Journal Article

info-gibbs: a motif discovery algorithm that directly optimizes information content during sampling

Matthieu Defrance and Jacques van Helden

in Bioinformatics

Volume 25, issue 20, pages 2715-2722
Published in print October 2009 | ISSN: 1367-4803
Published online August 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp490
info-gibbs: a motif discovery algorithm that directly optimizes information content during sampling

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Motivation: Discovering cis-regulatory elements in genome sequence remains a challenging issue. Several methods rely on the optimization of some target scoring function. The information content (IC) or relative entropy of the motif has proven to be a good estimator of transcription factor DNA binding affinity. However, these information-based metrics are usually used as a posteriori statistics rather than during the motif search process itself.

Results: We introduce here info-gibbs, a Gibbs sampling algorithm that efficiently optimizes the IC or the log-likelihood ratio (LLR) of the motif while keeping computation time low. The method compares well with existing methods like MEME, BioProspector, Gibbs or GAME on both synthetic and biological datasets. Our study shows that motif discovery techniques can be enhanced by directly focusing the search on the motif IC or the motif LLR.

Availability: http://rsat.ulb.ac.be/rsat/info-gibbs

Contact: defrance@bigre.ulb.ac.be

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6431 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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