Journal
BMC GENOMICS
Volume 13, Issue -, Pages -Publisher
BIOMED CENTRAL LTD
DOI: 10.1186/1471-2164-13-681
Keywords
Allele-specific binding; Transcription factor; Histone modification; Data integration; Next-generation sequencing; Statistical model
Funding
- National Institute of Health [R01HG006841]
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDA01010305]
- Hundred Talents Program of the Chinese Academy of Sciences
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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.
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