A systematic comparison of tools for predicting antimicrobial resistance from nanopore sequence data

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 the pathogens and predict their antimicrobial resistance is a rapid and patient-side alternative. A limitation of this approach is the inconsistency of in silico predicted AMR phenotypes. Here, we benchmarked the current performance of in silico AMR prediction strategies for nanopore-generated long read data. Using nanopore data paired with AST phenotyping for 201 samples, we assessed the impact of basecalling mode, data volume, and assembly strategy to compare the performance of eight in silico AMR prediction tools with seven AMR databases. We found that basecalling accuracy mode does not 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 (0.8 for both ResFinder and ABRicate), whilst DeepARG scored best for sensitivity (0.65). However, even the best performing in silico AMR prediction strategy missed some resistance identified by lab-based AST. In silico AMR prediction can therefore supplement lab-based AST, but cannot yet replace it.