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

Matthieu Defrance | Zendy; Jacques van Helden | Zendy

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

Author(s) -

Matthieu Defrance,

Jacques van Helden

Publication year - 2009

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btp490

Subject(s) - gibbs sampling , motif (music) , computer science , estimator , algorithm , a priori and a posteriori , computation , data mining , computational biology , biology , mathematics , artificial intelligence , statistics , physics , bayesian probability , philosophy , epistemology , acoustics

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.

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