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BioMed Research International
Volume 2015 (2015), Article ID 218068, 10 pages
Research Article

A Fast Cluster Motif Finding Algorithm for ChIP-Seq Data Sets

Department of Automation, School of Electronics and Control Engineering, Chang’An University, Xi’an 710064, China

Received 8 April 2015; Accepted 4 June 2015

Academic Editor: Andre Van Wijnen

Copyright © 2015 Yipu Zhang and Ping Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


New high-throughput technique ChIP-seq, coupling chromatin immunoprecipitation experiment with high-throughput sequencing technologies, has extended the identification of binding locations of a transcription factor to the genome-wide regions. However, the most existing motif discovery algorithms are time-consuming and limited to identify binding motifs in ChIP-seq data which normally has the significant characteristics of large scale data. In order to improve the efficiency, we propose a fast cluster motif finding algorithm, named as FCmotif, to identify the motifs in large scale ChIP-seq data set. It is inspired by the emerging substrings mining strategy to find the enriched substrings and then searching the neighborhood instances to construct PWM and cluster motifs in different length. FCmotif is not following the OOPS model constraint and can find long motifs. The effectiveness of proposed algorithm has been proved by experiments on the ChIP-seq data sets from mouse ES cells. The whole detection of the real binding motifs and processing of the full size data of several megabytes finished in a few minutes. The experimental results show that FCmotif has advantageous to deal with the motif finding in the ChIP-seq data; meanwhile it also demonstrates better performance than other current widely-used algorithms such as MEME, Weeder, ChIPMunk, and DREME.