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Cited by 3,202Antibiotic Resistance in BacteriaAntibiotic Use and ResistanceGenomics and Phylogenetic Studies

CARD 2020: Antibiotic Resistome Surveillance with the Comprehensive Antibiotic Resistance Database

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Alcock, Brian P, Raphenya, Amogelang R, Lau, Tammy T Y, Tsang, Kara K, Bouchard, Mégane, Edalatmand, Arman, Huynh, William, Nguyen, Anna-Lisa V +26 more·2019·Nucleic Acids Research
DOI 10.1093/nar/gkz935PMC PMC7145624
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Abstract

The Comprehensive Antibiotic Resistance Database (CARD; https://card.mcmaster.ca) is a curated resource providing reference DNA and protein sequences, detection models and bioinformatics tools on the molecular basis of bacterial antimicrobial resistance (AMR). CARD focuses on providing high-quality reference data and molecular sequences within a controlled vocabulary, the Antibiotic Resistance Ontology (ARO), designed by the CARD biocuration team to integrate with software development efforts for resistome analysis and prediction, such as CARD’s Resistance Gene Identifier (RGI) software. Since 2017, CARD has expanded through extensive curation of reference sequences, revision of the ontological structure, curation of over 500 new AMR detection models, development of a new classification paradigm and expansion of analytical tools. Most notably, a new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes. By adding these resistance variants to CARD, we are able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants. Here, we describe updates and recent expansions to CARD and its biocuration process, including new resources for community biocuration of AMR molecular reference data.

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  • CARD and ResFinder use carefully curated databases to identify known antibiotic resistance genes, providing clear results but may fail to detect novel, unusual, or modulated resistance mechanisms.

    “In contrast, CARD and ResFinder use carefully curated databases to identify known antibiotic resistance genes, providing clear results but may fail to detect novel, unusual, or modulated resistance mechanisms [67,152,167,168].”

    From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [168]

    The Introduction describes CARD and ResFinder as curated AMR databases, and the Resistance Gene Identifier version 5 section explains how CARD identifies resistance determinants and classifies its results. The limitation is too broad as stated: that section says CARD can detect previously unknown variants and, in Loose mode, potential novel resistance genes. It does identify a narrower limitation—RGI does not scan for frameshifts, deletions or insertions—but does not establish a general inability to detect unusual or modulated mechanisms. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Some AMR determinants in probiotics reflect intrinsic or species-associated background resistance, whereas others are acquired, horizontally transferable and clinically relevant.

    “Some determinants reflect intrinsic or species-associated background resistance, whereas others are acquired, horizontally transferable and clinically relevant.”

    From Integrated Phenotypic and Sequencing-Based Resistome Assessment of Antimicrobial Resistance Determinants in a Sample of Commercial Farm-Animal Probiotic Products (2026) · cites this paper as [13]

    In “Current state of CARD and the ARO” (page not supplied), the paper lists acquired resistance genes and resistant mutations among AMR determinants. It does not establish the claimed distinction between intrinsic or species-associated resistance and horizontally transferable determinants in probiotics. Its discussion of clinical resistance and plasmid-associated genes does not show that the acquired determinants described here are horizontally transferable. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Intrinsic determinants in Enterococcus faecium must be interpreted differently from mobile glycopeptide, oxazolidinone, colistin, ESBL or carbapenemase genes.

    “Intrinsic determinants in Enterococcus faecium, for example, must be interpreted differently from mobile glycopeptide, oxazolidinone, colistin, extended-spectrum β-lactamase (ESBL) or carbapenemase genes [11,12,13,14].”

    From Integrated Phenotypic and Sequencing-Based Resistome Assessment of Antimicrobial Resistance Determinants in a Sample of Commercial Farm-Animal Probiotic Products (2026) · cites this paper as [13]

    In “Expanded and higher resolution AMR detection models” (page not provided), the paper describes different models and interpretive context for resistance genes and variants. Its ARO classifications also distinguish gene families, drug classes and resistance mechanisms. Our copy does not state that intrinsic determinants in Enterococcus faecium must be interpreted differently from the specified mobile genes. (LLM, conf=0.70)

    Experimental — accuracy not yet measured

  • The mere presence of an AMR-associated database hit does not necessarily imply public-health risk.

    “the mere presence of an AMR-associated database hit does not necessarily imply public-health risk.”

    From Integrated Phenotypic and Sequencing-Based Resistome Assessment of Antimicrobial Resistance Determinants in a Sample of Commercial Farm-Animal Probiotic Products (2026) · cites this paper as [13]

  • AI capabilities are fueling probiogenomics, where computational models predict how a probiotic's genome will interact with the host immune system or existing microbiota, ensuring stable colonization without side effects.

    “The growth of AI capabilities is fueling new fields, such as probiogenomics, where computational models are used to predict how a probiotic’s genome will interact with the host immune system or existing microbiota, ensuring stable colonization without side effects [164,169].”

    From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [169]

    The “Resistance gene identifier version 5” section describes computational prediction of antimicrobial-resistance genes from genomic and metagenomic data. Searches for probiogenomics, probiotic–host or microbiota interactions, colonization, and side effects found no passage addressing those claims. No page number was available in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Authors
Alcock, B. P., Raphenya, A. R., Lau, T. T. Y., Tsang, K. K., Bouchard, M., Edalatmand, A., Huynh, W., Nguyen, A.-L. V., Cheng, A. A., Liu, S., Min, S. Y., Miroshnichenko, A., Tran, H.-K., Werfalli, R. E., Nasir, J. A., Oloni, M., Speicher, D. J., Florescu, A., Singh, B., Faltyn, M., Hernandez-Koutoucheva, A., Sharma, A. N., Bordeleau, E., Pawlowski, A. C., Zubyk, H. L., Dooley, D., Griffiths, E., Maguire, F., Winsor, G. L., Beiko, R. G., Brinkman, F. S. L., Hsiao, W. W. L., Domselaar, G. V., & McArthur, A. G.
Affiliations
  • Michael G. DeGroote Institute for Infectious Disease Research (CA)
  • McMaster University (CA)
  • Center for Genome Sciences, National Autonomous University of Mexico, Cuernavaca, Morelos 62210, Mexico
  • Harvard University (US)
  • University of British Columbia (CA)
  • Simon Fraser University (CA)
  • Dalhousie University (CA)
  • Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada
  • Public Health Agency of Canada (CA)
Corresponding author
McArthur, Andrew G
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2019-10-29
Volume
48
Issue
D1
Pages
D517-D525
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkz935
PMC
PMC7145624
OpenAlex
W2982531601
Cited by
3,202
References
3
Citations by year
2026: 231 · 2025: 420 · 2024: 606 · 2023: 667 · 2022: 645 · 2021: 470 · 2020: 156 · 2019: 3 · 2018: 1 · 2012: 1
FWCI
110.13
Type
journal article
Language
en
Open access
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Locations
  • Nucleic Acids Research (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Griffith Research Online (submittedVersion, cc-by)
Funders
  • Canadian Institutes of Health Research (PJT-156214)
  • Genome Canada
  • Cisco Systems
  • Cisco Research Chair in Bioinformatics
  • Ontario Graduate Scholarship
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Medicine › Antibiotic Resistance in Bacteria
  • Life Sciences › Immunology and Microbiology › Applied Microbiology and Biotechnology › Antibiotic Use and Resistance
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
antibiotic resistance, biocuration, resistance gene detection, bacterial antimicrobial resistance
MeSH terms
Bacteria, Bacterial Proteins, Genes, Bacterial, Software, Drug Resistance, Bacterial, Databases, Genetic

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