Benchmarking nanopore-based strategies for antimicrobial resistance prediction
- 1The Roslin Institute, University of Edinburgh, Edinburgh, UK
- 2The Royal (Dick) School of Veterinary Studies and the Roslin Institute, University of Edinburgh, Edinburgh, UK
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.
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BibTeX
@article{ring2026benchmarking,
title = {{Benchmarking nanopore-based strategies for antimicrobial resistance prediction}},
author = {Natalie Ring and Alison S. Low and Rhodri Evans and Marianne Keith and Gavin K. Paterson and David Gally and Tim Nuttall and Dylan Neil Clements and J. Ross Fitzgerald},
journal = {Microbial Genomics},
year = {2026},
month = nov,
doi = {10.1099/mgen.0.001885},
url = {https://doi.org/10.1099/mgen.0.001885},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Benchmarking nanopore-based strategies for antimicrobial resistance prediction
AU - Natalie Ring
AU - Alison S. Low
AU - Rhodri Evans
AU - Marianne Keith
AU - Gavin K. Paterson
AU - David Gally
AU - Tim Nuttall
AU - Dylan Neil Clements
AU - J. Ross Fitzgerald
PY - 2026
DA - 2026/11/03
JO - Microbial Genomics
DO - 10.1099/mgen.0.001885
UR - https://doi.org/10.1099/mgen.0.001885
AB - 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.
SN - 2057-5858
ER -