A Bayesian mixture model for chromatin interaction data

Niu Liang and Lin Shili ()
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Niu Liang: Department of Environmental Health, School of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
Lin Shili: Department of Statistics, Ohio State University, Columbus, OH 43210, USA

Statistical Applications in Genetics and Molecular Biology, 2015, vol. 14, issue 1, 53-64

Abstract: Chromatin interactions mediated by a particular protein are of interest for studying gene regulation, especially the regulation of genes that are associated with, or known to be causative of, a disease. A recent molecular technique, Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET), that uses chromatin immunoprecipitation (ChIP) and high throughput paired-end sequencing, is able to detect such chromatin interactions genomewide. However, ChIA-PET may generate noise (i.e., pairings of DNA fragments by random chance) in addition to true signal (i.e., pairings of DNA fragments by interactions). In this paper, we propose MC_DIST based on a mixture modeling framework to identify true chromatin interactions from ChIA-PET count data (counts of DNA fragment pairs). The model is cast into a Bayesian framework to take into account the dependency among the data and the available information on protein binding sites and gene promoters to reduce false positives. A simulation study showed that MC_DIST outperforms the previously proposed hypergeometric model in terms of both power and type I error rate. A real data study showed that MC_DIST may identify potential chromatin interactions between protein binding sites and gene promoters that may be missed by the hypergeometric model. An R package implementing the MC_DIST model is available at http://www.stat.osu.edu/~statgen/SOFTWARE/MDM.

Keywords: Bayesian mixture model; ChIA-PET; R package (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/sagmb-2014-0029

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