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

Amogelang R. Raphenya; James Robertson; Casper Jamin; Leonardo de Oliveira Martins; Finlay Maguire; Andrew G. McArthur; John P. Hays

doi:10.1038/s41597-022-01463-7

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

Amogelang R. Raphenya, James Robertson, Casper Jamin, Leonardo de Oliveira Martins, Finlay Maguire, Andrew G. McArthur, John P. Hays

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10 Citas (Scopus)

Resumen

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.

Idioma original	English
Número de artículo	341
Publicación	Scientific data
Volumen	9
N.º	1
DOI	https://doi.org/10.1038/s41597-022-01463-7
Estado	Published - dic. 2022

Nota bibliográfica

Funding Information:
This work was made possible and supported by a collaboration between the Public Health Alliance for Genomic Epidemiology (PHA4GE - https://pha4ge.org), the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR - https://www.jpiamr.eu/) and the MRC Cloud Infrastructure for Microbial Bioinformatics (MRC CLIMB-BD - https://tinyurl.com/climb-movie). We would also like to thank Boas van der Putten (University of Amsterdam) for initial contributions to the work performed in this publication.

Funding Information:
This work was made possible and supported by a collaboration between the Public Health Alliance for Genomic Epidemiology (PHA4GE - https://pha4ge.org ), the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR - https://www.jpiamr.eu/ ) and the MRC Cloud Infrastructure for Microbial Bioinformatics (MRC CLIMB-BD - https://tinyurl.com/climb-movie ). We would also like to thank Boas van der Putten (University of Amsterdam) for initial contributions to the work performed in this publication.

Funding Information:
The authors declare no competing interests. L.de.O.M was funded by the Quadram Institute Bioscience BBSRC funded Core Capability Grant (project number BB/CCG1860/1). A.R.R and A.G.M. were supported by a grant from the Canadian Institutes of Health Research (PJT-156214) and A.G.M. was additionally supported by a David Braley Chair in Computational Biology. F.M. was funded by the Dalhousie University and Sunnybrook Health Sciences Centre. C.J. was funded by the Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Center. J.P.H was funded by a Network Plus 2020 grant from the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR - Seq. 4AMR - ZonMW 549010001) and the Erasmus University Medical Centre Rotterdam (Erasmus MC). No additional funding was required for the work described in this manuscript.

Publisher Copyright:
© 2022, The Author(s).

ASJC Scopus Subject Areas

Statistics and Probability
Information Systems
Education
Computer Science Applications
Statistics, Probability and Uncertainty
Library and Information Sciences

PubMed: MeSH publication types

Dataset
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title = "Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing",

abstract = "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 {\textquoteleft}gold standard{\textquoteright} 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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note = "Funding Information: This work was made possible and supported by a collaboration between the Public Health Alliance for Genomic Epidemiology (PHA4GE - https://pha4ge.org), the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR - https://www.jpiamr.eu/) and the MRC Cloud Infrastructure for Microbial Bioinformatics (MRC CLIMB-BD - https://tinyurl.com/climb-movie). We would also like to thank Boas van der Putten (University of Amsterdam) for initial contributions to the work performed in this publication. Funding Information: This work was made possible and supported by a collaboration between the Public Health Alliance for Genomic Epidemiology (PHA4GE - https://pha4ge.org ), the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR - https://www.jpiamr.eu/ ) and the MRC Cloud Infrastructure for Microbial Bioinformatics (MRC CLIMB-BD - https://tinyurl.com/climb-movie ). We would also like to thank Boas van der Putten (University of Amsterdam) for initial contributions to the work performed in this publication. Funding Information: The authors declare no competing interests. L.de.O.M was funded by the Quadram Institute Bioscience BBSRC funded Core Capability Grant (project number BB/CCG1860/1). A.R.R and A.G.M. were supported by a grant from the Canadian Institutes of Health Research (PJT-156214) and A.G.M. was additionally supported by a David Braley Chair in Computational Biology. F.M. was funded by the Dalhousie University and Sunnybrook Health Sciences Centre. C.J. was funded by the Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Center. J.P.H was funded by a Network Plus 2020 grant from the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR - Seq. 4AMR - ZonMW 549010001) and the Erasmus University Medical Centre Rotterdam (Erasmus MC). No additional funding was required for the work described in this manuscript. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

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AU - Maguire, Finlay

AU - McArthur, Andrew G.

AU - Hays, John P.

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N2 - 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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Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing

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ASJC Scopus Subject Areas

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