title: info-gibbs: a motif discovery algorithm that directly optimizes information content during sampling.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  http://rsat.ulb.ac.be/rsat/info-gibbs
authors.affiliation: Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles CP 263, Campus Plaine, Boulevard du Triomphe, B-1050 Bruxelles, Belgium. defrance@bigre.ulb.ac.be
authors.forename: Matthieu
authors.initials: M
authors.lastname: Defrance
authors.forename: Jacques
authors.initials: J
authors.lastname: van Helden
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D019277:Entropy; D023281:Genomics; D016013:Likelihood Functions; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D014157:Transcription Factors / Q000737:chemistry
chemical_list: D014157:Transcription Factors