ArticlePLoS computational biology2023
Ten simple rules for the sharing of bacterial genotype-Phenotype data on antimicrobial resistance.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- A comprehensive AMR genotype-phenotype database (CABBAGE).Nucleic acids research · 2026Article
- Mitigating antimicrobial resistance by innovative solutions in AI (MARISA): a modified James Lind Alliance analysis.npj antimicrobials and resistance · 2025Article
- Tools and challenges in the use of routine clinical data for antimicrobial resistance surveillance.npj antimicrobials and resistance · 2025Review
- Complete genome assemblies and antibiograms of 22 Staphylococcus capitis isolates.BMC genomic data · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
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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Registered trials
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