Detection of recombination events in bacterial genomes from large population samples

  • Marttinen P
  • Hanage W
  • Croucher N
 et al. 
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Abstract

Analysis of important human pathogen populations is currently under
transition toward whole-genome sequencing of growing numbers of samples
collected on a global scale. Since recombination in bacteria is often
an important factor shaping their evolution by enabling resistance
elements and virulence traits to rapidly transfer from one evolutionary
lineage to another, it is highly beneficial to have access to tools
that can detect recombination events. Multiple advanced statistical
methods exist for such purposes; however, they are typically limited
either to only a few samples or to data from relatively short regions
of a total genome. By harnessing the power of recent advances in
Bayesian modeling techniques, we introduce here a method for detecting
homologous recombination events from whole-genome sequence data for
bacterial population samples on a large scale. Our statistical approach
can efficiently handle hundreds of whole genome sequenced population
samples and identify separate origins of the recombinant sequence,
offering an enhanced insight into the diversification of bacterial
clones at the level of the whole genome. A data set of 241 whole
genome sequences from an important pandemic lineage of Streptococcus
pneumoniae is used together with multiple simulated data sets to
demonstrate the potential of our approach.

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Authors

  • P Marttinen

  • W P Hanage

  • N J Croucher

  • T R Connor

  • S R Harris

  • S D Bentley

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