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Nucleic Acids Res. 2015 Mar 11;43(5):2757-66. doi: 10.1093/nar/gkv151. Epub 2015 Feb 26.

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

Author information

1
Department of Mathematics and Computer Science, Emory University, 400 Dowman Drive, Atlanta, GA 30322, USA Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA 30322, USA.
2
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA 30322, USA.
3
Department of Human Genetics, Emory University, School of Medicine, 615 Michael Street, Atlanta, GA 30322, USA.
4
Department of Human Genetics, Emory University, School of Medicine, 615 Michael Street, Atlanta, GA 30322, USA zhaohui.qin@emory.edu.
5
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA 30322, USA zhaohui.qin@emory.edu.
6
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA 30322, USA Department of Biomedical Informatics, Emory University, 36 Eagle Row, Atlanta, GA 30322, USA zhaohui.qin@emory.edu.

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.

PMID:
25722376
PMCID:
PMC4357735
DOI:
10.1093/nar/gkv151
[Indexed for MEDLINE]
Free PMC Article

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