Detecting recombination in 4-taxa DNA sequence alignments with Bayesian hidden Markov models and Markov chain Monte Carlo |
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Authors: | Husmeier Dirk McGuire Gráinne |
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Affiliation: | Biomathematics and Statistics Scotland, JCMB, King's Buildings, Edinburgh, United Kingdom. dirk@bioss.ac.uk |
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Abstract: | 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. |
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Keywords: | phylogeny DNA sequence alignment recombination hidden Markov models Markov chain Monte Carlo |
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