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

Autor: Chindelevitch L; MRC Centre for Global Infectious Disease Analysis, Imperial College, London, England, United Kingdom., van Dongen M; AMR Insights, Amsterdam, the Netherlands., Graz H; Biophys Ltd, Usk, Wales, United Kingdom., Pedrotta A; FIND, the global alliance for diagnostics, Geneva, Switzerland., Suresh A; FIND, the global alliance for diagnostics, Geneva, Switzerland., Uplekar S; FIND, the global alliance for diagnostics, Geneva, Switzerland., Jauneikaite E; MRC Centre for Global Infectious Disease Analysis, Imperial College, London, England, United Kingdom.; NIHR HPRU in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College, London, England, United Kingdom., Wheeler N; Institute of Microbiology and Infection, University of Birmingham, Birmingham, England, United Kingdom.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2023 Jun 22; Vol. 19 (6), pp. e1011129. Date of Electronic Publication: 2023 Jun 22 (Print Publication: 2023).
DOI: 10.1371/journal.pcbi.1011129
Abstrakt: 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.
Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: AP, AS and SU declare that they are employed by FIND, the global alliance for diagnostics. None of the other authors have anything to declare.
(Copyright: © 2023 Chindelevitch et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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