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Bioinformatics Advance Access published online on November 17, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm543
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A Profile-Based Deterministic Sequential Monte Carlo Algorithm for Motif Discovery

Kuo-ching Liang , Xiaodong Wang and Dimitris Anastassiou

Columbia University, Department of Electrical Engineering, New York, NY 10025, USA

To whom correspondence should be addressed. Prof. Xiaodong Wang, E-mail: xw2008{at}columbia.edu


  Abstract

Motivation: Conserved motifs often represent biological significance, providing insight on biological aspects such as gene transcription regulation, biomolecular secondary structure, presence of noncoding RNAs and evolution history. With the increasing number of sequenced genomic data, faster and more accurate tools are needed to automate the process of motif discovery.

Results: We propose a deterministic sequential Monte Carlo (DSMC) motif discovery technique based on the position weight matrix (PWM) model to locate conserved motifs in a given set of nucleotide sequences, and extend our model to search for instances of the motif with insertions/deletions. We show that the proposed method can be used to align the motif where there are insertions and deletions found in different instances of the motif, which can not be satisfactorily done using other multiple alignment and motif discovery algorithms.

Availability: MATLAB code is available at http://www.ee.columbia.edu/~kcliang

Contact: wangx{at}ee.columbia.edu

Associate Editor: Prof. Martin Bishop

Received on April 16, 2007; revised on October 11, 2007; accepted on October 27, 2007

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