02842nas 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=1598054802881nas a2200397 4500008004100000245016800041210006900209300001200278490000700290520143600297653010101733653002201834653001001856653008301866653003301949653003301982653003302015653003202048653005102080100001602131700002102147700001502168700001602183700001602199700001602215700001602231700001802247700001602265700001402281700001302295700002102308700001502329700001702344700001602361856010602377 2005 eng d00aA predictor based on the somatic genomic changes of the BRCA1/BRCA2 breast cancer tumors identifies the non-BRCA1/BRCA2 tumors with BRCA1 promoter hypermethylation0 apredictor based on the somatic genomic changes of the BRCA1BRCA2 a1146-530 v113 aThe genetic changes underlying in the development and progression of familial breast cancer are poorly understood. To identify a somatic genetic signature of tumor progression for each familial group, BRCA1, BRCA2, and non-BRCA1/BRCA2 (BRCAX) tumors, by high-resolution comparative genomic hybridization, we have analyzed 77 tumors previously characterized for BRCA1 and BRCA2 germ line mutations. Based on a combination of the somatic genetic changes observed at the six most different chromosomal regions and the status of the estrogen receptor, we developed using random forests a molecular classifier, which assigns to a given tumor a probability to belong either to the BRCA1 or to the BRCA2 class. Because 76.5% (26 of 34) of the BRCAX cases were classified with our predictor to the BRCA1 class with a probability of >50%, we analyzed the BRCA1 promoter region for aberrant methylation in all the BRCAX cases. We found that 15 of the 34 BRCAX analyzed tumors had hypermethylation of the BRCA1 gene. When we considered the predictor, we observed that all the cases with this epigenetic event were assigned to the BRCA1 class with a probability of >50%. Interestingly, 84.6% of the cases (11 of 13) assigned to the BRCA1 class with a probability >80% had an aberrant methylation of the BRCA1 promoter. This fact suggests that somatic BRCA1 inactivation could modify the profile of tumor progression in most of the BRCAX cases.10aBRCA1 Protein/*genetics BRCA2 Protein/*genetics Breast Neoplasms/*genetics/pathology Chromosomes10aGenetic/*genetics10aHuman10aHuman Humans Male Mutation Nucleic Acid Hybridization/methods Promoter Regions10aPair 12/genetics Chromosomes10aPair 15/genetics Chromosomes10aPair 18/genetics Chromosomes10aPair 2/genetics Chromosomes10aPair 8/genetics *DNA Methylation Female Genome1 aAlvarez, S.1 aDiaz-Uriarte, R.1 aOsorio, A.1 aBarroso, A.1 aMelchor, L.1 aPaz, M., F.1 aHonrado, E.1 aRodriguez, R.1 aUrioste, M.1 aValle, L.1 aDiez, O.1 aCigudosa, J., C.1 aDopazo, J.1 aEsteller, M.1 aBenitez, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1570918201390nas a2200169 4500008004100000245006900041210006700110300001100177490000700188520056300195653024500758653005201003100002301055700001501078700002101093856010601114 2004 eng d00aDNMAD: web-based diagnosis and normalization for microarray data0 aDNMAD webbased diagnosis and normalization for microarray data a3656-80 v203 aSUMMARY: We present a web server for Diagnosis and Normalization of MicroArray Data (DNMAD). DNMAD includes several common data transformations such as spatial and global robust local regression or multiple slide normalization, and allows for detecting several kinds of errors that result from the manipulation and the image analysis of the arrays. This tool offers a user-friendly interface, and is completely integrated within the Gene Expression Pattern Analysis Suite (GEPAS). AVAILABILITY: The tool is accessible on-line at http://dnmad.bioinfo.cnio.es.10aAlgorithms Database Management Systems Gene Expression Profiling/*methods/standards Information Storage and Retrieval/*methods *Internet Oligonucleotide Array Sequence Analysis/*methods/standards Sequence Alignment/methods Sequence Analysis10aDNA/*methods *Software *User-Computer Interface1 aVaquerizas, J., M.1 aDopazo, J.1 aDiaz-Uriarte, R. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1524709401450nas 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.abstract02990nas 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=1538226101309nas 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=1862909701226nas 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