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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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Online
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1544-6115
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Volume 11, Issue 2 (Jan 2012)

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A Generalized Hidden Markov Model for Determining Sequence-based Predictors of Nucleosome Positioning

Carlee Moser / Mayetri Gupta
Published Online: 2012-01-06 | DOI: https://doi.org/10.2202/1544-6115.1707

Chromatin structure, in terms of positioning of nucleosomes and nucleosome-free regions in the DNA, has been found to have an immense impact on various cell functions and processes, ranging from transcriptional regulation to growth and development. In spite of numerous experimental and computational approaches being developed in the past few years to determine the intrinsic relationship between chromatin structure (nucleosome positioning) and DNA sequence features, there is yet no universally accurate approach to predict nucleosome positioning from the underlying DNA sequence alone. We here propose an alternative approach to predicting nucleosome positioning from sequence, making use of characteristic sequence differences, and inherent dependencies in overlapping sequence features. Our nucleosomal positioning prediction algorithm, based on the idea of generalized hierarchical hidden Markov models (HGHMMs), was used to predict nucleosomal state based on the DNA sequence in yeast chromosome III, and compared with two other existing methods. The HGHMM method performed favorably among the three models in terms of specificity and sensitivity, and provided estimates that were largely consistent with predictions from the method of Yuan and Liu (2008). However, all the methods still give higher than desirable misclassification rates, indicating that sequence-based features may provide only limited information towards understanding positioning of nucleosomes. The method is implemented in the open-source statistical software R, and is freely available from the authors’ website.

Keywords: chromatin; nucleosome; DNA sequence; Bayesian modeling

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Published Online: 2012-01-06


Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1707.

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©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston. Copyright Clearance Center

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