PathoGFAIR: a collection of FAIR and adaptable (meta)genomics workflows for (foodborne) pathogens detection and tracking
PathoGFAIR detects the pathogens at species and subspecies taxonomic ranks in all but 1 tested sample, regardless of whether the pathogen is isolated or the sample is incubated before sequencing. Importantly, PathoGFAIR is easy to use and can be straightforwardly adapted and extended for other types of analysis and sequencing techniques, making it usable in various pathogen detection scenarios.
Authors
- Björn Grüning (ORCID: https://orcid.org/0000-0002-3079-6586)
- Bérénice Batut (ORCID: https://orcid.org/0000-0001-9852-1987)
- Anna Henger
- Paul Zierep (ORCID: https://orcid.org/0000-0003-2982-388X)
- Engy Nasr (ORCID: https://orcid.org/0000-0001-9047-4215)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- University of Freiburg (DE)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Bioanalytica (Switzerland) (CH)
- University of Clermont Auvergne (FR)
- Institut Français de Bioinformatique (FR)
- Clermont Université (FR)
Publication Details
- Journal
- GigaScience
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1093/gigascience/giaf017
- Citations
- 2
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.63
Funders
- EOSC-Life
- European Commission
- Agence Nationale de la Recherche
- Bundesministerium für Bildung und Forschung
- Albert-Ludwigs-Universität Freiburg
- German Network for Bioinformatics Infrastructure