Bayesian modeling of recombination events in bacterial populations
1 Department of Mathematics and statistics, University of Helsinki, FIN-00014, Finland
2 Department of Biological Sciences, Warwick University, Coventry. CV4 7AL, UK
3 Department of Infectious Disease Epidemiology, Imperial College London. W2 1PG, UK
4 Cardiff School of Biosciences, Cardiff University, Cardiff. CF10 3TL, UK
5 Department of Mathematics, Abo Akademi University, FIN-20500, Finland
BMC Bioinformatics 2008, 9:421 doi:10.1186/1471-2105-9-421Published: 7 October 2008
We consider the discovery of recombinant segments jointly with their origins within multilocus DNA sequences from bacteria representing heterogeneous populations of fairly closely related species. The currently available methods for recombination detection capable of probabilistic characterization of uncertainty have a limited applicability in practice as the number of strains in a data set increases.
We introduce a Bayesian spatial structural model representing the continuum of origins over sites within the observed sequences, including a probabilistic characterization of uncertainty related to the origin of any particular site. To enable a statistically accurate and practically feasible approach to the analysis of large-scale data sets representing a single genus, we have developed a novel software tool (BRAT, Bayesian Recombination Tracker) implementing the model and the corresponding learning algorithm, which is capable of identifying the posterior optimal structure and to estimate the marginal posterior probabilities of putative origins over the sites.
A multitude of challenging simulation scenarios and an analysis of real data from seven housekeeping genes of 120 strains of genus Burkholderia are used to illustrate the possibilities offered by our approach. The software is freely available for download at URL http://web.abo.fi/fak/mnf//mate/jc/software/brat.html webcite.