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Ten simple rules for the sharing of bacterial genotype—Phenotype data on antimicrobial resistance

The increasing availability of high-throughput sequencing (frequently termed next-generation sequencing (NGS)) data has created opportunities to gain deeper insights into the mechanisms of a number of diseases and is already impacting many areas of medicine and public health. The area of infectious...

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Autores principales: Chindelevitch, Leonid, van Dongen, Maarten, Graz, Heather, Pedrotta, Antonio, Suresh, Anita, Uplekar, Swapna, Jauneikaite, Elita, Wheeler, Nicole
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10286994/
https://www.ncbi.nlm.nih.gov/pubmed/37347768
http://dx.doi.org/10.1371/journal.pcbi.1011129
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author Chindelevitch, Leonid
van Dongen, Maarten
Graz, Heather
Pedrotta, Antonio
Suresh, Anita
Uplekar, Swapna
Jauneikaite, Elita
Wheeler, Nicole
author_facet Chindelevitch, Leonid
van Dongen, Maarten
Graz, Heather
Pedrotta, Antonio
Suresh, Anita
Uplekar, Swapna
Jauneikaite, Elita
Wheeler, Nicole
author_sort Chindelevitch, Leonid
collection PubMed
description The increasing availability of high-throughput sequencing (frequently termed next-generation sequencing (NGS)) data has created opportunities to gain deeper insights into the mechanisms of a number of diseases and is already impacting many areas of medicine and public health. The area of infectious diseases stands somewhat apart from other human diseases insofar as the relevant genomic data comes from the microbes rather than their human hosts. A particular concern about the threat of antimicrobial resistance (AMR) has driven the collection and reporting of large-scale datasets containing information from microbial genomes together with antimicrobial susceptibility test (AST) results. Unfortunately, the lack of clear standards or guiding principles for the reporting of such data is hampering the field’s advancement. We therefore present our recommendations for the publication and sharing of genotype and phenotype data on AMR, in the form of 10 simple rules. The adoption of these recommendations will enhance AMR data interoperability and help enable its large-scale analyses using computational biology tools, including mathematical modelling and machine learning. We hope that these rules can shed light on often overlooked but nonetheless very necessary aspects of AMR data sharing and enhance the field’s ability to address the problems of understanding AMR mechanisms, tracking their emergence and spread in populations, and predicting microbial susceptibility to antimicrobials for diagnostic purposes.
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spelling pubmed-102869942023-06-23 Ten simple rules for the sharing of bacterial genotype—Phenotype data on antimicrobial resistance Chindelevitch, Leonid van Dongen, Maarten Graz, Heather Pedrotta, Antonio Suresh, Anita Uplekar, Swapna Jauneikaite, Elita Wheeler, Nicole PLoS Comput Biol Education The increasing availability of high-throughput sequencing (frequently termed next-generation sequencing (NGS)) data has created opportunities to gain deeper insights into the mechanisms of a number of diseases and is already impacting many areas of medicine and public health. The area of infectious diseases stands somewhat apart from other human diseases insofar as the relevant genomic data comes from the microbes rather than their human hosts. A particular concern about the threat of antimicrobial resistance (AMR) has driven the collection and reporting of large-scale datasets containing information from microbial genomes together with antimicrobial susceptibility test (AST) results. Unfortunately, the lack of clear standards or guiding principles for the reporting of such data is hampering the field’s advancement. We therefore present our recommendations for the publication and sharing of genotype and phenotype data on AMR, in the form of 10 simple rules. The adoption of these recommendations will enhance AMR data interoperability and help enable its large-scale analyses using computational biology tools, including mathematical modelling and machine learning. We hope that these rules can shed light on often overlooked but nonetheless very necessary aspects of AMR data sharing and enhance the field’s ability to address the problems of understanding AMR mechanisms, tracking their emergence and spread in populations, and predicting microbial susceptibility to antimicrobials for diagnostic purposes. Public Library of Science 2023-06-22 /pmc/articles/PMC10286994/ /pubmed/37347768 http://dx.doi.org/10.1371/journal.pcbi.1011129 Text en © 2023 Chindelevitch et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Education
Chindelevitch, Leonid
van Dongen, Maarten
Graz, Heather
Pedrotta, Antonio
Suresh, Anita
Uplekar, Swapna
Jauneikaite, Elita
Wheeler, Nicole
Ten simple rules for the sharing of bacterial genotype—Phenotype data on antimicrobial resistance
title Ten simple rules for the sharing of bacterial genotype—Phenotype data on antimicrobial resistance
title_full Ten simple rules for the sharing of bacterial genotype—Phenotype data on antimicrobial resistance
title_fullStr Ten simple rules for the sharing of bacterial genotype—Phenotype data on antimicrobial resistance
title_full_unstemmed Ten simple rules for the sharing of bacterial genotype—Phenotype data on antimicrobial resistance
title_short Ten simple rules for the sharing of bacterial genotype—Phenotype data on antimicrobial resistance
title_sort ten simple rules for the sharing of bacterial genotype—phenotype data on antimicrobial resistance
topic Education
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10286994/
https://www.ncbi.nlm.nih.gov/pubmed/37347768
http://dx.doi.org/10.1371/journal.pcbi.1011129
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