IASeq: Integrative analysis of allele-specificity of protein-DNA interactions in multiple ChIP-seq datasets

Yingying Wei, Xia Li, Qian fei Wang, Hongkai Ji

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number681
JournalBMC genomics
Volume13
Issue number1
DOIs
StatePublished - Nov 21 2012

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Keywords

  • Allele-specific binding
  • Data integration
  • Histone modification
  • Next-generation sequencing
  • Statistical model
  • Transcription factor

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

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