HMMF: An Hidden Markov Model Based Approach for Motif Finding
Transcriptional Factor Binding Site (TFBS) motifs on DNA genomes play important functional roles in gene expression and regulation. Accurately identifying the motifs is thus an important problem in bioinformatics. However, exhaustively enumerating all possible locations for a motif in a set of sequences is computationally intractable. Many heuristic or approximation algorithms and machine learning based approaches have been developed for this problem. In this paper, we develop a novel approach that can efficiently explore all possible locations of TFBS motifs in a set of sequences with high accuracy. Our approach constructs an ensemble of k Hidden Markov Models (HMM) through local alignments of two sequences in the set and then progressively aligns each HMM in the ensemble to other sequences in the set and update the parameters of the k HMMs. Our experimental results showed that our approach could achieve higher accuracy with satisfying efficiency than previous state-of-art approaches.
Hidden Markov Model (HMM) Motif finding Transcription factor binding site
Chunmei Liu Yinglei Song Moses Garuba Legand Burge
Dept.of Systems and Computer Science Howard University Washington,DC 20059,USA Dept.of Mathematics and Computer Science University of Maryland Eastern Shore Princess Anne,MD 21853
国际会议
北京
英文
1-3
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）