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Bioinformatics Advance Access published online on March 29, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti410
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received September 22, 2004
Revised March 13, 2005
Accepted March 23, 2005

Article

Identification of transcription factor binding sites with variable-order Bayesian networks

I. Ben-Gal 1*, A. Shani 1, A. Gohr 2, J. Grau 2, S. Arviv 1, A. Shmilovici 3, S. Posch 4, and I. Grosse 5

1 Department of Industrial Engineering, Tel-Aviv University, Tel-Aviv, 69978, Israel
2 Institute of Computer Science, University Halle, 06099 Halle (Saale), Germany; Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany
3 Department of Information Systems Engineering, Ben-Gurion University P.O.Box 653, Beer-Sheva, 84105, Israel
4 Institute of Computer Science, University Halle, 06099 Halle (Saale), Germany
5 Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany

* To whom correspondence should be addressed.
I. Ben-Gal, E-mail: bengal{at}eng.tau.ac.il


  Abstract

Motivation: We propose a new class of variable order Bayesian network (VOBN) models for the identification of transcription factor binding sites (TFBSs). The proposed models generalize the widely-used position weight matrix (PWM) models, Markov models and Bayesian network (BN) models. In contrast to these models, where for each position a fixed subset of the remaining positions is used to model dependencies, in VOBN models these subsets may vary based on the specific nucleotides observed, which are called the context.

This flexibility turns out to be of advantage for the classification and analysis of TFBSs, as statistical dependencies between nucleotides in different TFBS positions (not necessarily adjacent) may be taken into account efficiently - in a position-specific and context-specific manner.

Results: We apply the VOBN model to a set of 238 experimentally verified sigma-70 binding sites in E.coli. We find that the VOBN model can distinguish those 238 sites from a set of 472 intergenic ‘non-promoter’ sequences with higher accuracy than fixed-order Markov models or Bayesian trees (BT). We use a replicated stratified-holdout experiments having a fixed true-negative rate of 99.9%. We find that for a foreground inhomogeneous VOBN model of order 1 and a background homogeneous variable-order Markov (VOM) model of order 5 the obtained mean true-positive (TP) rate is 47.56%. In comparison, the best TP rate for the conventional models is 44.39%, obtained from a foreground PWM model and a background 2nd-order Markov model. As the standard deviation of the estimated TP rate is ~ 0.01%, this improvement is highly significant.

Availability: All datasets are available upon request from the authors at bengal@eng.tau.ac.il. A web server for utilizing VOBN and VOM models is available at http://www.eng.tau.ac.il/~bengal/.


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