Efficient Inference of Recent and Ancestral Recombination within Bacterial Populations

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Title: Efficient Inference of Recent and Ancestral Recombination within Bacterial Populations
Author(s): Mostowy, R
Croucher, NJ
Andam, CP
Corander, J
Hanage, WP
Marttinen, P
Item 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).
Publication Date: 11-Feb-2017
Date of Acceptance: 1-Feb-2017
URI: http://hdl.handle.net/10044/1/48714
DOI: https://dx.doi.org/10.1093/molbev/msx066
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
Sponsor/Funder: Medical Research Council (MRC)
Wellcome Trust
Funder's Grant Number: MR/K010174/1B
104169/Z/14/Z
Keywords: 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
Streptococcus pneumoniae
antibiotic resistance
bacterial population genetics
hidden Markov models
population structure
recombination detection
Evolutionary Biology
0604 Genetics
0603 Evolutionary Biology
0601 Biochemistry And Cell Biology
Publication Status: Published
Appears in Collections:Faculty of Medicine
Epidemiology, Public Health and Primary Care



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