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Open Access Highly Accessed Methodology article

iASeq: integrative analysis of allele-specificity of protein-DNA interactions in multiple ChIP-seq datasets

Yingying Wei1, Xia Li23, Qian-fei Wang2 and Hongkai Ji1*

  • * Corresponding author: Hongkai Ji hji@jhsph.edu

  • † Equal contributors

Author Affiliations

1 Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe StreetBaltimore, Maryland 21205, USA

2 CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, P.R. China

3 University of Chinese Academy of Sciences, Beijing 100049, P.R. China

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BMC Genomics 2012, 13:681  doi:10.1186/1471-2164-13-681

Published: 29 November 2012

Abstract

Background

ChIP-seq provides new opportunities to study allele-specific protein-DNA binding (ASB). However, detecting allelic imbalance from a single ChIP-seq dataset often has low statistical power since only sequence reads mapped to heterozygote SNPs are informative for discriminating two alleles.

Results

We develop a new method iASeq to address this issue by jointly analyzing multiple ChIP-seq datasets. iASeq uses a Bayesian hierarchical mixture model to learn correlation patterns of allele-specificity among multiple proteins. Using the discovered correlation patterns, the model allows one to borrow information across datasets to improve detection of allelic imbalance. Application of iASeq to 77 ChIP-seq samples from 40 ENCODE datasets and 1 genomic DNA sample in GM12878 cells reveals that allele-specificity of multiple proteins are highly correlated, and demonstrates the ability of iASeq to improve allelic inference compared to analyzing each individual dataset separately.

Conclusions

iASeq illustrates the value of integrating multiple datasets in the allele-specificity inference and offers a new tool to better analyze ASB.

Keywords:
Allele-specific binding; Transcription factor; Histone modification; Data integration; Next-generation sequencing; Statistical model