Detecting recombination in 4-taxa DNA sequence alignments with Bayesian hidden Markov models and Markov chain Monte Carlo.
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.
Husmeier
Dirk
D
Biomathematics and Statistics Scotland, JCMB, King's Buildings, Edinburgh, United Kingdom. dirk@bioss.ac.uk
McGuire
Gráinne
G
D001483:Base Sequence; D001499:Bayes Theorem; D004247:DNA / Q000145:classification / Q000235:genetics*; D016013:Likelihood Functions; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D010802:Phylogeny; D011995:Recombination, Genetic / Q000235:genetics*; D015203:Reproducibility of Results
D004247:DNA