Evidence map›Paper›PMID 37552534›Full record

ArticleMicrobial genomics2023

Drug resistance prediction for

Michael B Hall, Leandro Lima, Lachlan J M Coin, Zamin Iqbal

Abstract read
In one paragraph

Article in Microbial genomics, 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. Frontiers in veterinary science · 2026
    Article
  3. Article
  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

4 authors.

Michael B HallEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridgeshire, UK.
Leandro LimaEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridgeshire, UK.
Lachlan J M CoinDepartment of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Australia.
Zamin IqbalEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridgeshire, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis is a global pandemic disease with a rising burden of antimicrobial resistance. As a result, the World Health Organization (WHO) has a goal of enabling universal access to drug susceptibility testing (DST). Given the slowness of and infrastructure requirements for phenotypic DST, whole-genome sequencing, followed by genotype-based prediction of DST, now provides a route to achieving this. Since a central component of genotypic DST is to detect the presence of any known resistance-causing mutations, a natural approach is to use a reference graph that allows encoding of known variation. We have developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a

Indexed as

Mycobacterium tuberculosisTuberculosisTuberculosis, Multidrug-ResistantAntitubercular AgentsDrug Resistance, Multiple, BacterialHumansMicrobial Sensitivity TestsAntitubercular Agentsbenchmarkdrug resistance predictiongenome graphsMycobacterium tuberculosisreference graphssoftware

Identifiers

PMID37552534
PMCPMC10483414

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.