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Detalle de: GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data

Title GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data
Abstract

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.

Apa Reference Vaquerizas, J. M., Conde, L., Yankilevich, P., Cabezon, A., Minguez, P., Diaz-Uriarte, R., Al-Shahrour, F., Herrero, J., & Dopazo, J. (2005). GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data. Nucleic Acids Research, 33(Web Server), W616–W620. 10.1093/nar/gki500
Doi 10.1093/nar/gki500
Publication Date 1 Jul 2005
Publication Type Article
Researcher Ramon Díaz Uriarte
Ontology Transcriptomics, Functional annotation, Expression Profiling, Regulatory biology, Statistical Methods for Biology, Molecular Interactions/Docking, Clinical data
Research Groups Bioinformatics and Computational Biology in Cancer Evolution
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