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Bioinformatics Advance Access published online on November 7, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm523
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Statistical methods to infer cooperative binding among transcription factors in Saccharomyces cerevisiae

Debayan Datta a and Hongyu Zhao b,c,*

aDepartment of Biomedical Engineering,Yale University,New Haven,CT 06520
bDepartment of Epidemiology and Public Health,Yale University,New Haven,CT 06520
cDepartment of Genetics,Yale University,New Haven,CT 06520

*To whom correspondence should be addressed. Hongyu Zhao, E-mail: hongyu.zhao{at}yale.edu


  Abstract

Motivation: Transcription factors regulate transcription in prokaryotes and eukaryotes by binding to specific DNA sequences in the regulatory regions of the genes. This regulation usually occurs in a coordinated manner involving multiple transcription factors. Genome wide location data, also called ChIP-chip data, have enabled researchers to infer the binding sites for individual regulatory proteins. However, current methods to infer binding sites, such as simple thresholding based on p-values, are not optimal for a number of study objectives like combinatorial regulation, leading to potential loss of information. Hence, there is a need to develop more efficient statistical methods for analyzing such data.

Results: We propose to use log-linear models to study cooperative binding among transcription factors and have developed an Expectation Maximization algorithm for statistical inferences. Our method is advantageous over simple thresholding methods both based on simulation and real data studies. We apply our method to infer the cooperative network of 204 regulators in Rich Medium and a subset of them in four different environmental conditions. Our results indicate that the cooperative network is condition specific; for a set of regulators, the network structure changes under different environmental conditions.

Availability: Our program is available at http://bioinformatics.med.yale.edu/TFcooperativity.

Contact: hongyu.zhao{at}yale.edu

Associate Editor: Dr. Jonathan Wren

Received on August 5, 2007; revised on September 17, 2007; accepted on October 12, 2007

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