PlasmidNL: an interactive R/Shiny application for comparative plasmid epidemiology and surveillance-based data exploration
Abstract Summary Plasmids are key drivers of antimicrobial resistance (AMR) dissemination via horizontal gene transfer, yet their epidemiological analysis remains challenging due to fragmented analytical workflows and limited integration of genomic and contextual metadata. We present PlasmidNL, an interactive R/Shiny-based application for comparative plasmid epidemiology. PlasmidNL integrates precomputed plasmid clustering results with plasmid typing outputs (PlasmidFinder and MOB-suite), antimicrobial resistance gene profiles, and epidemiological metadata within a unified analytical environment. The platform enables interactive exploration of plasmid population structure, distribution of resistance determinants, and associated metadata across large datasets. A key feature is the ability to contextualize user-provided datasets against a curated Dutch national surveillance collection comprising 4,596 plasmids from carbapenemase-producing organisms collected between 2012 and 2022. We additionally provide a reproducible preprocessing pipeline that generates standardized input files compatible with the application. PlasmidNL is freely available as both a web-based service and a locally deployable application. Availability and implementation PlasmidNL is implemented in R using the Shiny framework and is accessible online at https://apps.rivm.nl/bsr-ids-ienv/plasmidnl/. Source code is available at https://github.com/gidjes/PlasmidNL under an MIT license.
Authors
- Angela de Haan (ORCID: https://orcid.org/0000-0003-1916-1835)
- Fabian Landman (ORCID: https://orcid.org/0000-0001-6543-836X)
- Sandra Witteveen (ORCID: https://orcid.org/0000-0001-9132-9028)
- Lapo Mughini‐Gras (ORCID: https://orcid.org/0000-0001-5420-949X)
- Antoni P. A. Hendrickx (ORCID: https://orcid.org/0000-0003-4431-9323)
- Anita C. Schürch (ORCID: https://orcid.org/0000-0003-1894-7545)
- Simon Lansu (ORCID: https://orcid.org/0009-0009-7911-5751)
- Jeroen Bos (ORCID: https://orcid.org/0009-0001-3278-2591)
- Gijs Teunis (ORCID: https://orcid.org/0009-0001-8936-5537)
- Doğukan Bayraktar
- Noa van Daatselaar
Institutions
- Utrecht University (NL)
- University Medical Center Utrecht (NL)
- National Institute for Public Health and the Environment (NL)
Publication Details
- Journal
- Bioinformatics Advances
- Published
- 2026-10-29
- DOI
- https://doi.org/10.1093/bioadv/vbag328
- Primary Topic
- Antibiotic Resistance in Bacteria
- Type
- article
- Field-Weighted Citation Impact
- 0.00