title: NestedMICA: sensitive inference of over-represented motifs in nucleic acid sequence.
abstract: NestedMICA is a new, scalable, pattern-discovery system for finding transcription factor binding sites and similar motifs in biological sequences. Like several previous methods, NestedMICA tackles this problem by optimizing a probabilistic mixture model to fit a set of sequences. However, the use of a newly developed inference strategy called Nested Sampling means NestedMICA is able to find optimal solutions without the need for a problematic initialization or seeding step. We investigate the performance of NestedMICA in a range scenario, on synthetic data and a well-characterized set of muscle regulatory regions, and compare it with the popular MEME program. We show that the new method is significantly more sensitive than MEME: in one case, it successfully extracted a target motif from background sequence four times longer than could be handled by the existing program. It also performs robustly on synthetic sequences containing multiple significant motifs. When tested on a real set of regulatory sequences, NestedMICA produced motifs which were good predictors for all five abundant classes of annotated binding sites.
authors.affiliation: Wellcome Trust Sanger Institute, Hinxton Cambridge, CB10 1SA, UK. td2@sanger.ac.uk
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Down
authors.forename: Tim J P
authors.initials: TJ
authors.lastname: Hubbard
mesh_terms: D001483:Base Sequence; D001499:Bayes Theorem; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008390:Markov Chains; D018006:Myogenic Regulatory Factors / Q000378:metabolism; D012045:Regulatory Sequences, Nucleic Acid; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D018006:Myogenic Regulatory Factors; D014157:Transcription Factors