RT Journal Article
SR Electronic
T1 Bayesian inference of ancestral recombination graphs for bacterial populations
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 059105
DO 10.1101/059105
A1 Timothy G. Vaughan
A1 David Welch
A1 Alexei J. Drummond
A1 Patrick J. Biggs
A1 Tessy George
A1 Nigel P. French
YR 2016
UL http://biorxiv.org/content/early/2016/06/15/059105.abstract
AB Homologous recombination is a central feature of bacterial evolution, yet confounds traditional phylogenetic methods. While a number of methods specific to bacterial evolution have been developed, none of these permit joint inference of a bacterial recombination graph and associated parameters. In this paper, we present a new method which addresses this shortcoming. Our method uses a novel Markov chain Monte Carlo algorithm to perform phylogenetic inference under the ClonalOrigin model of Didelot et al. (Genetics, 2010). We demonstrate the utility of our method by applying it to rMLST data sequenced from pathogenic and non-pathogenic Escherichia coli serotype O157 and O26 isolates collected in rural New Zealand. The method is implemented as an open source BEAST 2 package, Bacter, which is available via the project web page at tgvaughan.github.io/bacter