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

Author

Listed:
  • Samb Rawane
  • Belleau Pascal
  • Khadraoui Khader
  • Deschênes Astrid
  • Droit Arnaud

    (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)

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.

Suggested Citation

  • Samb Rawane & Belleau Pascal & Khadraoui Khader & Deschênes Astrid & Droit Arnaud & Lakhal-Chaieb Lajmi, 2015. "Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(6), pages 517-532, December.
    • Handle: RePEc:bpj:sagmbi:v:14:y:2015:i:6:p:517-532:n:2
    DOI: 10.1515/sagmb-2014-0098
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    References listed on IDEAS

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    1. Xuekui Zhang & Gordon Robertson & Martin Krzywinski & Kaida Ning & Arnaud Droit & Steven Jones & Raphael Gottardo, 2011. "PICS: Probabilistic Inference for ChIP-seq," Biometrics, The International Biometric Society, vol. 67(1), pages 151-163, March.
    2. Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
    3. Kuan Pei Fen & Huebert Dana & Gasch Audrey & Keles Sunduz, 2009. "A Non-Homogeneous Hidden-State Model on First Order Differences for Automatic Detection of Nucleosome Positions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-45, June.
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