01882nas a2200313   4500008004100000245005200041210005100093300001200144490000700156520100300163653001001166653002901176100001701205700001601222700002101238700001601259700002301275700001401298700001601312700001301328700001801341700001401359700001901373700001501392700001601407700002401423700001501447856010601462       2006                        eng d00aNext station in microarray data analysis: GEPAS0 aNext station in microarray data analysis GEPAS  aW486-910 v343 a<p>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.</p>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=1684505602188nas 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<p>The Gene Expression Profile Analysis Suite, GEPAS, has been running for more than three years. With &gt;76,000 experiments analysed during the last year and a daily average of almost 300 analyses, GEPAS can be considered a well-established and widely used platform for gene expression microarray data analysis. GEPAS is oriented to the analysis of whole series of experiments. Its design and development have been driven by the demands of the biomedical community, probably the most active collective in the field of microarray users. Although clustering methods have obviously been implemented in GEPAS, our interest has focused more on methods for finding genes differentially expressed among distinct classes of experiments or correlated to diverse clinical outcomes, as well as on building predictors. There is also a great interest in CGH-arrays which fostered the development of the corresponding tool in GEPAS: InSilicoCGH. Much effort has been invested in GEPAS for developing and implementing efficient methods for functional annotation of experiments in the proper statistical framework. Thus, the popular FatiGO has expanded to a suite of programs for functional annotation of experiments, including information on transcription factor binding sites, chromosomal location and tissues. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.</p>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=1598054800738nas a2200229   4500008004100000245005000041210004900091260010900140300001200249490000600261100001400267700001300281700001400294700001400308700001900322700001300341700001500354700001600369700002200385700001300407856008800420       2005                        eng d00aSalinibacter ruber: genomics and biogeography0 aSalinibacter ruber genomics and biogeography  aDordrecht, NetherlandsbNina Gunde-Cimerman, Ana Plemenitas, and Aharon Oren. Kluwer Academic Publishers  a257-2660 v91 aAntón, J1 aPeña, A1 aValens, M1 aSantos, F1 aGlöckner, F.O1 aBauer, M1 aDopazo, J.1 aHerrero, J.1 aRosselló-Mora, R1 aAmann, R  uhttps://www.clinbioinfosspa.es/content/salinibacter-ruber-genomics-and-biogeography02102nas 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<p>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.</p>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=1521543401309nas a2200145   4500008004100000245010100041210006900142300001100211490000600222520077700228100001601005700002101021700001501042856010601057       2003                        eng d00aAn approach to inferring transcriptional regulation among genes from large-scale expression data0 aapproach to inferring transcriptional regulation among genes fro  a148-540 v43 aThe use of DNA microarrays opens up the possibility of measuring the expression levels of thousands of genes simultaneously under different conditions. Time-course experiments allow researchers to study the dynamics of gene interactions. The inference of genetic networks from such measures can give important insights for the understanding of a variety of biological problems. Most of the existing methods for genetic network reconstruction require many experimental data points, or can only be applied to the reconstruction of small subnetworks. Here we present a method that reduces the dimensionality of the dataset and then extracts the significant dynamic correlations among genes. The method requires a number of points achievable in common time-course experiments.1 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=1862909702356nas a2200265   4500008004100000245006200041210006200103300001000165490000700175520124200182653003001424653007301454653007501527653004901602653004201651653004301693653005001736653007501786653005801861100001901919700001601938700001501954700001501969856010601984       2003                        eng d00aComparing bacterial genomes through conservation profiles0 aComparing bacterial genomes through conservation profiles  a991-80 v133 aWe constructed two-dimensional representations of profiles of gene conservation across different genomes using the genome of Escherichia coli as a model. These profiles permit both the visualization at the genome level of different traits in the organism studied and, at the same time, reveal features related to the genomes analyzed (such as defective genomes or genomes that lack a particular system). Conserved genes are not uniformly distributed along the E. coli genome but tend to cluster together. The study of gene distribution patterns across genomes is important for the understanding of how sets of genes seem to be dependent on each other, probably having some functional link. This provides additional evidence that can be used for the elucidation of the function of unannotated genes. Clustering these patterns produces families of genes which can be arranged in a hierarchy of closeness. In this way, functions can be defined at different levels of generality depending on the level of the hierarchy that is studied. The combined study of conservation and phenotypic traits opens up the possibility of defining phenotype/genotype associations, and ultimately inferring the gene or genes responsible for a particular trait.10aBacterial Genotype Models10aBacterial/genetics Cluster Analysis Conserved Sequence/*genetics DNA10aBacterial/genetics Escherichia coli/classification/*genetics Evolution10aBacterial/genetics Gene Order/genetics Genes10aBacterial/genetics/physiology *Genome10aChromosome Mapping/methods Chromosomes10aGenetic Phenotype Phylogeny Sequence Homology10aMolecular Gene Expression Profiling/methods Gene Expression Regulation10aNucleic Acid Species Specificity Terminology as Topic1 aMartin, M., J.1 aHerrero, J.1 aMateos, A.1 aDopazo, J.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1269532401226nas 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<p>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).</p>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=1282434500720nas 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-relevant01648nas a2200241   4500008004100000245010900041210006900150300001100219490000700230520055300237653010300790653018900893653008001082100001601162700001401178700001601192700001501208700001701223700002201240700002101262700001701283856010601300       2002                        eng d00aBioinformatics methods for the analysis of expression arrays: data clustering and information extraction0 aBioinformatics methods for the analysis of expression arrays dat  a269-830 v983 aExpression arrays facilitate the monitoring of changes in the expression patterns of large collections of genes. The analysis of expression array data has become a computationally-intensive task that requires the development of bioinformatics technology for a number of key stages in the process, such as image analysis, database storage, gene clustering and information extraction. Here, we review the current trends in each of these areas, with particular emphasis on the development of the related technology being carried out within our groups.10aAbstracting and Indexing as Topic/methods *Cluster Analysis *Database Management Systems Databases10aComputer-Assisted/methods Information Storage and Retrieval/*methods Internet Medline National Library of Medicine (U.S.) Oligonucleotide Array Sequence Analysis/*methods United States10aGenetic Gene Expression Gene Expression Profiling/*methods Image Processing1 aTamames, J.1 aClark, D.1 aHerrero, J.1 aDopazo, J.1 aBlaschke, C.1 aFernandez, J., M.1 aOliveros, J., C.1 aValencia, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1214199201191nas a2200157   4500008004100000245011600041210006900157300001100226490000600237520046000243653007400703653011900777100001600896700001500912856010600927       2002                        eng d00aCombining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns0 aCombining hierarchical clustering and selforganizing maps for ex  a467-700 v13 aSelf-organizing maps (SOM) constitute an alternative to classical clustering methods because of its linear run times and superior performance to deal with noisy data. Nevertheless, the clustering obtained with SOM is dependent on the relative sizes of the clusters. Here, we show how the combination of SOM with hierarchical clustering methods constitutes an excellent tool for exploratory analysis of massive data like DNA microarray expression patterns.10aCluster Analysis Computational Biology/methods *Gene Expression Genes10aFungal/genetics *Genome Oligonucleotide Array Sequence Analysis/*methods Statistics as Topic/*methods Time Factors1 aHerrero, J.1 aDopazo, J.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1264591903136nas a2200397   4500008004100000245010000041210006900141300001200210490000800222520146400230653015401694653005901848653001301907653008901920653015002009653006702159653003702226653008702263653001102350100001502361700001902376700001402395700001502409700001602424700001802440700003002458700002602488700001802514700001402532700001802546700001602564700001902580700001502599700001802614856010602632       2002                        eng d00aIdentification of genes involved in resistance to interferon-alpha in cutaneous T-cell lymphoma0 aIdentification of genes involved in resistance to interferonalph  a1825-370 v1613 aInterferon-alpha therapy has been shown to be active in the treatment of mycosis fungoides although the individual response to this therapy is unpredictable and dependent on essentially unknown factors. In an effort to better understand the molecular mechanisms of interferon-alpha resistance we have developed an interferon-alpha resistant variant from a sensitive cutaneous T-cell lymphoma cell line. We have performed expression analysis to detect genes differentially expressed between both variants using a cDNA microarray including 6386 cancer-implicated genes. The experiments showed that resistance to interferon-alpha is consistently associated with changes in the expression of a set of 39 genes, involved in signal transduction, apoptosis, transcription regulation, and cell growth. Additional studies performed confirm that STAT1 and STAT3 expression and interferon-alpha induction and activation are not altered between both variants. The gene MAL, highly overexpressed by resistant cells, was also found to be expressed by tumoral cells in a series of cutaneous T-cell lymphoma patients treated with interferon-alpha and/or photochemotherapy. MAL expression was associated with longer time to complete remission. Time-course experiments of the sensitive and resistant cells showed a differential expression of a subset of genes involved in interferon-response (1 to 4 hours), cell growth and apoptosis (24 to 48 hours.), and signal transduction.10aAntineoplastic Agents/*pharmacology/therapeutic use Carrier Proteins/biosynthesis/genetics DNA-Binding Proteins/biosynthesis/genetics Drug Resistance10aBiological Oligonucleotide Array Sequence Analysis RNA10aCultured10aCutaneous/diagnosis/drug therapy/*genetics/metabolism *Membrane Glycoproteins Models10aInterleukin-1 Reproducibility of Results STAT1 Transcription Factor STAT3 Transcription Factor Trans-Activators/biosynthesis/genetics Tumor Cells10aNeoplasm Gene Expression Profiling *Gene Expression Regulation10aNeoplasm/biosynthesis *Receptors10aNeoplastic Humans Interferon-alpha/*pharmacology/therapeutic use Kinetics Lymphoma10aT-Cell1 aTracey, L.1 aVilluendas, R.1 aOrtiz, P.1 aDopazo, A.1 aSpiteri, I.1 aLombardia, L.1 aRodriguez-Peralto, J., L.1 aFernandez-Herrera, J.1 aHernandez, A.1 aFraga, J.1 aDominguez, O.1 aHerrero, J.1 aAlonso, M., A.1 aDopazo, J.1 aPiris, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1241452900612nas a2200145   4500008004100000245013500041210006900176260002000245300001100265100001500276700001600291700001600307700001500323856012800338       2002                        eng d00aSupervised Neural Networks For Clustering Conditions In DNA Array Data After Reducing Noise By Clustering Gene Expression Profiles0 aSupervised Neural Networks For Clustering Conditions In DNA Arra  bKluwer Academic  a91-1031 aMateos, A.1 aHerrero, J.1 aTamames, J.1 aDopazo, J.  uhttps://www.clinbioinfosspa.es/content/supervised-neural-networks-clustering-conditions-dna-array-data-after-reducing-noise00641nas a2200133   4500008004100000245018000041210006900221260003200290100001400322700001500336700001600351700001500367856012500382       2002                        eng d00aUnsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data0 aUnsupervised reduction of the dimensionality followed by supervi  aMartigny, SwitzerlandbIEEE1 aConde, L.1 aMateos, A.1 aHerrero, J.1 aDopazo, J.  uhttp://ieeexplore.ieee.org/document/1030019/http://xplorestaging.ieee.org/ielx5/8007/22134/01030019.pdf?arnumber=103001900621nas a2200133   4500008004100000245016500041210006900206260002000275300001200295100001500307700001600322700001500338856013400353       2002                        eng d00aUsing perceptrons for supervised classification of DNA microarray samples: obtaining the optimal level of information and finding differentially expressed genes0 aUsing perceptrons for supervised classification of DNA microarra  bJ.R. Dorronsoro  a577-5821 aMateos, A.1 aHerrero, J.1 aDopazo, J.  uhttps://www.clinbioinfosspa.es/content/using-perceptrons-supervised-classification-dna-microarray-samples-obtaining-optimal-level02311nas a2200361   4500008004100000245009500041210006900136300001100205490000600216520111600222653009801338653003901436653003101475653000801506653005901514100001501573700001601588700001601604700001601620700001601636700001601652700001701668700002101685700001501706700002101721700001601742700001401758700001401772700001501786700001601801700002601817856010601843       2001                        eng d00aAnnotated draft genomic sequence from a Streptococcus pneumoniae type 19F clinical isolate0 aAnnotated draft genomic sequence from a Streptococcus pneumoniae  a99-1250 v73 aThe public availability of numerous microbial genomes is enabling the analysis of bacterial biology in great detail and with an unprecedented, organism-wide and taxon-wide, broad scope. Streptococcus pneumoniae is one of the most important bacterial pathogens throughout the world. We present here sequences and functional annotations for 2.1-Mbp of pneumococcal DNA, covering more than 90% of the total estimated size of the genome. The sequenced strain is a clinical isolate resistant to macrolides and tetracycline. It carries a type 19F capsular locus, but multilocus sequence typing for several conserved genetic loci suggests that the strain sequenced belongs to a pneumococcal lineage that most often expresses a serotype 15 capsular polysaccharide. A total of 2,046 putative open reading frames (ORFs) longer than 100 amino acids were identified (average of 1,009 bp per ORF), including all described two-component systems and aminoacyl tRNA synthetases. Comparisons to other complete, or nearly complete, bacterial genomes were made and are presented in a graphical form for all the predicted proteins.10aBacterial Molecular Sequence Data Pneumococcal Infections/*microbiology Prokaryotic Cells RNA10aBacterial/chemistry/genetics Genes10aBacterial/genetics *Genome10aDNA10aTransfer/metabolism Streptococcus pneumoniae/*genetics1 aDopazo, J.1 aMendoza, A.1 aHerrero, J.1 aCaldara, F.1 aHumbert, Y.1 aFriedli, L.1 aGuerrier, M.1 aGrand-Schenk, E.1 aGandin, C.1 ade Francesco, M.1 aPolissi, A.1 aBuell, G.1 aFeger, G.1 aGarcia, E.1 aPeitsch, M.1 aGarcia-Bustos, J., F.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1144234802638nas a2200157   4500008004100000245009500041210006900136300001100205490000700216520196500223653013802188100001602326700001702342700001502359856010602374       2001                        eng d00aA hierarchical unsupervised growing neural network for clustering gene expression patterns0 ahierarchical unsupervised growing neural network for clustering   a126-360 v173 aMOTIVATION: We describe a new approach to the analysis of gene expression data coming from DNA array experiments, using an unsupervised neural network. DNA array technologies allow monitoring thousands of genes rapidly and efficiently. One of the interests of these studies is the search for correlated gene expression patterns, and this is usually achieved by clustering them. The Self-Organising Tree Algorithm, (SOTA) (Dopazo,J. and Carazo,J.M. (1997) J. Mol. Evol., 44, 226-233), is a neural network that grows adopting the topology of a binary tree. The result of the algorithm is a hierarchical cluster obtained with the accuracy and robustness of a neural network. RESULTS: SOTA clustering confers several advantages over classical hierarchical clustering methods. SOTA is a divisive method: the clustering process is performed from top to bottom, i.e. the highest hierarchical levels are resolved before going to the details of the lowest levels. The growing can be stopped at the desired hierarchical level. Moreover, a criterion to stop the growing of the tree, based on the approximate distribution of probability obtained by randomisation of the original data set, is provided. By means of this criterion, a statistical support for the definition of clusters is proposed. In addition, obtaining average gene expression patterns is a built-in feature of the algorithm. Different neurons defining the different hierarchical levels represent the averages of the gene expression patterns contained in the clusters. Since SOTA runtimes are approximately linear with the number of items to be classified, it is especially suitable for dealing with huge amounts of data. The method proposed is very general and applies to any data providing that they can be coded as a series of numbers and that a computable measure of similarity between data items can be used. AVAILABILITY: A server running the program can be found at: http://bioinfo.cnio.es/sotarray.10a*Algorithms Automatic Data Processing *Gene Expression Profiling *Neural Networks (Computer) *Oligonucleotide Array Sequence Analysis1 aHerrero, J.1 aValencia, A.1 aDopazo, J.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11238068