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MetaCompare: a computational pipeline for prioritizing environmental resistome risk
The spread of antibiotic resistance is a growing public health concern. While numerous studies have highlighted the importance of environmental sources and pathways of the spread of antibiotic resistance, a systematic means of comparing and prioritizing risks represented by various environmental com...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5995210/ https://www.ncbi.nlm.nih.gov/pubmed/29718191 http://dx.doi.org/10.1093/femsec/fiy079 |
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author | Oh, Min Pruden, Amy Chen, Chaoqi Heath, Lenwood S Xia, Kang Zhang, Liqing |
author_facet | Oh, Min Pruden, Amy Chen, Chaoqi Heath, Lenwood S Xia, Kang Zhang, Liqing |
author_sort | Oh, Min |
collection | PubMed |
description | The spread of antibiotic resistance is a growing public health concern. While numerous studies have highlighted the importance of environmental sources and pathways of the spread of antibiotic resistance, a systematic means of comparing and prioritizing risks represented by various environmental compartments is lacking. Here, we introduce MetaCompare, a publicly available tool for ranking ‘resistome risk’, which we define as the potential for antibiotic resistance genes (ARGs) to be associated with mobile genetic elements (MGEs) and mobilize to pathogens based on metagenomic data. A computational pipeline was developed in which each ARG is evaluated based on relative abundance, mobility, and presence within a pathogen. This is determined through the assembly of shotgun sequencing data and analysis of contigs containing ARGs to determine if they contain sequence similarity to MGEs or human pathogens. Based on the assembled metagenomes, samples are projected into a 3-dimensionalhazard space and assigned resistome risk scores. To validate, we tested previously published metagenomic data derived from distinct aquatic environments. Based on unsupervised machine learning, the test samples clustered in the hazard space in a manner consistent with their origin. The derived scores produced a well-resolved ascending resistome risk ranking of: wastewater treatment plant effluent, dairy lagoon, and hospital sewage. |
format | Online Article Text |
id | pubmed-5995210 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-59952102018-06-15 MetaCompare: a computational pipeline for prioritizing environmental resistome risk Oh, Min Pruden, Amy Chen, Chaoqi Heath, Lenwood S Xia, Kang Zhang, Liqing FEMS Microbiol Ecol Research Article The spread of antibiotic resistance is a growing public health concern. While numerous studies have highlighted the importance of environmental sources and pathways of the spread of antibiotic resistance, a systematic means of comparing and prioritizing risks represented by various environmental compartments is lacking. Here, we introduce MetaCompare, a publicly available tool for ranking ‘resistome risk’, which we define as the potential for antibiotic resistance genes (ARGs) to be associated with mobile genetic elements (MGEs) and mobilize to pathogens based on metagenomic data. A computational pipeline was developed in which each ARG is evaluated based on relative abundance, mobility, and presence within a pathogen. This is determined through the assembly of shotgun sequencing data and analysis of contigs containing ARGs to determine if they contain sequence similarity to MGEs or human pathogens. Based on the assembled metagenomes, samples are projected into a 3-dimensionalhazard space and assigned resistome risk scores. To validate, we tested previously published metagenomic data derived from distinct aquatic environments. Based on unsupervised machine learning, the test samples clustered in the hazard space in a manner consistent with their origin. The derived scores produced a well-resolved ascending resistome risk ranking of: wastewater treatment plant effluent, dairy lagoon, and hospital sewage. Oxford University Press 2018-04-26 /pmc/articles/PMC5995210/ /pubmed/29718191 http://dx.doi.org/10.1093/femsec/fiy079 Text en © FEMS 2018. http://creativecommons.org/licenses/by-nc/4.0/ 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 |
spellingShingle | Research Article Oh, Min Pruden, Amy Chen, Chaoqi Heath, Lenwood S Xia, Kang Zhang, Liqing MetaCompare: a computational pipeline for prioritizing environmental resistome risk |
title | MetaCompare: a computational pipeline for prioritizing environmental resistome risk |
title_full | MetaCompare: a computational pipeline for prioritizing environmental resistome risk |
title_fullStr | MetaCompare: a computational pipeline for prioritizing environmental resistome risk |
title_full_unstemmed | MetaCompare: a computational pipeline for prioritizing environmental resistome risk |
title_short | MetaCompare: a computational pipeline for prioritizing environmental resistome risk |
title_sort | metacompare: a computational pipeline for prioritizing environmental resistome risk |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5995210/ https://www.ncbi.nlm.nih.gov/pubmed/29718191 http://dx.doi.org/10.1093/femsec/fiy079 |
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