a systems biology approach to transcription factor binding site prediction系统生物学方法转录因子结合位点预测.pdfVIP

A Systems Biology Approach to Transcription Factor Binding Site Prediction 1. 2. 2 1,2,3 Xiang Zhou , Pavel Sumazin , Presha Rajbhandari , Andrea Califano * 1 Department of Biomedical Informatics (DBMI), Columbia University, New York, New York, United States of America, 2 Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, New York, United States of America, 3 Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York, United States of America Abstract Background: The elucidation of mammalian transcriptional regulatory networks holds great promise for both basic and translational research and remains one the greatest challenges to systems biology. Recent reverse engineering methods deduce regulatory interactions from large-scale mRNA expression profiles and cross-species conserved regulatory regions in DNA. Technical challenges faced by these methods include distinguishing between direct and indirect interactions, associating transcription regulators with predicted transcription factor binding sites (TFBSs), identifying non-linearly conserved binding sites across species, and providing realistic accuracy estimates. Methodology/Principal Findings: We address these challenges by closely integrating proven methods for regulatory network reverse engineering from mRNA expression data, linearly and non-linearly conserved regulatory region discovery, and TFBS evaluation and discovery. Using an extensive test set of high-likelihood interactions, which we collected in order to provide realistic prediction-accuracy estimates, we show that a careful integration of these methods lead