Detecting recombination with MCMC

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 sequ...

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Bibliographic Details
Published in:Bioinformatics 2002-07, Vol.18 (suppl-1), p.S345-S353, Article S345
Main Authors: Husmeier, Dirk, McGuire, Gráinne
Format: Article
Language:English
Subjects:
Algorithms
Barce
Base Sequence
Gene Expression Profiling - methods
Hepatitis B virus - genetics
Markov Chains
Models, Genetic
Models, Statistical
Molecular Sequence Data
Monte Carlo Method
Neisseria - genetics
Phylogeny
Recombination, Genetic - genetics
Sequence Alignment - methods
Sequence Analysis, DNA - methods
Software
Zea mays - genetics
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Summary: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_software Contact: dirk@bioss.ac.uk Keywords: phylogenetic trees; DNA sequence alignments; recombination; hidden Markov models; Gibbs sampling; Markov chain Monte Carlo.
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/18.suppl_1.S345