Benchmarking nanopore-based strategies for antimicrobial resistance prediction
In brief
Nanopore predictions reached 80% balanced accuracy but missed lab-detected resistance
In 201 samples spanning 27 bacterial species, the best nanopore-based resistance prediction tools achieved 80% balanced accuracy, but still missed some resistance found by laboratory antimicrobial susceptibility testing. Assembly strategy and data volume affected performance, while basecalling accuracy mode did not significantly change it. These predictions can supplement lab testing, but cannot yet replace it.
- Journal
- Microbial genomics (Q1)
- Published
- 1 November 2026
- Study design
- Unclassified
- Evidence level
- Level 5, Expert Opinion (CEBM 5)
- Authors
- Natalie Ring, Alison S Low, Rhodri Evans, Marianne Keith, Gavin K Paterson, David Gally, et al.
- PMID
- 42975948
- DOI
- 10.1099/mgen.0.001885
Why clinicians should know about it
- Picked for Microbiology (medical) (top studies of the week, 8 November 2026): Benchmarks nanopore sequencing approaches for AMR prediction
Abstract
Antimicrobial resistance (AMR) presents a pressing need to ensure that the right antimicrobials are used to target the right microbes at the right time. Ideally, the appropriate antimicrobial is selected after patient samples have been cultured and assessed with antimicrobial sensitivity testing (AST). However, the time needed for culture-based diagnosis leads to immediate empirical treatment, often with broad-spectrum and/or high-tier antimicrobials. Direct nanopore metagenomic whole-genome sequencing to identify pathogens and predict their AMR is a rapid and patient-side alternative. A limitation of this approach is potential inconsistencies in i predicted AMR phenotypes. Here, we benchmarked the current performance of in silico AMR prediction strategies for nanopore-generated long-read data. Using real-world nanopore data paired with AST phenotyping for 201 samples representing 27 bacterial species, we assessed the impact of basecalling mode, data volume and assembly strategy and compared the performance of eight in silico AMR prediction tools with seven AMR databases. We found that basecalling accuracy mode does not significantly affect the overall accuracy of in silico AMR predictions but assembly strategy and data volume both do. Prediction tools using the ResFinder database scored best for balanced accuracy (0.80±0.02 for both ResFinder and ABRicate), whilst DeepARG scored best for sensitivity (0.65±0.03); predictions were more accurate for some antibiotic classes and genera than others. However, even the best-performing in silico AMR prediction strategy missed some resistance identified by lab-based AST. We conclude, therefore, that currently in silico AMR prediction can supplement lab-based AST but cannot yet replace it.
Abstract as published, via PubMed.
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