Efficient Inference of Recent and Ancestral Recombination within Bacterial Populations
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Published version
Author(s)
Type
Journal Article
Abstract
Prokaryotic evolution is affected by horizontal transfer of genetic material through recombination. Inference of an evolutionary tree of bacteria thus relies on accurate identification of the population genetic structure and recombination-derived mosaicism. Rapidly growing databases represent a challenge for computational methods to detect recombinations in bacterial genomes. We introduce a novel algorithm called fastGEAR which identifies lineages in diverse microbial alignments, and recombinations between them and from external origins. The algorithm detects both recent recombinations (affecting a few isolates) and ancestral recombinations between detected lineages (affecting entire lineages), thus providing insight into recombinations affecting deep branches of the phylogenetic tree. In simulations, fastGEAR had comparable power to detect recent recombinations and outstanding power to detect the ancestral ones, compared with state-of-the-art methods, often with a fraction of computational cost. We demonstrate the utility of the method by analyzing a collection of 616 whole-genomes of a recombinogenic pathogen Streptococcus pneumoniae, for which the method provided a high-resolution view of recombination across the genome. We examined in detail the penicillin-binding genes across the Streptococcus genus, demonstrating previously undetected genetic exchanges between different species at these three loci. Hence, fastGEAR can be readily applied to investigate mosaicism in bacterial genes across multiple species. Finally, fastGEAR correctly identified many known recombination hotspots and pointed to potential new ones. Matlab code and Linux/Windows executables are available at https://users.ics.aalto.fi/~pemartti/fastGEAR/ (last accessed February 6, 2017).
Date Issued
2017-02-11
Date Acceptance
2017-02-01
Citation
MOLECULAR BIOLOGY AND EVOLUTION, 2017, 34 (5), pp.1167-1182
ISSN
0737-4038
Publisher
OXFORD UNIVERSITY PRESS
Start Page
1167
End Page
1182
Journal / Book Title
MOLECULAR BIOLOGY AND EVOLUTION
Volume
34
Issue
5
Copyright Statement
© 2017 The Author(s). Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License
(http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any
medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License
(http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any
medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Sponsor
Medical Research Council (MRC)
Wellcome Trust
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000399373300011&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
MR/K010174/1B
104169/Z/14/Z
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemistry & Molecular Biology
Evolutionary Biology
Genetics & Heredity
bacterial population genetics
recombination detection
population structure
hidden Markov models
Streptococcus pneumoniae
antibiotic resistance
STREPTOCOCCUS-PNEUMONIAE
GENE-TRANSFER
EVOLUTION
EVENTS
RESISTANCE
DIFFERENTIATION
DIVERSITY
GENOMICS
SAMPLES
IMPACT
0604 Genetics
0603 Evolutionary Biology
0601 Biochemistry And Cell Biology
Publication Status
Published