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Detecting recombination with MCMC.

Dirk Husmeier·Gráinne McGuire
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Abstract

MOTIVATION: We present a statistical method for detecting recombination, whose objective is to accurately locate the recombinant breakpoints in DNA sequence alignments of small numbers of taxa (4 or 5). Our approach explicitly models the sequence of phylogenetic tree topologies along a multiple sequence alignment. Inference under this model is done in a Bayesian way, using Markov chain Monte Carlo (MCMC). The algorithm returns the site-dependent posterior probability of each tree topology, which is used for detecting recombinant regions and locating their breakpoints. RESULTS: The method was tested on a synthetic and three real DNA sequence alignments, where it was found to outperform the established detection methods PLATO, RECPARS, and TOPAL.

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AlgorithmsBase SequenceGene Expression ProfilingHepatitis B virusMarkov ChainsModels, GeneticModels, StatisticalMolecular Sequence DataMonte Carlo MethodNeisseriaPhylogenyRecombination, GeneticSequence AlignmentSequence Analysis, DNASoftwareZea maysComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tValidation Study

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BibTeXRIS

Dirk Husmeier, Gráinne McGuire. 2002. Detecting recombination with MCMC.. https://doi.org/10.1093/bioinformatics%2F18.suppl_1.s345

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