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ARG databases

short description

Databases for antibiotic resistance genes (ARG) provide data on currently known genetic determinants of AMR while also offering sequence analysis and annotation. The quality of any project involving sequencing of microbiomes for the purpose of analyzing the resistomes is highly dependent on the quality of ARG databases available. Information related to function and phenotypical organization of each of these genes is mined from the available literature.

Over the years there have been a number of different databases have become available, and their focus can either be a very broad range of antibiotics or a more narrow/specific field. The first curated databased launched was the Antibiotic Resistance Genes Database (ARDB). Now it is no longer maintained, but all of its information has been transferred to the Comprehensive Antibiotic Resistance Database (CARD), which is being actively maintained and updated. On this note, we invite you to watch the presentation by Prof. Andrew G. McArthur, the creator of CARD, on the how the database is constructed, the challenges, and future perspectives. We encourage you to watch the whole presentation, but a particular focus between 4:30 and 8:30 is welcome to understand how CARD is structured. Click HERE to go to the presentation.

In addition to ARBD and CARD, ResistoXplorer supports other seven databases which are listed below together with their documentation:

  • CARD: CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database
  • ResFinder:
    Identification of acquired antimicrobial resistance genes
  • MEGARes:
    MEGARes 2.0: a database for classification of antimicrobial drug, biocide and metal resistance determinants in metagenomic sequence data
  • AMRFinder:
    Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates
  • SARG:
    ARGs-OAP v2.0 with an expanded SARG database and Hidden Markov Models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes
  • DeepARG-DB:
    DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
  • ARGminer:
    ARGminer: a web platform for the crowdsourcing-based curation of antibiotic resistance genes
  • ARDB:
    ARDB–Antibiotic Resistance Genes Database
  • ARG-ANNOT:
    ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes

It is important to note that the information that can be obtained and visualized in ResistoXplorer is dependent on the database of choice – information for ARGs varies significantly across them. Two commonly faced issues are lack of curation and inconsistent nomenclature strategies. Overall, researchers are encouraged to use multiple databases to obtain more complete data in their projects.

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Exploring the Landscape of Antibiotic Resistance in Microbiomes

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