BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows
File(s)
Author(s)
Type
Journal Article
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.
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.
Date Issued
2025-01-10
Date Acceptance
2024-12-23
Citation
BMC Genomics, 2025, 26 (1)
ISSN
1471-2164
Publisher
BMC
Journal / Book Title
BMC Genomics
Volume
26
Issue
1
Copyright Statement
© The Author(s) 2025. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Sponsor
Medical Research Council (MRC)
Identifier
https://doi.org/10.1186/s12864-024-11158-5
Grant Number
MR/X020258/1
Subjects
06 Biological Sciences
08 Information and Computing Sciences
11 Medical and Health Sciences
Bioinformatics
Publication Status
Published
Article Number
ARTN 27
Date Publish Online
2025-01-10