TY - JOUR A1 - Petrillo, Mauro A1 - Fabbri, M A1 - Kagkli, Dafni Maria A1 - Querci, Maddalena A1 - Van den Eede, Guy A1 - Alm, Erik A1 - Aytan-Aktug, Derya A1 - Capella-Gutierrez, Salvador A1 - Carrillo, Catherine A1 - Cestaro, Alessandro A1 - Chan, Kok-Gan A1 - Coque, Teresa A1 - Endrullat, Christoph A1 - Gut, Ivo A1 - Hammer, Paul A1 - Kay, Gemma L. A1 - Madec, Jean-Yves A1 - Mather, Alison E. A1 - McHardy, Alice Carolyn A1 - Naas, Thierry A1 - Paracchini, Valentina A1 - Peter, Silke A1 - Pightling, Arthur A1 - Raffael, Barbara A1 - Rossen, John A1 - Ruppé, Etienne A1 - Schlaberg, Robert A1 - Vanneste, Kevin A1 - Weber, Lukas M. A1 - Westh, Henrik A1 - Angers-Loustau, Alexandre T1 - A roadmap for the generation of benchmarking resources for antimicrobial resistance detection using next generation sequencing [version 1; peer review: 2 approved with reservations] JF - F1000Research N2 - Next Generation Sequencing technologies significantly impact the field of Antimicrobial Resistance (AMR) detection and monitoring, with immediate uses in diagnosis and risk assessment. For this application and in general, considerable challenges remain in demonstrating sufficient trust to act upon the meaningful information produced from raw data, partly because of the reliance on bioinformatics pipelines, which can produce different results and therefore lead to different interpretations. With the constant evolution of the field, it is difficult to identify, harmonise and recommend specific methods for large-scale implementations over time. In this article, we propose to address this challenge through establishing a transparent, performance-based, evaluation approach to provide flexibility in the bioinformatics tools of choice, while demonstrating proficiency in meeting common performance standards. The approach is two-fold: first, a community-driven effort to establish and maintain “live” (dynamic) benchmarking platforms to provide relevant performance metrics, based on different use-cases, that would evolve together with the AMR field; second, agreed and defined datasets to allow the pipelines’ implementation, validation, and quality-control over time. Following previous discussions on the main challenges linked to this approach, we provide concrete recommendations and future steps, related to different aspects of the design of benchmarks, such as the selection and the characteristics of the datasets (quality, choice of pathogens and resistances, etc.), the evaluation criteria of the pipelines, and the way these resources should be deployed in the community. KW - antimicrobial resistance KW - bioinformatics KW - next-generation sequencing KW - benchmarking Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-15699 ER -