Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling

Samb Rawane, Khadraoui Khader, Belleau Pascal, Deschênes Astrid, Lakhal-Chaieb Lajmi and Droit Arnaud ()
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Samb Rawane: Centre de Recherche du CHU de Québec – Pavillon CHUL, Faculté de Médecine, Université Laval, 2705 Boulevard Laurier, Québec, QC G1V 4G2, Canada
Khadraoui Khader: Département de mathématiques et statistique, Université Laval, Québec, QC G1V 0A6, Canada
Belleau Pascal: Centre de Recherche du CHU de Québec – Pavillon CHUL, Faculté de Médecine, Université Laval, 2705 Boulevard Laurier, Québec, QC G1V 4G2, Canada
Deschênes Astrid: Centre de Recherche du CHU de Québec – Pavillon CHUL, Faculté de Médecine, Université Laval, 2705 Boulevard Laurier, Québec, QC G1V 4G2, Canada
Lakhal-Chaieb Lajmi: Département de mathématiques et statistique, Université Laval, Québec, QC G1V 0A6, Canada
Droit Arnaud: Centre de Recherche du CHU de Québec – Pavillon CHUL, Faculté de Médecine, Université Laval, 2705 Boulevard Laurier, Québec, QC G1V 4G2, Canada

Statistical Applications in Genetics and Molecular Biology, 2015, vol. 14, issue 6, 517-532

Abstract: Genome-wide mapping of nucleosomes has revealed a great deal about the relationships between chromatin structure and control of gene expression. Recent next generation CHIP-chip and CHIP-Seq technologies have accelerated our understanding of basic principles of chromatin organization. These technologies have taught us that nucleosomes play a crucial role in gene regulation by allowing physical access to transcription factors. Recent methods and experimental advancements allow the determination of nucleosome positions for a given genome area. However, most of these methods estimate the number of nucleosomes either by an EM algorithm using a BIC criterion or an effective heuristic strategy. Here, we introduce a Bayesian method for identifying nucleosome positions. The proposed model is based on a Multinomial-Dirichlet classification and a hierarchical mixture distributions. The number and the positions of nucleosomes are estimated using a reversible jump Markov chain Monte Carlo simulation technique. We compare the performance of our method on simulated data and MNase-Seq data from Saccharomyces cerevisiae against PING and NOrMAL methods.

Keywords: Bayesian t-mixture; genome-wide profiling; Multinomial-Dirichlet prior; nucleosome positioning; reversible-jump MCMC (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/sagmb-2014-0098

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