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dc.contributor.authorMcGuire, Graineen
dc.contributor.authorHusmeier, Dirken
dc.date.accessioned2008-11-27T13:22:14Z
dc.date.available2008-11-27T13:22:14Z
dc.date.issued2003-01-01en
dc.identifier.citationMcGuire Graine (External), Husmeier, DH. (2003-01-01) Detecting Recombination in 4-Taxa DNA Sequence Alignments with Bayesian Hidden Markov Models and Markov Chain Monte Carlo, Molecular Biology and Evolution 20 (3) 315-337en
dc.identifier.issn0737-4038en
dc.identifier.urihttp://mbe.oxfordjournals.org/cgi/content/abstract/20/3/315en
dc.identifier.uri10.1093/molbev/msg039en
dc.identifier.urihttp://hdl.handle.net/1842/2570
dc.description.abstractThis 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.en
dc.format.extent843472 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.titleDetecting Recombination in 4-Taxa DNA Sequence Alignments with Bayesian Hidden Markov Models and Markov Chain Monte Carloen
dc.typeArticleen


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