title: Detecting recombination with MCMC.
abstract: BACKGROUND
  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.
authors.affiliation: Biomathematics and Statistics Scotland (BioSS), JCMB, The King's Buildings, Edinburgh EH9 3JZ, UK. dirk@bioss.ac.uk
authors.forename: Dirk
authors.initials: D
authors.lastname: Husmeier
authors.forename: Gráinne
authors.initials: G
authors.lastname: McGuire
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D020869:Gene Expression Profiling / Q000379:methods*; D006515:Hepatitis B virus / Q000235:genetics; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D009010:Monte Carlo Method; D009343:Neisseria / Q000235:genetics; D010802:Phylogeny; D011995:Recombination, Genetic / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D003313:Zea mays / Q000235:genetics