02205nas 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=1850880602221nas a2200157 4500008004100000245005800041210005600099300000800155490000600163520157700169653010501746653007601851100001501927700001501942856010601957 2005 eng d00aGenome-scale evidence of the nematode-arthropod clade0 aGenomescale evidence of the nematodearthropod clade aR410 v63 aBACKGROUND: The issue of whether coelomates form a single clade, the Coelomata, or whether all animals that moult an exoskeleton (such as the coelomate arthropods and the pseudocoelomate nematodes) form a distinct clade, the Ecdysozoa, is the most puzzling issue in animal systematics and a major open-ended subject in evolutionary biology. Previous single-gene and genome-scale analyses designed to resolve the issue have produced contradictory results. Here we present the first genome-scale phylogenetic evidence that strongly supports the Ecdysozoa hypothesis. RESULTS: Through the most extensive phylogenetic analysis carried out to date, the complete genomes of 11 eukaryotic species have been analyzed in order to find homologous sequences derived from 18 human chromosomes. Phylogenetic analysis of datasets showing an increased adjustment to equal evolutionary rates between nematode and arthropod sequences produced a gradual change from support for Coelomata to support for Ecdysozoa. Transition between topologies occurred when fast-evolving sequences of Caenorhabditis elegans were removed. When chordate, nematode and arthropod sequences were constrained to fit equal evolutionary rates, the Ecdysozoa topology was statistically accepted whereas Coelomata was rejected. CONCLUSIONS: The reliability of a monophyletic group clustering arthropods and nematodes was unequivocally accepted in datasets where traces of the long-branch attraction effect were removed. This is the first phylogenomic evidence to strongly support the ’moulting clade’ hypothesis.10aAnimals Arthropods/*classification/genetics Caenorhabditis elegans/classification/genetics Evolution10aMolecular *Genome Genomics Nematoda/*classification/genetics *Phylogeny1 aDopazo, H.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1589286902188nas 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=1598054802990nas a2200325 4500008004100000245013600041210006900177300001100246490000700257520176100264653001602025653002602041653001002067653003402077653003402111653011302145653008802258100001702346700002102363700001602384700002502400700002702425700001502452700002102467700001402488700001502502700002502517700001602542856010602558 2004 eng d00aGene expression analysis of chromosomal regions with gain or loss of genetic material detected by comparative genomic hybridization0 aGene expression analysis of chromosomal regions with gain or los a353-650 v413 aComparative genomic hybridization (CGH) has been widely used to detect copy number alterations in cancer and to identify regions containing candidate tumor-responsible genes; however, gene expression changes have been described only in highly amplified regions (amplicons). To study the overall impact of slight copy number changes on gene expression, we analyzed 16 T-cell lymphomas by using CGH and a custom-designed cDNA microarray containing 7,657 genes and expressed sequence tags related to tumorigenesis. We evaluated mean gene expression and variability within CGH-altered regions and explored the relationship between the effects of the gene and its position within these regions. Minimally overlapping CGH candidate areas (6q25, 13q21-q22, and 19q13.1) revealed a weak relationship between altered genomic content and gene expression. However, some candidate genes showed modified expression within these regions in the majority of tumors; these candidate genes were evaluated and confirmed in another independent series of 23 T-cell lymphomas by use of the same cDNA microarray and by FISH on a tissue microarray. When all the CGH regions detected for each tumor were considered, we found a significant increase or decrease in the mean expression of the genes contained in gained or lost regions, respectively. In addition, we found that the expression of a gene was dependent not only on its position within an altered region but also on its own mechanism of regulation: genes in the same altered region responded very differently to the gain or loss of genetic material. Supplementary material for this article can be found on the Genes, Chromosomes, and Cancer website at http://www.interscience.wiley.com/jpages/1045-2257/suppmat/index.html.10aChromosomes10aFluorescence Lymphoma10aHuman10aPair 13/*genetics Chromosomes10aPair 19/*genetics Chromosomes10aPair 6/*genetics Expressed Sequence Tags *Gene Dosage Gene Expression Profiling Humans In Situ Hybridization10aT-Cell/*genetics Nucleic Acid Hybridization Oligonucleotide Array Sequence Analysis1 aMelendez, B.1 aDiaz-Uriarte, R.1 aCuadros, M.1 aMartinez-Ramirez, A.1 aFernandez-Piqueras, J.1 aDopazo, A.1 aCigudosa, J., C.1 aRivas, C.1 aDopazo, J.1 aMartinez-Delgado, B.1 aBenitez, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1538226100556nas a2200133 4500008004100000245011100041210006900152300001000221100001200231700001500243700001900258700001500277856013000292 2004 eng d00aGene expression Correlation and Gene Ontology-Based Similarity: An Assessment of Quantitative Relationship0 aGene expression Correlation and Gene OntologyBased Similarity An a25-311 aWang, H1 aAzuaje, F.1 aBodenreider, O1 aDopazo, J. uhttps://www.clinbioinfosspa.es/content/gene-expression-correlation-and-gene-ontology-based-similarity-assessment-quantitative01226nas a2200169 4500008004100000245003900041210003900080300001000119490000700129520049600136653021400632653005200846100001600898700002100914700001500935856010600950 2003 eng d00aGene expression data preprocessing0 aGene expression data preprocessing a655-60 v193 aWe present an interactive web tool for preprocessing microarray gene expression data. It analyses the data, suggests the most appropriate transformations and proceeds with them after user agreement. The normal preprocessing steps include scale transformations, management of missing values, replicate handling, flat pattern filtering and pattern standardization and they are required before performing any pattern analysis. The processed data set can be sent to other pattern analysis tools.10a*Database Management Systems Gene Expression Profiling/*methods Information Storage and Retrieval/methods Internet Oligonucleotide Array Sequence Analysis/*methods Sequence Alignment/*methods Sequence Analysis10aDNA/*methods *Software *User-Computer Interface1 aHerrero, J.1 aDiaz-Uriarte, R.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1265172601500nas 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=12824345