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A Bayesian Approach to DNA Sequence Segmentation
Author(s) -
Boys Richard J.,
Henderson Daniel A.
Publication year - 2004
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2004.00206.x
Subject(s) - markov chain monte carlo , markov chain , bayesian probability , sequence (biology) , segmentation , benchmark (surveying) , computer science , hidden markov model , markov model , pattern recognition (psychology) , artificial intelligence , algorithm , mathematics , computational biology , machine learning , biology , genetics , geography , geodesy
Summary 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.