Base-resolution methylation patterns accurately predict transcription factor bindings in vivo

• Type of Material: Article
• Authors: Tianlei Xu, Emory University; Ben Li, Emory University; Meng Zhao, Emory University; Keith E. Szulwach, Emory University; R. Craig Street, Emory University; Li Lin, Emory University; Bing Yao, Emory University; Feiran Zhang, Emory University; Peng Jin, Emory University; Hao Wu, Emory University; Zhaohui Qin, Emory University
• Language: English
• Date: 2015-03-11
• Publisher: Oxford University Press (OUP): Policy C - Option B
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2015
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1093/nar/gkv151
• Title of Journal or Parent Work: Nucleic Acids Research
• ISSN: 0305-1048
• Volume: 43
• Issue: 5
• Start Page: 2757
• End Page: 2766
• Grant/Funding Information: National Institutes of Health [R01HG005119 to Z.S.Q.]; National Science Foundation [DMS1000617 to Z.S.Q.].; Funding for open access charge: National Institute of Health [P01 GM085354].
• Research Categories: Biology, Genetics; Biology, Bioinformatics

Abstract

Detecting in vivo transcription factor (TF) binding is important for understanding gene regulatory circuitries. ChIP-seq is a powerful technique to empirically define TF binding in vivo. However, the multitude of distinct TFs makes genome-wide profiling for them all labor-intensive and costly. Algorithms for in silico prediction of TF binding have been developed, based mostly on histone modification or DNase I hypersensitivity data in conjunction with DNA motif and other genomic features. However, technical limitations of these methods prevent them from being applied broadly, especially in clinical settings. We conducted a comprehensive survey involving multiple cell lines, TFs, and methylation types and found that there are intimate relationships between TF binding and methylation level changes around the binding sites. Exploiting the connection between DNA methylation and TF binding, we proposed a novel supervised learning approach to predict TF–DNA interaction using data from base-resolution whole-genome methylation sequencing experiments. We devised beta-binomial models to characterize methylation data around TF binding sites and the background. Along with other static genomic features, we adopted a random forest framework to predict TF–DNA interaction. After conducting comprehensive tests, we saw that the proposed method accurately predicts TF binding and performs favorably versus competing methods.