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Bayesian inference of ancestral recombination graphs for bacterial populations

Timothy G. Vaughan, David Welch, Alexei J. Drummond, Patrick J. Biggs, Tessy George, Nigel P. French
doi: https://doi.org/10.1101/059105
Timothy G. Vaughan
Centre for Computational Evolution and Department of Computer Science, University of Auckland, Auckland, New Zealand,
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  • For correspondence: tgvaughan@gmail.com
David Welch
Centre for Computational Evolution and Department of Computer Science, University of Auckland, Auckland, New Zealand,
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Alexei J. Drummond
Centre for Computational Evolution and Department of Computer Science, University of Auckland, Auckland, New Zealand,
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Patrick J. Biggs
mEpiLab, Infectious Disease Research Centre, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand
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Tessy George
mEpiLab, Infectious Disease Research Centre, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand
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Nigel P. French
mEpiLab, Infectious Disease Research Centre, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand
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Abstract

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

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 15, 2016.
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Bayesian inference of ancestral recombination graphs for bacterial populations
Timothy G. Vaughan, David Welch, Alexei J. Drummond, Patrick J. Biggs, Tessy George, Nigel P. French
bioRxiv 059105; doi: https://doi.org/10.1101/059105
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Bayesian inference of ancestral recombination graphs for bacterial populations
Timothy G. Vaughan, David Welch, Alexei J. Drummond, Patrick J. Biggs, Tessy George, Nigel P. French
bioRxiv 059105; doi: https://doi.org/10.1101/059105

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