GEPAS, a web-based tool for microarray data analysis and interpretation.

TitleGEPAS, a web-based tool for microarray data analysis and interpretation.
Publication TypeJournal Article
Year of Publication2008
AuthorsTárraga, J, Medina, I, Carbonell, J, Huerta-Cepas, J, Minguez, P, Alloza, E, Al-Shahrour, F, Vegas-Azcárate, S, Goetz, S, Escobar, P, Garcia-Garcia, F, Conesa, A, Montaner, D, Dopazo, J
JournalNucleic Acids Res
Volume36
IssueWeb Server issue
PaginationW308-14
Date Published2008 Jul 01
ISSN1362-4962
KeywordsComputer Graphics; Dose-Response Relationship, Drug; Gene Expression Profiling; Internet; Kinetics; Oligonucleotide Array Sequence Analysis; Software
Abstract

Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org.

DOI10.1093/nar/gkn303
Alternate JournalNucleic Acids Res.
PubMed ID18508806
PubMed Central IDPMC2447723