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A Bayesian Approach to DNA Sequence Segmentation

Lookup NU author(s): Professor Richard Boys, Dr Daniel Henderson

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Abstract

Many deoxyribonucleic acid (DNA) sequences display compositional heterogeneity in the form of segments of similar structure. This article describes a Bayesian method that identifies such segments by using a Markov chain governed by a hidden Markov model. Markov chain Monte Carlo (MCMC) techniques are employed to compute all posterior quantities of interest and, in particular, allow inferences to be made regarding the number of segment types and the order of Markov dependence in the DNA sequence. The method is applied to the segmentation of the bacteriophage lambda genome, a common benchmark sequence used for the comparison of statistical segmentation algorithms.

Publication metadata

Author(s): Boys RJ, Henderson DA

Publication type: Article

Publication status: Published

Journal: Biometrics

Year: 2004

Volume: 60

Issue: 3

Pages: 573-581

Print publication date: 01/09/2004

ISSN (print): 0006-341X

ISSN (electronic): 1541-0420

Publisher: John Wiley & Sons, Inc.

URL: http://dx.doi.org/10.1111/j.0006-341X.2004.00206.x

DOI: 10.1111/j.0006-341X.2004.00206.x

PubMed id: 15339274

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