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 |
| Tools |
