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GeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure

BACKGROUND: Horizontal gene transfer contributes to bacterial evolution through mobilising genes across various taxonomical boundaries. It is frequently mediated by mobile genetic elements (MGEs), which may capture, maintain, and rearrange mobile genes and co-mobilise them between bacteria, causing...

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Autores principales: Wan, Yu, Wick, Ryan R., Zobel, Justin, Ingle, Danielle J., Inouye, Michael, Holt, Kathryn E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513276/
https://www.ncbi.nlm.nih.gov/pubmed/32972363
http://dx.doi.org/10.1186/s12864-020-07019-6
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author Wan, Yu
Wick, Ryan R.
Zobel, Justin
Ingle, Danielle J.
Inouye, Michael
Holt, Kathryn E.
author_facet Wan, Yu
Wick, Ryan R.
Zobel, Justin
Ingle, Danielle J.
Inouye, Michael
Holt, Kathryn E.
author_sort Wan, Yu
collection PubMed
description BACKGROUND: Horizontal gene transfer contributes to bacterial evolution through mobilising genes across various taxonomical boundaries. It is frequently mediated by mobile genetic elements (MGEs), which may capture, maintain, and rearrange mobile genes and co-mobilise them between bacteria, causing horizontal gene co-transfer (HGcoT). This physical linkage between mobile genes poses a great threat to public health as it facilitates dissemination and co-selection of clinically important genes amongst bacteria. Although rapid accumulation of bacterial whole-genome sequencing data since the 2000s enables study of HGcoT at the population level, results based on genetic co-occurrence counts and simple association tests are usually confounded by bacterial population structure when sampled bacteria belong to the same species, leading to spurious conclusions. RESULTS: We have developed a network approach to explore WGS data for evidence of intraspecies HGcoT and have implemented it in R package GeneMates (github.com/wanyuac/GeneMates). The package takes as input an allelic presence-absence matrix of interested genes and a matrix of core-genome single-nucleotide polymorphisms, performs association tests with linear mixed models controlled for population structure, produces a network of significantly associated alleles, and identifies clusters within the network as plausible co-transferred alleles. GeneMates users may choose to score consistency of allelic physical distances measured in genome assemblies using a novel approach we have developed and overlay scores to the network for further evidence of HGcoT. Validation studies of GeneMates on known acquired antimicrobial resistance genes in Escherichia coli and Salmonella Typhimurium show advantages of our network approach over simple association analysis: (1) distinguishing between allelic co-occurrence driven by HGcoT and that driven by clonal reproduction, (2) evaluating effects of population structure on allelic co-occurrence, and (3) direct links between allele clusters in the network and MGEs when physical distances are incorporated. CONCLUSION: GeneMates offers an effective approach to detection of intraspecies HGcoT using WGS data.
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spelling pubmed-75132762020-09-25 GeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure Wan, Yu Wick, Ryan R. Zobel, Justin Ingle, Danielle J. Inouye, Michael Holt, Kathryn E. BMC Genomics Software BACKGROUND: Horizontal gene transfer contributes to bacterial evolution through mobilising genes across various taxonomical boundaries. It is frequently mediated by mobile genetic elements (MGEs), which may capture, maintain, and rearrange mobile genes and co-mobilise them between bacteria, causing horizontal gene co-transfer (HGcoT). This physical linkage between mobile genes poses a great threat to public health as it facilitates dissemination and co-selection of clinically important genes amongst bacteria. Although rapid accumulation of bacterial whole-genome sequencing data since the 2000s enables study of HGcoT at the population level, results based on genetic co-occurrence counts and simple association tests are usually confounded by bacterial population structure when sampled bacteria belong to the same species, leading to spurious conclusions. RESULTS: We have developed a network approach to explore WGS data for evidence of intraspecies HGcoT and have implemented it in R package GeneMates (github.com/wanyuac/GeneMates). The package takes as input an allelic presence-absence matrix of interested genes and a matrix of core-genome single-nucleotide polymorphisms, performs association tests with linear mixed models controlled for population structure, produces a network of significantly associated alleles, and identifies clusters within the network as plausible co-transferred alleles. GeneMates users may choose to score consistency of allelic physical distances measured in genome assemblies using a novel approach we have developed and overlay scores to the network for further evidence of HGcoT. Validation studies of GeneMates on known acquired antimicrobial resistance genes in Escherichia coli and Salmonella Typhimurium show advantages of our network approach over simple association analysis: (1) distinguishing between allelic co-occurrence driven by HGcoT and that driven by clonal reproduction, (2) evaluating effects of population structure on allelic co-occurrence, and (3) direct links between allele clusters in the network and MGEs when physical distances are incorporated. CONCLUSION: GeneMates offers an effective approach to detection of intraspecies HGcoT using WGS data. BioMed Central 2020-09-24 /pmc/articles/PMC7513276/ /pubmed/32972363 http://dx.doi.org/10.1186/s12864-020-07019-6 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Wan, Yu
Wick, Ryan R.
Zobel, Justin
Ingle, Danielle J.
Inouye, Michael
Holt, Kathryn E.
GeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure
title GeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure
title_full GeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure
title_fullStr GeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure
title_full_unstemmed GeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure
title_short GeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure
title_sort genemates: an r package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513276/
https://www.ncbi.nlm.nih.gov/pubmed/32972363
http://dx.doi.org/10.1186/s12864-020-07019-6
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