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Activity Number: 352
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #305945
Title: Iaseq: Integrating Multiple ChIP-Seq Data Sets for Detecting Allele-Specific Binding
Author(s): Yingying Wei*+ and Xia Li and Hongkai Ji
Companies: The Johns Hopkins University and Beijing Institute of Genomics and The Johns Hopkins University
Address: Dept of Biostatistics Johns Hopkins Univ, Baltimore, MD, 21205-2179, United States
Keywords: Bayes hierarchical model ; Allele-specific binding ; ChIP-seq ; Next generation sequencing ; Multiple datasets ; Meta analysis

In diploid organisms, certain genes can be expressed, methylated or regulated in an allele-specific manner. These allele-specific events (AS) are of high interest for phenotypic diversity and disease susceptibility. Next generation sequencing technologies provide opportunities to study AS globally. However, little is known about the mechanism of AS. For instance, the patterns of allele-specific binding (ASB) across different Transcription Factors (TFs) and histone modifications (HMs) are unclear. Moreover, the limited number of reads on heterozygotic SNPs results in low-signal-to-noise ratio when calling AS. Here, we propose a Bayes hierarchical model to study ASB by jointly analyzing multiple ChIP-seq studies. The model is able to learn the patterns of ASB across studies and make substantial improvement in calling ASB. We apply our model to 57 ChIP-seq studies for GM12878 in ENCODE. Compared to single study based statistics, its accuracy of identifying ASB increases more than 40% according to two gold standards. Besides, we reveal relationships across different TFs and HMs for ASB. In principle, the model can also be applied to call AS for multiple RNA-seq and MeDIP-seq studies.

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