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

Rawane Samb , Khader Khadraoui , Pascal Belleau , Astrid Deschênes , Lajmi Lakhal-Chaieb and Arnaud Droit EMAIL logo

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.


Corresponding author: Arnaud Droit, Centre de Recherche du CHU de Québec – Pavillon CHUL, Faculté de Médecine, Université Laval, 2705 Boulevard Laurier, R-4773, Québec, QC G1V 4G2, Canada, e-mail:
aRawane Samb, Khader Khadraoui and Pascal Belleau: These authors contributed equally to this work.

Acknowledgments

We thank Frédéric Fournier for assistance with the preparation of the manuscript. We also thank Charles Joly Beauparlant for his advices on algorithm implementation and Fabien Claude Lamaze for assistance with the biological aspect. RJMCMC computations on simulated and biological data were made on the supercomputer Colosse from Université Laval, managed by Calcul Québec and Compute Canada. The operation of this supercomputer is funded by the Canada Foundation for Innovation (CFI), Ministère de l’Économie, de l’Innovation et des Exportations du Québec (MEIE), RMGA and the Fonds de recherche du Québec – Nature et technologies (FRQ-NT).

Funding: Canadian Institutes of Health Research, (Grant/Award number: IC513823).

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Supplemental Material:

The online version of this article (DOI: 10.1515/sagmb-2014-0098) offers supplementary material, available to authorized users.


Published Online: 2015-12-10
Published in Print: 2015-12-1

©2015 by De Gruyter

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