BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows
Nikolaos Strepis,
Dennis Dollee,
Donny Vrins,
Kevin Vanneste,
Bert Bogaerts,
Catherine Carrillo,
Amrita Bharat,
Kristy Horan,
Norelle L. Sherry,
Torsten Seemann,
Benjamin P. Howden,
Saskia Hiltemann,
Leonid Chindelevitch,
Andrew P. Stubbs,
John P. Hays
Affiliations
Nikolaos Strepis
Department of Medical Microbiology and Infectious Diseases, Erasmus University Medical Centre (Erasmus MC)
Dennis Dollee
Department of Pathology and Clinical Bioinformatics, Erasmus University Medical Centre (Erasmus MC)
Donny Vrins
Department of Pathology and Clinical Bioinformatics, Erasmus University Medical Centre (Erasmus MC)
Kevin Vanneste
Transversal activities in Applied Genomics
Bert Bogaerts
Transversal activities in Applied Genomics
Catherine Carrillo
Canadian Food Inspection Agency
Amrita Bharat
National Microbiology Laboratory, Public Health Agency of Canada
Kristy Horan
Microbiological Diagnostic Unit Public Health Laboratory (MDU-PHL), Department of Microbiology & Immunology, University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Norelle L. Sherry
Microbiological Diagnostic Unit Public Health Laboratory (MDU-PHL), Department of Microbiology & Immunology, University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Torsten Seemann
Microbiological Diagnostic Unit Public Health Laboratory (MDU-PHL), Department of Microbiology & Immunology, University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Benjamin P. Howden
Microbiological Diagnostic Unit Public Health Laboratory (MDU-PHL), Department of Microbiology & Immunology, University of Melbourne at the Peter Doherty Institute for Infection & Immunity
Saskia Hiltemann
Institute of Pharmaceutical Sciences, Faculty of Chemistry and Pharmacy, University of Freiburg
Leonid Chindelevitch
MRC Centre for Global Infectious Disease Analysis, Imperial College London
Andrew P. Stubbs
Department of Medical Microbiology and Infectious Diseases, Erasmus University Medical Centre (Erasmus MC)
John P. Hays
Department of Medical Microbiology and Infectious Diseases, Erasmus University Medical Centre (Erasmus MC)
Abstract Background The Joint Programming Initiative on Antimicrobial Resistance (JPIAMR) networks ‘Seq4AMR’ and ‘B2B2B AMR Dx’ were established to promote collaboration between microbial whole genome sequencing (WGS) and antimicrobial resistance (AMR) stakeholders. A key topic discussed was the frequent variability in results obtained between different microbial WGS-related AMR gene prediction workflows. Further, comparative benchmarking studies are difficult to perform due to differences in AMR gene prediction accuracy and a lack of agreement in the naming of AMR genes (semantic conformity) for the results obtained. To illustrate this problem, and as a capacity-building exercise to encourage stakeholder involvement, a comparative Galaxy-based BenchAMRking platform was developed and validated using datasets from bacterial species with PCR-verified AMR gene presence or absence information from abritAMR. Results The Galaxy-based BenchAMRking platform ( https://erasmusmc-bioinformatics.github.io/benchAMRking/ ) specifically focusses on the steps involved in identifying AMR genes from raw reads and sequence assemblies. The platform currently comprises four well-characterised and published workflows that have previously been used to identify AMR genes using WGS data from several different bacterial species. These four workflows, which include the ISO certified abritAMR workflow, make use of different computational tools (or tool versions), and interrogate different AMR gene sequence databases. By utilising their own data, users can investigate potential AMR gene-calling problems associated with their own in silico workflows/protocols, with a potential use case outlined in this publication. Conclusions BenchAMRking is a Galaxy-based comparison platform where users can access, visualise, and explore some of the major discrepancies associated with AMR gene prediction from microbial WGS data.
