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Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing

Whole genome sequencing (WGS) is a key tool in identifying and characterising disease-associated bacteria across clinical, agricultural, and environmental contexts. One increasingly common use of genomic and metagenomic sequencing is in identifying the type and range of antimicrobial resistance (AMR...

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Autores principales: Raphenya, Amogelang R., Robertson, James, Jamin, Casper, de Oliveira Martins, Leonardo, Maguire, Finlay, McArthur, Andrew G., Hays, John P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200708/
https://www.ncbi.nlm.nih.gov/pubmed/35705638
http://dx.doi.org/10.1038/s41597-022-01463-7
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author Raphenya, Amogelang R.
Robertson, James
Jamin, Casper
de Oliveira Martins, Leonardo
Maguire, Finlay
McArthur, Andrew G.
Hays, John P.
author_facet Raphenya, Amogelang R.
Robertson, James
Jamin, Casper
de Oliveira Martins, Leonardo
Maguire, Finlay
McArthur, Andrew G.
Hays, John P.
author_sort Raphenya, Amogelang R.
collection PubMed
description Whole genome sequencing (WGS) is a key tool in identifying and characterising disease-associated bacteria across clinical, agricultural, and environmental contexts. One increasingly common use of genomic and metagenomic sequencing is in identifying the type and range of antimicrobial resistance (AMR) genes present in bacterial isolates in order to make predictions regarding their AMR phenotype. However, there are a large number of alternative bioinformatics software and pipelines available, which can lead to dissimilar results. It is, therefore, vital that researchers carefully evaluate their genomic and metagenomic AMR analysis methods using a common dataset. To this end, as part of the Microbial Bioinformatics Hackathon and Workshop 2021, a ‘gold standard’ reference genomic and simulated metagenomic dataset was generated containing raw sequence reads mapped against their corresponding reference genome from a range of 174 potentially pathogenic bacteria. These datasets and their accompanying metadata are freely available for use in benchmarking studies of bacteria and their antimicrobial resistance genes and will help improve tool development for the identification of AMR genes in complex samples.
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spelling pubmed-92007082022-06-17 Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing Raphenya, Amogelang R. Robertson, James Jamin, Casper de Oliveira Martins, Leonardo Maguire, Finlay McArthur, Andrew G. Hays, John P. Sci Data Data Descriptor Whole genome sequencing (WGS) is a key tool in identifying and characterising disease-associated bacteria across clinical, agricultural, and environmental contexts. One increasingly common use of genomic and metagenomic sequencing is in identifying the type and range of antimicrobial resistance (AMR) genes present in bacterial isolates in order to make predictions regarding their AMR phenotype. However, there are a large number of alternative bioinformatics software and pipelines available, which can lead to dissimilar results. It is, therefore, vital that researchers carefully evaluate their genomic and metagenomic AMR analysis methods using a common dataset. To this end, as part of the Microbial Bioinformatics Hackathon and Workshop 2021, a ‘gold standard’ reference genomic and simulated metagenomic dataset was generated containing raw sequence reads mapped against their corresponding reference genome from a range of 174 potentially pathogenic bacteria. These datasets and their accompanying metadata are freely available for use in benchmarking studies of bacteria and their antimicrobial resistance genes and will help improve tool development for the identification of AMR genes in complex samples. Nature Publishing Group UK 2022-06-15 /pmc/articles/PMC9200708/ /pubmed/35705638 http://dx.doi.org/10.1038/s41597-022-01463-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Raphenya, Amogelang R.
Robertson, James
Jamin, Casper
de Oliveira Martins, Leonardo
Maguire, Finlay
McArthur, Andrew G.
Hays, John P.
Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing
title Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing
title_full Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing
title_fullStr Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing
title_full_unstemmed Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing
title_short Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing
title_sort datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200708/
https://www.ncbi.nlm.nih.gov/pubmed/35705638
http://dx.doi.org/10.1038/s41597-022-01463-7
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