title: Discovering motifs with transcription factor domain knowledge.
abstract: We introduce a new motif-discovery algorithm, DIMDom, which exploits two additional kinds of information not commonly exploited: (a) the characteristic pattern of binding site classes, where class is determined based on biological information about transcription factor domains and (b) posterior probabilities of these classes. We compared the performance of DIMDom with MEME on all the transcription factors of Drosophila with at least one known binding site in the TRANSFAC database and found that DOMDom outperformed MEME with 2.5 times the number of successes and 1.5 times in the accuracy in finding binding sties and motifs.
authors.affiliation: Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong, China.
authors.forename: Henry C M
authors.initials: HC
authors.lastname: Leung
authors.forename: Francis Y L
authors.initials: FY
authors.lastname: Chin
authors.forename: Bethany M Y
authors.initials: BM
authors.lastname: Chan
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology; D004247:DNA / Q000235:genetics / Q000378:metabolism; D004330:Drosophila / Q000235:genetics / Q000378:metabolism; D008954:Models, Biological; D011401:Promoter Regions, Genetic; D017434:Protein Structure, Tertiary; D014157:Transcription Factors / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D014157:Transcription Factors; D004247:DNA