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


BACKGROUND
Our program is available at http://bioinformatics.med.yale.edu/TFcooperativity.
Datta
Debayan
D
Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA.
Zhao
Hongyu
H
D000465:Algorithms; D002453:Cell Cycle; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003470:Culture Media; D030562:Databases, Protein; D053263:Gene Regulatory Networks; D015233:Models, Statistical; D011485:Protein Binding; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism; D014157:Transcription Factors / Q000378:metabolism*
D003470:Culture Media; D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors