PanRes - Collection of antimicrobial resistance genes
Creators
- 1. Technical University of Denmark
Description
PanRes database of antimicrobial resistance genes
Many different collections of antimicrobial resistance genes (ARGs) have been collected and used for various purposes. In order to develop a workflow for mass screening of public metagenomes, we recently gathered up and filtered in a number of these gene collections to produce PanRes.
For details, please see the methods section in the following publication:
"ARGfinder - a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets" (Unpublished, submitted)
Briefly, the PanRes gene collection is gathered from a combination of other resistance gene collections into one, so each unique sequence has an "pan_" identifier (PanRes_genes). A separate table (PanRes_data) provides an overview of all the genes, their origin database and which genes cluster together in high-identity clusters.
A number of previously published collections of ARGs were used in the creation of PanRes (See references):
ResFinder (downloaded 2023-01-20, (Bortolaia et al. 2020)),
ResFinderFG (version 2.0, (Gschwind et al. 2023))
CARD (version 3.2.5, (Alcock et al. 2023))
MegaRes (version 3.0.0, (Bonin et al. 2023))
AMRFinderPlus (version 3.11/2022-12-19.1, (Feldgarden et al. 2021))
ARGANNOT (V6_July2019, (Gupta et al. 2014))
The 'CsabaPal' collection (Provided by Csaba Pál and Zoltán Farkas in November 2022, Daruka et al. 2023))
BacMet (version 1.1, (Pal et al. 2014))
Files
README.md
Additional details
References
- Alcock BP, Huynh W, Chalil R et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res 2023;51:D690–9.
- Bonin N, Doster E, Worley H et al. MEGARes and AMR++, v3.0: an updated comprehensive database of antimicrobial resistance determinants and an improved software pipeline for classification using high-throughput sequencing. Nucleic Acids Res 2023;51:D744–52.
- Bortolaia V, Kaas RS, Ruppe E et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother 2020;75:3491–500.
- Daruka L, Czikkely MS, Szili P et al. Antibiotics of the future are prone to resistance in Gram-negative pathogens. Revis 2023.
- Feldgarden M, Brover V, Gonzalez-Escalona N et al. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep 2021;11, DOI: 10.1038/s41598-021-91456-0.
- Gschwind R, Ugarcina Perovic S, Weiss M et al. ResFinderFG v2.0: a database of antibiotic resistance genes obtained by functional metagenomics. Nucleic Acids Res 2023:gkad384.
- Gupta SK, Padmanabhan BR, Diene SM et al. ARG-ANNOT, a New Bioinformatic Tool To Discover Antibiotic Resistance Genes in Bacterial Genomes. Antimicrob Agents Chemother 2014;58:212–20.
- Pal C, Bengtsson-Palme J, Rensing C et al. BacMet: antibacterial biocide and metal resistance genes database. Nucleic Acids Res 2014;42:D737–43.