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Bioinformatics Advance Access published online on January 29, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth006
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received August 28, 2003
Revised October 31, 2003
Accepted November 3, 2003

Article

Modeling within-motif dependence for transcription factor binding site predictions

Qing Zhou 1 Jim S. Liu 1*

1 Department of Statistics, Harvard University, 1 Oxford ST, Cambridge, MA 02138, USA

* To whom correspondence should be addressed. E-mail: jliu{at}stat.harvard.edu.


  Abstract

Motivation: The position-specific weight matrix (PWM) model, which assumes that each position in the DNA site contributes independently to the overall protein-DNA interaction, has been the primary means to describe transcription factor binding site motifs. Recent biological experiments, however, suggest that there exists interdependence among positions in the binding sites. In order to exploit this interdependence to aid motif discovery, we extend the PWM model to include pairs of correlated positions and design a Markov chain Monte Carlo algorithm to sample in the model space. We then combine the model sampling step with the Gibbs sampling framework for de novo motif discoveries.

Results: Testing on experimentally validated binding sites, we find that about 25% of the transcription factor binding motifs show significant within-site position correlations, and 80% of these motif models can be improved by considering the correlated positions. Using both simulated data and real promoter sequences, we show that the new de novo motif-finding algorithm can infer the true correlated position pairs accurately and is more precise in finding putative TF binding sites than the standard Gibbs sampling algorithms.

Availability: The program is available at http://www.people.fas.harvard.edu/~junliu/


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