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BioMed Research International
Volume 2015 (2015), Article ID 853461, 10 pages
http://dx.doi.org/10.1155/2015/853461
Research Article

An Affinity Propagation-Based DNA Motif Discovery Algorithm

School of Computer Science and Technology, Xidian University, Xi’an 710071, China

Received 3 January 2015; Revised 10 June 2015; Accepted 11 June 2015

Academic Editor: Graziano Pesole

Copyright © 2015 Chunxiao Sun et al. 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.

Abstract

The planted motif search (PMS) is one of the fundamental problems in bioinformatics, which plays an important role in locating transcription factor binding sites (TFBSs) in DNA sequences. Nowadays, identifying weak motifs and reducing the effect of local optimum are still important but challenging tasks for motif discovery. To solve the tasks, we propose a new algorithm, APMotif, which first applies the Affinity Propagation (AP) clustering in DNA sequences to produce informative and good candidate motifs and then employs Expectation Maximization (EM) refinement to obtain the optimal motifs from the candidate motifs. Experimental results both on simulated data sets and real biological data sets show that APMotif usually outperforms four other widely used algorithms in terms of high prediction accuracy.