BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows
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 fre...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-024-11158-5 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Nikolaos Strepis |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e7d0534cb2a94895a02bc12ace457623 |
| institution | Kabale University |
| issn | 1471-2164 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj-art-e7d0534cb2a94895a02bc12ace4576232025-01-12T12:09:15ZengBMCBMC Genomics1471-21642025-01-012611910.1186/s12864-024-11158-5BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflowsNikolaos Strepis0Dennis Dollee1Donny Vrins2Kevin Vanneste3Bert Bogaerts4Catherine Carrillo5Amrita Bharat6Kristy Horan7Norelle L. Sherry8Torsten Seemann9Benjamin P. Howden10Saskia Hiltemann11Leonid Chindelevitch12Andrew P. Stubbs13John P. Hays14Department of Medical Microbiology and Infectious Diseases, Erasmus University Medical Centre (Erasmus MC)Department of Pathology and Clinical Bioinformatics, Erasmus University Medical Centre (Erasmus MC)Department of Pathology and Clinical Bioinformatics, Erasmus University Medical Centre (Erasmus MC)Transversal activities in Applied GenomicsTransversal activities in Applied GenomicsCanadian Food Inspection AgencyNational Microbiology Laboratory, Public Health Agency of CanadaMicrobiological Diagnostic Unit Public Health Laboratory (MDU-PHL), Department of Microbiology & Immunology, University of Melbourne at the Peter Doherty Institute for Infection & ImmunityMicrobiological Diagnostic Unit Public Health Laboratory (MDU-PHL), Department of Microbiology & Immunology, University of Melbourne at the Peter Doherty Institute for Infection & ImmunityMicrobiological Diagnostic Unit Public Health Laboratory (MDU-PHL), Department of Microbiology & Immunology, University of Melbourne at the Peter Doherty Institute for Infection & ImmunityMicrobiological Diagnostic Unit Public Health Laboratory (MDU-PHL), Department of Microbiology & Immunology, University of Melbourne at the Peter Doherty Institute for Infection & ImmunityInstitute of Pharmaceutical Sciences, Faculty of Chemistry and Pharmacy, University of FreiburgMRC Centre for Global Infectious Disease Analysis, Imperial College LondonDepartment of Medical Microbiology and Infectious Diseases, Erasmus University Medical Centre (Erasmus MC)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.https://doi.org/10.1186/s12864-024-11158-5Antimicrobial resistanceMicrobial whole genome sequencingBenchmarkingGalaxyWorkflows |
| spellingShingle | 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 BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows BMC Genomics Antimicrobial resistance Microbial whole genome sequencing Benchmarking Galaxy Workflows |
| title | BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows |
| title_full | BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows |
| title_fullStr | BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows |
| title_full_unstemmed | BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows |
| title_short | BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows |
| title_sort | benchamrking a galaxy based platform for illustrating the major issues associated with current antimicrobial resistance amr gene prediction workflows |
| topic | Antimicrobial resistance Microbial whole genome sequencing Benchmarking Galaxy Workflows |
| url | https://doi.org/10.1186/s12864-024-11158-5 |
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