An ISO-certified genomics workflow for identification and surveillance of antimicrobial resistance

Norelle L. Sherry, Kristy A. Horan, Susan A. Ballard, Anders Gonҫalves da Silva, Claire L. Gorrie, Mark B. Schultz, Kerrie Stevens, Mary Valcanis, Michelle L. Sait, Timothy P. Stinear, Benjamin P. Howden () and Torsten Seemann
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Norelle L. Sherry: University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Kristy A. Horan: University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Susan A. Ballard: University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Anders Gonҫalves da Silva: University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Claire L. Gorrie: University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Mark B. Schultz: University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Kerrie Stevens: University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Mary Valcanis: University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Michelle L. Sait: University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Timothy P. Stinear: University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Benjamin P. Howden: University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Torsten Seemann: University of Melbourne at the Peter Doherty Institute for Infection & Immunity

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Realising the promise of genomics to revolutionise identification and surveillance of antimicrobial resistance (AMR) has been a long-standing challenge in clinical and public health microbiology. Here, we report the creation and validation of abritAMR, an ISO-certified bioinformatics platform for genomics-based bacterial AMR gene detection. The abritAMR platform utilises NCBI’s AMRFinderPlus, as well as additional features that classify AMR determinants into antibiotic classes and provide customised reports. We validate abritAMR by comparing with PCR or reference genomes, representing 1500 different bacteria and 415 resistance alleles. In these analyses, abritAMR displays 99.9% accuracy, 97.9% sensitivity and 100% specificity. We also compared genomic predictions of phenotype for 864 Salmonella spp. against agar dilution results, showing 98.9% accuracy. The implementation of abritAMR in our institution has resulted in streamlined bioinformatics and reporting pathways, and has been readily updated and re-verified. The abritAMR tool and validation datasets are publicly available to assist laboratories everywhere harness the power of AMR genomics in professional practice.

Date: 2023
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DOI: 10.1038/s41467-022-35713-4

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