Detection of recombination events in bacterial genomes from large population samples

Marttinen, Pekka, Hanage, William Paul, Croucher, Nicholas J., Connor, Thomas Richard ORCID: https://orcid.org/0000-0003-2394-6504, Harris, Simon R., Bentley, Stephen D. and Corander, Jukka 2011. Detection of recombination events in bacterial genomes from large population samples. Nucleic Acids Research 40 (1) , e6. 10.1093/nar/gkr928

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Biosciences Systems Immunity Research Institute (SIURI)
Subjects:	Q Science > QH Natural history > QH301 Biology Q Science > QR Microbiology > QR180 Immunology Q Science > QR Microbiology > QR355 Virology
Publisher:	Oxford University Press
ISSN:	0305-1048
Last Modified:	21 Oct 2022 10:51
URI:	https://orca.cardiff.ac.uk/id/eprint/41538

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