Article

hAMRonization: Enhancing antimicrobial resistance prediction using the PHA4GE AMR detection specification and tooling

2024-03-11

Abstract excerpt

The detection of antimicrobial resistance (AMR) markers directly from genomic or metagenomic data is becoming a standard clinical and public health procedure. This has resulted in the development of a number of different bioinformatic AMR prediction tools. Although many may implement similar principles, these tools differ significantly in their supported inputs, search algorithms, parameterisation, and underlying...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 49ba0c80-42bb-55ad-918d-0670aa23d706
DOI: 10.1101/2024.03.07.583950

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

hAMRonization: Enhancing antimicrobial resistance prediction using the PHA4GE AMR detection specification and toolingDOI 10.1101/2024.03.07.583950Read

Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencingSemantic proximity Read

amR: an R package suite to predict antimicrobial resistance in bacterial pathogensSemantic proximity Read

Bridging the gap between bioinformatics and the clinical and public health microbiology laboratory: An ISO-accredited genomics workflow for antimicrobial resistanceSemantic proximity Read

A systematic comparison of tools for predicting antimicrobial resistance from nanopore sequence dataSemantic proximity Read

Genomic and Machine Learning Approaches for Predicting Antimicrobial Resistance: A One Health Scoping Review in Low- and Middle-Income CountriesSemantic proximity Read

ResFinder - an open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes.Semantic proximity Read

BIOTIA-DX RESISTANCE Achieved the Best Antimicrobial Resistance Phenotype Prediction Accuracy at CAMDA 2026Semantic proximity Read

A systematic analysis of machine learning pipelines for robust antimicrobial resistance predictionSemantic proximity Read

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes.Semantic proximity Read

Keeping up with the pathogens: Improved antimicrobial resistance detection and prediction from<i>Pseudomonas</i>aeruginosa genomesSemantic proximity Read

ARIBA: rapid antimicrobial resistance genotyping directly from sequencing readsSemantic proximity Read

A One Health perspective on the use of genotypic methods for antimicrobial resistance prediction.Semantic proximity Read