Abstract
Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution. Here, we proposed a new statistical approach with a mixture model, chromatin interaction analysis using mixture model (ChIAMM), to detect significant chromatin interactions from ChIA-PET data. The statistical model is cast into a Bayesian framework to consider more systematic biases: the genomic distance, local enrichment, mappability, and GC content. Using different ChIA-PET datasets, we evaluated the performance of ChIAMM and compared it with the existing methods, including ChIA-PET Tool, ChiaSig, Mango, ChIA-PET2, and ChIAPoP. The result showed that the new approach performed better than most top existing methods in detecting significant chromatin interactions in ChIA-PET experiments.
Highlights
Diverse high-throughput methods have been developed to detect genome-wide chromatin interactions, including chromatin interaction analysis by paired-end tag sequencing (ChIAPET) and high-throughput chromosome conformation capture (Hi-C) (Fullwood et al, 2009; Lieberman-Aiden et al, 2009)
Chromatin interaction analysis by paired-end tag sequencing is a genome-wide, high-throughput, and high-resolution method to detect chromatin interactions associated with a specific protein of interest
We described a new statistical approach called chromatin interaction analysis using mixture model (ChIAMM) that corrects for non-specific interactions as a function of genomic distance, enrichment, GC content, and mappability score
Summary
Diverse high-throughput methods have been developed to detect genome-wide chromatin interactions, including chromatin interaction analysis by paired-end tag sequencing (ChIAPET) and high-throughput chromosome conformation capture (Hi-C) (Fullwood et al, 2009; Lieberman-Aiden et al, 2009). ChIA-PET was first introduced in 2009 as an essential experimental method for studying genome-wide chromatin interactions mediated by a specific protein of interest. It can discover many chromatin interactions at a higher resolution that are needed for studying gene transcription regulation. The processing of raw ChIA-PET data is not easy. The steps to process raw ChIA-PET data include linker trimming, read alignment, paired-end tag (PET) filtering, PCR duplicate
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