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Bioinformatics Advance Access originally published online on May 19, 2005
Bioinformatics 2005 21(14):3082-3088; doi:10.1093/bioinformatics/bti477
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

A multiple-feature framework for modelling and predicting transcription factor binding sites

Rainer Pudimat , Ernst-Günter Schukat-Talamazzini and Rolf Backofen *

Institut für Informatik, Friedrich-Schiller-Universität Ernst-Abbe-Platz 3, D-07743 Jena, Germany

*To whom correspondence should be addressed.

Motivation: The identification of transcription factor binding sites in promoter sequences is an important problem, since it reveals information about the transcriptional regulation of genes. For analysing transcriptional regulation, computational approaches for predicting putative binding sites are applied. Commonly used stochastic models for binding sites are position-specific score matrices, which show weak predictive power.

Results: We have developed a probabilistic modelling approach, which allows to consider diverse characteristic binding site properties to obtain more accurate representations of binding sites. These properties are modelled as random variables in Bayesian networks, which are capable of dealing with dependencies among binding site properties. Cross-validation on several datasets shows improvements in the false positive error rate and the significance (P-value) of true binding sites.

Supplementary information: A more extensive description of validation results are available at http://www.bio.inf.uni-jena.de/Software/promapper/

Contact: backofen{at}inf.uni-jena.de

Received on January 11, 2005; revised on April 5, 2005; accepted on April 27, 2005

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