scan ordered lists of genes. The method allows the understanding of the biological processes operating at molecular level behind the macroscopic experiment from which the list was generated. This procedure can be useful in situations where it is not possible to obtain statistically significant differences based on the experimental measurements (e.g. low prevalence diseases, etc.). Two examples demonstrate its application in two microarray experiments and the type of information that can be extracted.

10ababelomics10aBiological Neoplasm Proteins/genetics/*metabolism Phenotype Software Structure-Activity Relationship Systems Integration Tumor Markers10aBiological/genetics/*metabolism10aBreast Neoplasms/genetics/*metabolism Computer Simulation *Database Management Systems *Databases10aProtein Documentation/methods Gene Expression Profiling/*methods Humans *Models1 aAl-Shahrour, Fatima1 aDiaz-Uriarte, R.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1584070202188nas a2200241 4500008004100000245009500041210006900136300001200205490000700217520141200224653001001636653002901646100002301675700001401698700002001712700001601732700001601748700002101764700002401785700001601809700001501825856010601840 2005 eng d00aGEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data0 aGEPAS an experimentoriented pipeline for the analysis of microar aW616-200 v333 a

The Gene Expression Profile Analysis Suite, GEPAS, has been running for more than three years. With >76,000 experiments analysed during the last year and a daily average of almost 300 analyses, GEPAS can be considered a well-established and widely used platform for gene expression microarray data analysis. GEPAS is oriented to the analysis of whole series of experiments. Its design and development have been driven by the demands of the biomedical community, probably the most active collective in the field of microarray users. Although clustering methods have obviously been implemented in GEPAS, our interest has focused more on methods for finding genes differentially expressed among distinct classes of experiments or correlated to diverse clinical outcomes, as well as on building predictors. There is also a great interest in CGH-arrays which fostered the development of the corresponding tool in GEPAS: InSilicoCGH. Much effort has been invested in GEPAS for developing and implementing efficient methods for functional annotation of experiments in the proper statistical framework. Thus, the popular FatiGO has expanded to a suite of programs for functional annotation of experiments, including information on transcription factor binding sites, chromosomal location and tissues. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.

10agepas10amicroarray data analysis1 aVaquerizas, J., M.1 aConde, L.1 aYankilevich, P.1 aCabezon, A.1 aMinguez, P.1 aDiaz-Uriarte, R.1 aAl-Shahrour, Fatima1 aHerrero, J.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1598054800404nas a2200121 4500008004100000245003900041210003900080260003500119300001100154100002400165700001500189856007800204 2005 eng d00aOntologies and functional genomics0 aOntologies and functional genomics bWiley, F. Azuaje and J. Dopazo a99-1021 aAl-Shahrour, Fatima1 aDopazo, J. uhttps://www.clinbioinfosspa.es/content/ontologies-and-functional-genomics02177nas a2200313 4500008004100000245013500041210006900176260001500245300001200260490000700272520116700279100003001446700002301476700001901499700002001518700002701538700002701565700002101592700001901613700001601632700002101648700002401669700002401693700002001717700002201737700001801759700002001777856006601797 2004 eng d00aExpression profiling of T-cell lymphomas differentiates peripheral and lymphoblastic lymphomas and defines survival related genes.0 aExpression profiling of Tcell lymphomas differentiates periphera c2004 Aug 1 a4971-820 v103 a

PURPOSE: T-Cell lymphomas constitute heterogeneous and aggressive tumors in which pathogenic alterations remain largely unknown. Expression profiling has demonstrated to be a useful tool for molecular classification of tumors. EXPERIMENTAL DESIGN: Using DNA microarrays (CNIO-OncoChip) containing 6386 cancer-related genes, we established the expression profiling of T-cell lymphomas and compared them to normal lymphocytes and lymph nodes. RESULTS: We found significant differences between the peripheral and lymphoblastic T-cell lymphomas, which include a deregulation of nuclear factor-kappaB signaling pathway. We also identify differentially expressed genes between peripheral T-cell lymphoma tumors and normal T lymphocytes or reactive lymph nodes, which could represent candidate tumor markers of these lymphomas. Additionally, a close relationship between genes associated to survival and those that differentiate among the stages of disease and responses to therapy was found. CONCLUSIONS: Our results reflect the value of gene expression profiling to gain insight about the molecular alterations involved in the pathogenesis of T-cell lymphomas.

1 aMartinez-Delgado, Beatriz1 aMeléndez, Barbara1 aCuadros, Marta1 aAlvarez, Javier1 aCastrillo, Jose, Maria1 aDe La Parte, Ana, Ruiz1 aMollejo, Manuela1 aBellas, Carmen1 aDiaz, Ramon1 aLombardía, Luis1 aAl-Shahrour, Fatima1 aDomínguez, Orlando1 aCascon, Alberto1 aRobledo, Mercedes1 aRivas, Carmen1 aBenitez, Javier uhttp://clincancerres.aacrjournals.org/content/10/15/4971.long01450nas a2200193 4500008004100000245010400041210006900145300001100214490000700225520063300232653005000865653001500915653002700930653016800957100002401125700002101149700001501170856007101185 2004 eng d00aFatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes0 aFatiGO a web tool for finding significant associations of Gene O a578-800 v203 a

We present a simple but powerful procedure to extract Gene Ontology (GO) terms that are significantly over- or under-represented in sets of genes within the context of a genome-scale experiment (DNA microarray, proteomics, etc.). Said procedure has been implemented as a web application, FatiGO, allowing for easy and interactive querying. FatiGO, which takes the multiple-testing nature of statistical contrast into account, currently includes GO associations for diverse organisms (human, mouse, fly, worm and yeast) and the TrEMBL/Swissprot GOAnnotations@EBI correspondences from the European Bioinformatics Institute.

10a*Algorithms Artificial Intelligence Databases10ababelomics10aDNA/*methods *Software10aGenetic Gene Expression Profiling/*methods *Hypermedia Information Storage and Retrieval/*methods *Internet *Phylogeny Sequence Alignment/methods Sequence Analysis1 aAl-Shahrour, Fatima1 aDiaz-Uriarte, R.1 aDopazo, J. uhttp://bioinformatics.oxfordjournals.org/content/20/4/578.abstract02102nas a2200217 4500008004100000245007500041210006900116300001200185490000700197520140300204653001001607653002901617100001601646700002301662700002401685700001401709700001501723700002501738700001501763856010601778 2004 eng d00aNew challenges in gene expression data analysis and the extended GEPAS0 aNew challenges in gene expression data analysis and the extended aW485-910 v323 a

Since the first papers published in the late nineties, including, for the first time, a comprehensive analysis of microarray data, the number of questions that have been addressed through this technique have both increased and diversified. Initially, interest focussed on genes coexpressing across sets of experimental conditions, implying, essentially, the use of clustering techniques. Recently, however, interest has focussed more on finding genes differentially expressed among distinct classes of experiments, or correlated to diverse clinical outcomes, as well as in building predictors. In addition to this, the availability of accurate genomic data and the recent implementation of CGH arrays has made mapping expression and genomic data on the chromosomes possible. There is also a clear demand for methods that allow the automatic transfer of biological information to the results of microarray experiments. Different initiatives, such as the Gene Ontology (GO) consortium, pathways databases, protein functional motifs, etc., provide curated annotations for genes. Whereas many resources on the web focus mainly on clustering methods, GEPAS has evolved to cope with the aforementioned new challenges that have recently arisen in the field of microarray data analysis. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://gepas.bioinfo.cnio.es.

10agepas10amicroarray data analysis1 aHerrero, J.1 aVaquerizas, J., M.1 aAl-Shahrour, Fatima1 aConde, L.1 aMateos, A.1 aDiaz-Uriarte, J., S.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1521543402164nas a2200229 4500008004100000245009400041210006900135300001100204490000700215520128900222653008201511653001201593653009301605100001401698700002301712700001601735700002401751700002201775700001601797700001501813856010601828 2004 eng d00aPupaSNP Finder: a web tool for finding SNPs with putative effect at transcriptional level0 aPupaSNP Finder a web tool for finding SNPs with putative effect  aW242-80 v323 aWe have developed a web tool, PupaSNP Finder (PupaSNP for short), for high-throughput searching for single nucleotide polymorphisms (SNPs) with potential phenotypic effect. PupaSNP takes as its input lists of genes (or generates them from chromosomal coordinates) and retrieves SNPs that could affect the conserved regions that the cellular machinery uses for the correct processing of genes (intron/exon boundaries or exonic splicing enhancers), predicted transcription factor binding sites (TFBS) and changes in amino acids in the proteins. The program uses the mapping of SNPs in the genome provided by Ensembl. Additionally, user-defined SNPs (not yet mapped in the genome) can be easily provided to the program. Also, additional functional information from Gene Ontology, OMIM and homologies in other model organisms is provided. In contrast to other programs already available, which focus only on SNPs with possible effect in the protein, PupaSNP includes SNPs with possible transcriptional effect. PupaSNP will be of significant help in studies of multifactorial disorders, where the use of functional SNPs will increase the sensitivity of identification of the genes responsible for the disease. The PupaSNP web interface is accessible through http://pupasnp.bioinfo.cnio.es.10aAmino Acid Substitution Binding Sites Humans Internet Phenotype *Polymorphism10aGenetic10aSingle Nucleotide RNA Splicing *Software Transcription Factors/metabolism *Transcription1 aConde, L.1 aVaquerizas, J., M.1 aSantoyo, J.1 aAl-Shahrour, Fatima1 aRuiz-Llorente, S.1 aRobledo, M.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1521538801500nas a2200217 4500008004100000245007700041210006900118300001100187490000700198520080200205653001001007653002901017100001601046700002401062700002101086700001501107700002301122700001601145700001501161856010601176 2003 eng d00aGEPAS: A web-based resource for microarray gene expression data analysis0 aGEPAS A webbased resource for microarray gene expression data an a3461-70 v313 a

We present a web-based pipeline for microarray gene expression profile analysis, GEPAS, which stands for Gene Expression Profile Analysis Suite (http://gepas.bioinfo.cnio.es). GEPAS is composed of different interconnected modules which include tools for data pre-processing, two-conditions comparison, unsupervised and supervised clustering (which include some of the most popular methods as well as home made algorithms) and several tests for differential gene expression among different classes, continuous variables or survival analysis. A multiple purpose tool for data mining, based on Gene Ontology, is also linked to the tools, which constitutes a very convenient way of analysing clustering results. On-line tutorials are available from our main web server (http://bioinfo.cnio.es).

10agepas10amicroarray data analysis1 aHerrero, J.1 aAl-Shahrour, Fatima1 aDiaz-Uriarte, R.1 aMateos, A.1 aVaquerizas, J., M.1 aSantoyo, J.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1282434500611nas a2200133 4500008004100000245011000041210006900151260004900220300001200269100002100281700002400302700001500326856013600341 2003 eng d00aUse of GO Terms to Understand the Biological Significance of Microarray Differential Gene Expression Data0 aUse of GO Terms to Understand the Biological Significance of Mic bKluwer Academic, K. F. Johnson and S. M. Lin a233-2471 aDíaz-Uriarte, R1 aAl-Shahrour, Fatima1 aDopazo, J. uhttps://www.clinbioinfosspa.es/content/use-go-terms-understand-biological-significance-microarray-differential-gene-expression-data00720nas a2200181 4500008004100000245013000041210006900171260003000240300001000270653001500280100002400295700001600319700001500335700001600350700002100366700001500387856013600402 2003 eng d00aUsing Gene Ontology on genome-scale studies to find significant associations of biologically relevant terms to group of genes0 aUsing Gene Ontology on genomescale studies to find significant a aNew York, USAbIEEE Press a43-5210ababelomics1 aAl-Shahrour, Fatima1 aHerrero, J.1 aMateos, A.1 aSantoyo, J.1 aDíaz-Uriarte, R1 aDopazo, J. uhttps://www.clinbioinfosspa.es/content/using-gene-ontology-genome-scale-studies-find-significant-associations-biologically-relevant