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Nucleic Acids Research 2005 33(Web Server Issue):W616-W620; doi:10.1093/nar/gki500
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© The Author 2005. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oupjournals.org

Article

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

Juan M. Vaquerizas1, Lucía Conde1, Patricio Yankilevich1, Amaya Cabezón1, Pablo Minguez1, Ramón Díaz-Uriarte1, Fátima Al-Shahrour1, Javier Herrero1,2 and Joaquín Dopazo1,3,*

1Bioinformatics Unit, Centro Nacional de Investigaciones Oncológicas (CNIO) Melchor Fernández Almagro 3, 28029 Madrid, Spain 2Ensembl Team, EMBL-EBI Hinxton, Cambridge, UK 3Functional Genomics Node, INB, Centro de Investigación Príncipe Felipe Autopista del Saler 16, 46013 Valencia, Spain

*To whom correspondence should be addressed. Tel: +34 96 328 96 80; Fax: +34 96 328 97 01; Email: jdopazo{at}ochoa.fib.es

Received February 14, 2005. Revised April 9, 2005. Accepted May 3, 2005.

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.

Present address: Juan M. Vaquerizas, Lucía Conde, Pablo Minguez, Fátima Al-Shahrour and Joaquín Dopazo, Bioinformatics Department, Centro de Investigación Príncipe Felipe, Autopista del Saler 16, 46013, Valencia, Spain


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