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Bioinformatics Advance Access published online on May 19, 2005
Bioinformatics, 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@oupjournals.org
Received January 11, 2005
Revised April 5, 2005
Accepted April 27, 2005

Article

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

Rainer Pudimat 1, Ernst-Günter Schukat-Talamazzini 1, and Rolf Backofen 1*

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

* To whom correspondence should be addressed.
Rolf Backofen, E-mail: backofen{at}inf.uni-jena.de


  Abstract

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 (PSSM), 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 to deal with dependencies among binding site properties. Cross validation on several data sets shows improvements in the false positive error rate and the significance (p-value) of true binding sites.

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


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