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Activity Number: 350
Type: Topic Contributed
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: International Chinese Statistical Association
Abstract #316114 View Presentation
Title: Base-Resolution Methylation Patterns Accurately Predict Transcription Factor Bindings in Vivo
Author(s): Hao Wu* and Steve Qin and Ben Li and Tianlei Xu
Companies: Emory University and Emory University and Emory University and Emory University
Keywords: DNA methylation ; machine learning ; transcription factor binding ; bisulfite sequencing ; high-throughput ; second generation sequencing
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 genomi


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