BibTex format
@article{Strepis:2025:10.1186/s12864-024-11158-5,
author = {Strepis, N and Dollee, D and Vrins, D and Vanneste, K and Bogaerts, B and Carrillo, C and Bharat, A and Horan, K and Sherry, NL and Seemann, T and Howden, BP and Hiltemann, S and Chindelevitch, L and Stubbs, AP and Hays, JP},
doi = {10.1186/s12864-024-11158-5},
journal = {BMC Genomics},
title = {BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows},
url = {http://dx.doi.org/10.1186/s12864-024-11158-5},
volume = {26},
year = {2025}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - 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.
AU - Strepis,N
AU - Dollee,D
AU - Vrins,D
AU - Vanneste,K
AU - Bogaerts,B
AU - Carrillo,C
AU - Bharat,A
AU - Horan,K
AU - Sherry,NL
AU - Seemann,T
AU - Howden,BP
AU - Hiltemann,S
AU - Chindelevitch,L
AU - Stubbs,AP
AU - Hays,JP
DO - 10.1186/s12864-024-11158-5
PY - 2025///
SN - 1471-2164
TI - BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows
T2 - BMC Genomics
UR - http://dx.doi.org/10.1186/s12864-024-11158-5
UR - https://doi.org/10.1186/s12864-024-11158-5
VL - 26
ER -