CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database
By Baofeng Jia, A. Raphenya, Brian P. Alcock, Nicholas Waglechner, Peiyao Guo, Kara K. Tsang, B. A. Lago, Biren M. Dave, Sheldon K. Pereira, Arjun N. Sharma and 13 more. Nucleic Acids Res., 2016
2,388 citations recorded by the source corpus.
The Comprehensive Antibiotic Resistance Database (CARD; http://arpcard.mcmaster.ca) is a manually curated resource containing high quality reference data on the molecular basis of antimicrobial resistance (AMR), with an emphasis on the genes, proteins and mutations involved in AMR. CARD is ontologically structured, model centric, and spans the breadth of AMR drug classes and resistance mechanisms, including intrinsic, mutation-driven and acquired resistance. It is built upon the Antibiotic Resistance Ontology (ARO), a custom built, interconnected and hierarchical controlled vocabulary allowing advanced data sharing and organization. Its design allows the development of novel genome analysis tools, such as the Resistance Gene Identifier (RGI) for resistome prediction from raw genome sequence. Recent improvements include extensive curation of additional reference sequences and mutations, development of a unique Model Ontology and accompanying AMR detection models to power sequence analysis, new visualization tools, and expansion of the RGI for detection of emergent AMR threats. CARD curation is updated monthly based on an interplay of manual literature curation, computational text mining, and genome analysis.
Subfields: Computational Theory and Mathematics, Molecular Medicine, Pharmacology
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Citations in the corpus
- Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates (2019, methodology): “We also note that the CARD system has recently been released as a software tool (24).”
- IslandViewer 4: expanded prediction of genomic islands for larger-scale datasets (2017, positive): “AMR genes have been newly predicted using RGI with the latest CARD database (6 February 2017) (24), leading to the identification of 10,826 perfect matches to the curated reference sequences of CARD and 166 193 genes with a strict cutoff identifying novel variants of known AMR genes.”
- ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads (2017, methodology): “ARIBA can easily be provided with custom reference sequence-sets, and includes support for a number of public databases: ARGANNOT [6], CARD [10], MEGARes [11], and ResFinder [5].”
- PLSDB: a resource of complete bacterial plasmids (2018, methodology): “The sequences were annotated by performing a BLASTn search for resistance factors from ARG-ANNOT (9), CARD (10) and ResFinder (11) with minimal identity and coverage of 95%, virulence factors from VFDB (12) with minimal identity and coverage of 95%, and replicons from PlasmidFinder (13) using the En”
- MEGARes 2.0: a database for classification of antimicrobial drug, biocide and metal resistance determinants in metagenomic sequence data (2019, methodology): “In this update, we have also incorporated the Resistance Gene Identifier (RGI) tool to confirm that relevant alignments contain the specific SNPs required to confer resistance (37).”
- Antibiotic-Resistance Genes in Waste Water. (2017, methodology): “Also, the HMM (Hidden Markov Model)-based database, Resfams [24], and updated version of the CARD database [28], contain sets of verified genes.”
- CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database (2022, methodology): “While CARD’s curated sequence data can be browsed, downloaded, or searched via BLAST at the CARD website, in our 2017 update (14) we introduced CARD’s Model Ontology, which allows AMR sequence and mutation reference data to be organized by the underlying specific mechanisms of resistance.”
- Antibiotic resistome in the livestock and aquaculture industries: Status and solutions (2020, background): “Over 50% increase of resistance was identified in LMICs with 0.13 to 0.34 in pigs, 0.15 to 0.41 in chickens, and 0.12 to 0.23 in cattle (Jia et al., 2017).”
- RefSeq: an update on prokaryotic genome annotation and curation (2017, methodology): “Resources used during this biocuration effort included ResFams (18), CARD (19), ResFinder (20), among others.”