A Bayesian approach to DNA sequence segmentation

Biometrics
Richard J Boys, Daniel A Henderson

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.

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Citations

Oct 14, 2006·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Jonathan M Keith
Apr 26, 2008·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Jonathan M KeithJohn S Mattick
Mar 10, 2010·Biostatistics·Hao WuAndrew P Feinberg
Oct 6, 2012·Nucleic Acids Research·Rajeev K Azad, Jing Li
Apr 23, 2008·EURASIP Journal on Bioinformatics & Systems Biology·David K Y Chiu, Yan Wang
Dec 19, 2009·Molecular Biology and Evolution·Eran ElhaikKresimir Josic
Jul 14, 2005·Biometrics·Hilary S BoothSusan R Wilson
Sep 2, 2006·Statistics in Medicine·Deborah Ashby
Oct 29, 2014·Computational and Structural Biotechnology Journal·Manjula Algama, Jonathan M Keith
Mar 16, 2007·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Vivek ThakurRam Ramaswamy
Mar 23, 2017·Biometrical Journal. Biometrische Zeitschrift·Daiane Aparecida Zuanetti, Luis Aparecido Milan

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