Evidence map›Paper›PMID 37347768›Full record

ArticlePLoS computational biology2023

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

Leonid Chindelevitch, Maarten van Dongen, Heather Graz, Antonio Pedrotta, Anita Suresh, Swapna Uplekar, Elita Jauneikaite, Nicole Wheeler

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Leonid ChindelevitchMRC Centre for Global Infectious Disease Analysis, Imperial College, London, England, United Kingdom.ORCID 0000-0002-6619-6013
Maarten van DongenAMR Insights, Amsterdam, the Netherlands.
Heather GrazBiophys Ltd, Usk, Wales, United Kingdom.
Antonio PedrottaFIND, the global alliance for diagnostics, Geneva, Switzerland.
Anita SureshFIND, the global alliance for diagnostics, Geneva, Switzerland.
Swapna UplekarFIND, the global alliance for diagnostics, Geneva, Switzerland.
Elita JauneikaiteMRC Centre for Global Infectious Disease Analysis, Imperial College, London, England, United Kingdom.ORCID 0000-0002-7075-6896
Nicole WheelerInstitute of Microbiology and Infection, University of Birmingham, Birmingham, England, United Kingdom.ORCID 0000-0003-4599-9164

Funding

Medical Research Council MR/R015600/1Medical Research Council MR/X036936/1
6 · The paper itself

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.

Indexed as

Anti-Bacterial AgentsAnti-Infective AgentsBacteriaDrug Resistance, BacterialGenome, MicrobialGenotypeHumansPhenotypeAnti-Bacterial AgentsAnti-Infective Agents

Identifiers

PMID37347768
PMCPMC10286994

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.