Detecting recombination with MCMC
Source: Bioinformatics, Volume 18, Supplement 1, July 2002 , pp. 345-353(9)
Publisher: Oxford University Press
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.Availability: The algorithm has been implemented in the C++ program package BARCE, which is freely available from http://www.bioss.sari.ac.uk/~dirk/my_softwareContact: firstname.lastname@example.orgKeywords: phylogenetic trees; DNA sequence alignments; recombination; hidden Markov models; Gibbs sampling; Markov chain Monte Carlo.
Document Type: Research article
Publication date: 2002-07-01
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