The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 3 of 126
Back to Result List

A roadmap for the generation of benchmarking resources for antimicrobial resistance detection using next generation sequencing [version 1; peer review: 2 approved with reservations]

  • 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.

Export metadata

Additional Services

Search Google Scholar Statistics
Metadaten
Author:Mauro Petrillo, M Fabbri, Dafni Maria Kagkli, Maddalena Querci, Guy Van den Eede, Erik Alm, Derya Aytan-Aktug, Salvador Capella-Gutierrez, Catherine Carrillo, Alessandro Cestaro, Kok-Gan Chan, Teresa Coque, Christoph Endrullat, Ivo Gut, Paul Hammer, Gemma L. Kay, Jean-Yves Madec, Alison E. Mather, Alice Carolyn McHardy, Thierry Naas, Valentina Paracchini, Silke Peter, Arthur Pightling, Barbara Raffael, John Rossen, Etienne Ruppé, Robert Schlaberg, Kevin Vanneste, Lukas M. Weber, Henrik Westh, Alexandre Angers-Loustau
URN:urn:nbn:de:kobv:526-opus4-15699
DOI (Version of Record):https://doi.org/10.12688/f1000research.39214.1
Parent Title (English):F1000Research
Document Type:Article
Language:English
Year of Publication:2021
Publishing Institution:Technische Hochschule Wildau
Release Date:2022/02/15
Tag:antimicrobial resistance; benchmarking; bioinformatics; next-generation sequencing
Article Number:10:80
Source:Petrillo M, Fabbri M, Kagkli DM et al. A roadmap for the generation of benchmarking resources for antimicrobial resistance detection using next generation sequencing [version 1; peer review: 2 approved with reservations]. F1000Research 2021, 10:80 (https://doi.org/10.12688/f1000research.39214.1)
Faculties an central facilities:Fachbereich Ingenieur- und Naturwissenschaften
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Verstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.