title: PairMotifChIP: A Fast Algorithm for Discovery of Patterns Conserved in Large ChIP-seq Data Sets.
abstract: Identifying conserved patterns in DNA sequences, namely, motif discovery, is an important and challenging computational task. With hundreds or more sequences contained, the high-throughput sequencing data set is helpful to improve the identification accuracy of motif discovery but requires an even higher computing performance. To efficiently identify motifs in large DNA data sets, a new algorithm called PairMotifChIP is proposed by extracting and combining pairs ofl-mers in the input with relatively small Hamming distance. In particular, a method for rapidly extracting pairs ofl-mers is designed, which can be used not only for PairMotifChIP, but also for other DNA data mining tasks with the same demand. Experimental results on the simulated data show that the proposed algorithm can find motifs successfully and runs faster than the state-of-the-art motif discovery algorithms. Furthermore, the validity of the proposed algorithm has been verified on real data.
authors.affiliation: School of Computer Science and Technology, Xidian University, Xi'an 710071, China.
authors.forename: Qiang
authors.identifier: 0000-0002-8725-6687
authors.initials: Q
authors.lastname: Yu
authors.affiliation: School of Computer Science and Technology, Xidian University, Xi'an 710071, China.
authors.forename: Hongwei
authors.initials: H
authors.lastname: Huo
authors.affiliation: School of Electronic Engineering, Xidian University, Xi'an 710071, China.
authors.forename: Dazheng
authors.initials: D
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D047369:Chromatin Immunoprecipitation; D003198:Computer Simulation; D030541:Databases, Genetic; D051379:Mice; D000066450:Mouse Embryonic Stem Cells / Q000378:metabolism; D059372:Nucleotide Motifs / Q000235:genetics*; D011336:Probability; D017422:Sequence Analysis, DNA; D013997:Time Factors