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MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches

BACKGROUND: Metagenomic datasets provide an opportunity to study horizontal gene transfer (HGT) on the level of a microbial community. However, current HGT detection methods cannot be applied to community-level datasets or require reference genomes. Here, we present MetaCHIP, a pipeline for referenc...

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Autores principales: Song, Weizhi, Wemheuer, Bernd, Zhang, Shan, Steensen, Kerrin, Thomas, Torsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6399960/
https://www.ncbi.nlm.nih.gov/pubmed/30832740
http://dx.doi.org/10.1186/s40168-019-0649-y
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author Song, Weizhi
Wemheuer, Bernd
Zhang, Shan
Steensen, Kerrin
Thomas, Torsten
author_facet Song, Weizhi
Wemheuer, Bernd
Zhang, Shan
Steensen, Kerrin
Thomas, Torsten
author_sort Song, Weizhi
collection PubMed
description BACKGROUND: Metagenomic datasets provide an opportunity to study horizontal gene transfer (HGT) on the level of a microbial community. However, current HGT detection methods cannot be applied to community-level datasets or require reference genomes. Here, we present MetaCHIP, a pipeline for reference-independent HGT identification at the community level. RESULTS: Assessment of MetaCHIP’s performance on simulated datasets revealed that it can predict HGTs with various degrees of genetic divergence from metagenomic datasets. The results also indicated that the detection of very recent gene transfers (i.e. those with low levels of genetic divergence) from metagenomics datasets is largely affected by the read assembly step. Comparison of MetaCHIP with a previous analysis on soil bacteria showed a high level of consistency for the prediction of recent HGTs and revealed a large number of additional non-recent gene transfers, which can provide new biological and ecological insight. Assessment of MetaCHIP’s performance on real metagenomic datasets confirmed the role of HGT in the spread of genes related to antibiotic resistance in the human gut microbiome. Further testing also showed that functions related to energy production and conversion as well as carbohydrate transport and metabolism are frequently transferred among free-living microorganisms. CONCLUSION: MetaCHIP provides an opportunity to study HGTs among members of a microbial community and therefore has several applications in the field of microbial ecology and evolution. MetaCHIP is implemented in Python and freely available at https://github.com/songweizhi/MetaCHIP. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40168-019-0649-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-63999602019-03-14 MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches Song, Weizhi Wemheuer, Bernd Zhang, Shan Steensen, Kerrin Thomas, Torsten Microbiome Methodology BACKGROUND: Metagenomic datasets provide an opportunity to study horizontal gene transfer (HGT) on the level of a microbial community. However, current HGT detection methods cannot be applied to community-level datasets or require reference genomes. Here, we present MetaCHIP, a pipeline for reference-independent HGT identification at the community level. RESULTS: Assessment of MetaCHIP’s performance on simulated datasets revealed that it can predict HGTs with various degrees of genetic divergence from metagenomic datasets. The results also indicated that the detection of very recent gene transfers (i.e. those with low levels of genetic divergence) from metagenomics datasets is largely affected by the read assembly step. Comparison of MetaCHIP with a previous analysis on soil bacteria showed a high level of consistency for the prediction of recent HGTs and revealed a large number of additional non-recent gene transfers, which can provide new biological and ecological insight. Assessment of MetaCHIP’s performance on real metagenomic datasets confirmed the role of HGT in the spread of genes related to antibiotic resistance in the human gut microbiome. Further testing also showed that functions related to energy production and conversion as well as carbohydrate transport and metabolism are frequently transferred among free-living microorganisms. CONCLUSION: MetaCHIP provides an opportunity to study HGTs among members of a microbial community and therefore has several applications in the field of microbial ecology and evolution. MetaCHIP is implemented in Python and freely available at https://github.com/songweizhi/MetaCHIP. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40168-019-0649-y) contains supplementary material, which is available to authorized users. BioMed Central 2019-03-04 /pmc/articles/PMC6399960/ /pubmed/30832740 http://dx.doi.org/10.1186/s40168-019-0649-y Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.
spellingShingle Methodology
Song, Weizhi
Wemheuer, Bernd
Zhang, Shan
Steensen, Kerrin
Thomas, Torsten
MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches
title MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches
title_full MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches
title_fullStr MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches
title_full_unstemmed MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches
title_short MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches
title_sort metachip: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6399960/
https://www.ncbi.nlm.nih.gov/pubmed/30832740
http://dx.doi.org/10.1186/s40168-019-0649-y
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