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MBE Advance Access originally published online on March 5, 2003
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Mol. Biol. Evol. 20(3):315-337. 2003
DOI: 10.1093/molbev/msg039
© 2003 by the Society for Molecular Biology and Evolution. ISSN: 0737-4038

Detecting Recombination in 4-Taxa DNA Sequence Alignments with Bayesian Hidden Markov Models and Markov Chain Monte Carlo

Dirk Husmeier* and Gráinne McGuire{dagger}

* Biomathematics and Statistics Scotland (BioSS), JCMB, King's Buildings, Edinburgh, United Kingdom
{dagger} Department of Applied Statistics, University of Reading, Reading, United Kingdom

This article presents a statistical method for detecting recombination in DNA sequence alignments, which is based on combining two probabilistic graphical models: (1) a taxon graph (phylogenetic tree) representing the relationship between the taxa, and (2) a site graph (hidden Markov model) representing interactions between different sites in the DNA sequence alignments. We adopt a Bayesian approach and sample the parameters of the model from the posterior distribution with Markov chain Monte Carlo, using a Metropolis-Hastings and Gibbs-within-Gibbs scheme. The proposed method is tested on various synthetic and real-world DNA sequence alignments, and we compare its performance with the established detection methods RECPARS, PLATO, and TOPAL, as well as with two alternative parameter estimation schemes.

Key Words: phylogeny • DNA sequence alignment • recombination • hidden Markov models • Markov chain Monte Carlo


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