A continuous-index Bayesian hidden Markov model for prediction of nucleosome positioning in genomic DNA.

Mitra, R. and Gupta, M. (2011) A continuous-index Bayesian hidden Markov model for prediction of nucleosome positioning in genomic DNA. Biostatistics, 12(3), pp. 462-477. (doi: 10.1093/biostatistics/kxq077)

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Abstract

Nucleosomes are units of chromatin structure, consisting of DNA sequence wrapped around proteins called "histones." Nucleosomes occur at variable intervals throughout genomic DNA and prevent transcription factor (TF) binding by blocking TF access to the DNA. A map of nucleosomal locations would enable researchers to detect TF binding sites with greater efficiency. Our objective is to construct an accurate genomic map of nucleosome-free regions (NFRs) based on data from high-throughput genomic tiling arrays in yeast. These high-volume data typically have a complex structure in the form of dependence on neighboring probes as well as underlying DNA sequence, variable-sized gaps, and missing data. We propose a novel continuous-index model appropriate for non-equispaced tiling array data that simultaneously incorporates DNA sequence features relevant to nucleosome formation. Simulation studies and an application to a yeast nucleosomal assay demonstrate the advantages of using the new modeling framework, as well as its robustness to distributional misspecifications. Our results reinforce the previous biological hypothesis that higher-order nucleotide combinations are important in distinguishing nucleosomal regions from NFRs.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Gupta, Professor Mayetri
Authors: Mitra, R., and Gupta, M.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Biostatistics
ISSN:1465-4644
ISSN (Online):1468-4357
Published Online:30 December 2010

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