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Bioinformatics Advance Access originally published online on March 25, 2007
Bioinformatics 2007 23(11):1313-1320; doi:10.1093/bioinformatics/btm054
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A sequential Monte Carlo EM approach to the transcription factor binding site identification problem

Edmund S. Jackson * and William J. Fitzgerald

Signal Processing Laboratory, Department of Engineering, Cambridge University, UK

*To whom correspondence should be addressed.


  Abstract

Motivation: A significant and stubbornly intractable problem in genome sequence analysis has been the de novo identification of transcription factor binding sites in promoter regions. Although theoretically pleasing, probabilistic methods have faced difficulties due to model mismatch and the nature of the biological sequence. These problems result in inference in a high dimensional, highly multimodal space, and consequently often display only local convergence and hence unsatisfactory performance.

Algorithm: In this article, we derive and demonstrate a novel method utilizing a sequential Monte Carlo-based expectation-maximization (EM) optimization to improve performance in this scenario. The Monte Carlo element should increase the robustness of the algorithm compared to classical EM. Furthermore, the parallel nature of the sequential Monte Carlo algorithm should be more robust than Gibbs sampling approaches to multimodality problems.

Results: We demonstrate the superior performance of this algorithm on both semi-synthetic and real data from Escherichia coli.

Availability: http://sigproc-eng.cam.ac.uk/~ej230/smc_em_tfbsid.tar.gz

Contact: ej230{at}cam.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Alex Bateman

Received on October 9, 2006; revised on January 21, 2007; accepted on February 9, 2007

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