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

Dirk Husmeier | Zendy

Open Access

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

Author(s) -

Dirk Husmeier

Publication year - 2003

Publication title -

molecular biology and evolution

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 6.637

H-Index - 218

eISSN - 1537-1719

pISSN - 0737-4038

DOI - 10.1093/molbev/msg039

Subject(s) - markov chain monte carlo , gibbs sampling , hidden markov model , biology , markov chain , phylogenetic tree , sequence (biology) , bayesian probability , posterior probability , graphical model , probabilistic logic , evolutionary biology , computational biology , genetics , mathematics , computer science , statistics , artificial intelligence , gene

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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