Open Access Software

TXTGate: profiling gene groups with text-based information

Patrick Glenisson1, Bert Coessens1*, Steven Van Vooren1, Janick Mathys1, Yves Moreau1,2 and Bart De Moor1

Author Affiliations

1 Departement Elektrotechniek (ESAT), Faculteit Toegepaste Wetenschappen, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Heverlee (Leuven), Belgium

2 Current address: Center for Biological Sequence Analysis, BioCentrum, Danish Technical University, Kemitorvet, DK-2800 Lyngby, Denmark

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Genome Biology 2004, 5:R43 doi:10.1186/gb-2004-5-6-r43

Published: 28 May 2004

Abstract

We implemented a framework called TXTGate that combines literature indices of selected public biological resources in a flexible text-mining system designed towards the analysis of groups of genes. By means of tailored vocabularies, term- as well as gene-centric views are offered on selected textual fields and MEDLINE abstracts used in LocusLink and the Saccharomyces Genome Database. Subclustering and links to external resources allow for in-depth analysis of the resulting term profiles.