title: PairMotif: A new pattern-driven algorithm for planted (l, d) DNA motif search.
abstract: Motif search is a fundamental problem in bioinformatics with an important application in locating transcription factor binding sites (TFBSs) in DNA sequences. The exact algorithms can report all (l, d) motifs and find the best one under a specific objective function. However, it is still a challenging task to identify weak motifs, since either a large amount of memory or execution time is required by current exact algorithms. A new exact algorithm, PairMotif, is proposed for planted (l, d) motif search (PMS) in this paper. To effectively reduce both candidate motifs and scanned l-mers, multiple pairs of l-mers with relatively large distances are selected from input sequences to restrict the search space. Comparisons with several recently proposed algorithms show that PairMotif requires less storage space and runs faster on most PMS instances. Particularly, among the algorithms compared, only PairMotif can solve the weak instance (27, 9) within 10 hours. Moreover, the performance of PairMotif is stable over the sequence length, which allows it to identify motifs in longer sequences. For the real biological data, experimental results demonstrate the validity of the proposed algorithm.
authors.affiliation: School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Yu
authors.forename: Hongwei
authors.initials: H
authors.lastname: Huo
authors.forename: Yipu
authors.initials: Y
authors.lastname: Zhang
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry*; D059372:Nucleotide Motifs; D010363:Pattern Recognition, Automated; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors; D004247:DNA