Detecting recombination with MCMC

Authors: Husmeier, Dirk¹; McGuire, Gráinne²

Source: Bioinformatics, Volume 18, Supplement 1, July 2002 , pp. 345-353(9)

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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.

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.

Document Type: Research article

Affiliations: 1: Biomathematics and Statistics Scotland (BioSS), JCMB, The King's Buildings, Edinburgh EH9 3JZ, UK 2: School of Applied Statistics, University of Reading, Reading RG6 6FN, UK

Publication date: 2002-07-01

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The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.