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Nucleic Acids Research Advance Access published online on September 28, 2009
Nucleic Acids Research, doi:10.1093/nar/gkp789
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© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Methods Online

A protein–protein interaction guided method for competitive transcription factor binding improves target predictions

Kirsti Laurila1, Olli Yli-Harja1 and Harri Lähdesmäki1,2,*

1Department of Signal Processing, Tampere University of Technology, P.O. Box 527, FI-33101 Tampere and 2Department of Information and Computer Science, Helsinki University of Technology, P.O. Box 5400, FI-02015 TKK, Finland

*To whom correspondence should be addressed. Email: kirsti.laurila{at}tut.fi

Correspondence may also be address to Harri Lähdesmäki Tel: +358 3 3115 11; Fax: +358 33 115 4989 Email: harri.lahdesmaki{at}tut.fi

Received June 29, 2009. Revised August 30, 2009. Accepted September 7, 2009.

An important milestone in revealing cells' functions is to build a comprehensive understanding of transcriptional regulation processes. These processes are largely regulated by transcription factors (TFs) binding to DNA sites. Several TF binding site (TFBS) prediction methods have been developed, but they usually model binding of a single TF at a time albeit few methods for predicting binding of multiple TFs also exist. In this article, we propose a probabilistic model that predicts binding of several TFs simultaneously. Our method explicitly models the competitive binding between TFs and uses the prior knowledge of existing protein–protein interactions (PPIs), which mimics the situation in the nucleus. Modeling DNA binding for multiple TFs improves the accuracy of binding site prediction remarkably when compared with other programs and the cases where individual binding prediction results of separate TFs have been combined. The traditional TFBS prediction methods usually predict overwhelming number of false positives. This lack of specificity is overcome remarkably with our competitive binding prediction method. In addition, previously unpredictable binding sites can be detected with the help of PPIs. Source codes are available at http://www.cs.tut.fi/~harrila/.


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