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      CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database

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      Author(s): 1 , 2 , 3 , 1 , 2 , 3 , 2 , 3 , 1 , 2 , 3 , 2 , 4 , 2 , 3 , 2 , 3 , 2 , 4 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 4 , 5 , 2 , 6 , 7 , 2 , 3 , 1 , 2 , 3 , 8 , 1 , 2 , 3 , 9 , 10 , 11 , 10 , 11 , 10 , 9 , 10 , 12 , 13 , 14 , 1 , 2 , 3
      Publication date (Electronic):
      Journal: Nucleic Acids Research
      Publisher: Oxford University Press

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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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          The comprehensive antibiotic resistance database.

          Andrew McArthur, Nicholas Waglechner, Fazmin Nizam … (2013)
          The field of antibiotic drug discovery and the monitoring of new antibiotic resistance elements have yet to fully exploit the power of the genome revolution. Despite the fact that the first genomes sequenced of free living organisms were those of bacteria, there have been few specialized bioinformatic tools developed to mine the growing amount of genomic data associated with pathogens. In particular, there are few tools to study the genetics and genomics of antibiotic resistance and how it impacts bacterial populations, ecology, and the clinic. We have initiated development of such tools in the form of the Comprehensive Antibiotic Research Database (CARD; http://arpcard.mcmaster.ca). The CARD integrates disparate molecular and sequence data, provides a unique organizing principle in the form of the Antibiotic Resistance Ontology (ARO), and can quickly identify putative antibiotic resistance genes in new unannotated genome sequences. This unique platform provides an informatic tool that bridges antibiotic resistance concerns in health care, agriculture, and the environment.
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            Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates

            Michael Feldgarden, Vyacheslav V. Brover, Daniel H. Haft … (2019)
            Antimicrobial resistance (AMR) is a major public health problem that requires publicly available tools for rapid analysis. To identify AMR genes in whole-genome sequences, the National Center for Biotechnology Information (NCBI) has produced AMRFinder, a tool that identifies AMR genes using a high-quality curated AMR gene reference database. The Bacterial Antimicrobial Resistance Reference Gene Database consists of up-to-date gene nomenclature, a set of hidden Markov models (HMMs), and a curated protein family hierarchy. Currently, it contains 4,579 antimicrobial resistance proteins and more than 560 HMMs. Here, we describe AMRFinder and its associated database. To assess the predictive ability of AMRFinder, we measured the consistency between predicted AMR genotypes from AMRFinder and resistance phenotypes of 6,242 isolates from the National Antimicrobial Resistance Monitoring System (NARMS). This included 5,425 Salmonella enterica , 770 Campylobacter spp., and 47 Escherichia coli isolates phenotypically tested against various antimicrobial agents. Of 87,679 susceptibility tests performed, 98.4% were consistent with predictions. To assess the accuracy of AMRFinder, we compared its gene symbol output with that of a 2017 version of ResFinder, another publicly available resistance gene detection system. Most gene calls were identical, but there were 1,229 gene symbol differences (8.8%) between them, with differences due to both algorithmic differences and database composition. AMRFinder missed 16 loci that ResFinder found, while ResFinder missed 216 loci that AMRFinder identified. Based on these results, AMRFinder appears to be a highly accurate AMR gene detection system.
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              ChEBI in 2016: Improved services and an expanding collection of metabolites

              Janna Hastings, Gareth S. Owen, Adriano Dekker … (2016)
              ChEBI is a database and ontology containing information about chemical entities of biological interest. It currently includes over 46 000 entries, each of which is classified within the ontology and assigned multiple annotations including (where relevant) a chemical structure, database cross-references, synonyms and literature citations. All content is freely available and can be accessed online at http://www.ebi.ac.uk/chebi. In this update paper, we describe recent improvements and additions to the ChEBI offering. We have substantially extended our collection of endogenous metabolites for several organisms including human, mouse, Escherichia coli and yeast. Our front-end has also been reworked and updated, improving the user experience, removing our dependency on Java applets in favour of embedded JavaScript components and moving from a monthly release update to a ‘live’ website. Programmatic access has been improved by the introduction of a library, libChEBI, in Java, Python and Matlab. Furthermore, we have added two new tools, namely an analysis tool, BiNChE, and a query tool for the ontology, OntoQuery.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date (Print): 08 January 2020
                Publication date (Electronic): 29 October 2019
                Publication date PMC-release: 29 October 2019
                Volume: 48
                Issue: D1
                Pages: D517-D525
                Affiliations
                [1 ] David Braley Centre for Antibiotic Discovery, McMaster University , Hamilton, Ontario, L8S 4K1, Canada
                [2 ] M.G. DeGroote Institute for Infectious Disease Research, McMaster University , Hamilton, Ontario, L8S 4K1, Canada
                [3 ] Department of Biochemistry and Biomedical Science, McMaster University , Hamilton, Ontario, L8S 4K1, Canada
                [4 ] Bachelor of Health Sciences Program, McMaster University , Hamilton, Ontario, L8S 4K1, Canada
                [5 ] Honours Biology Program, McMaster University , Hamilton, Ontario, L8S 4K1, Canada
                [6 ] Bachelor of Arts & Science Program, McMaster University , Hamilton, Ontario, L8S 4K1, Canada
                [7 ] Center for Genome Sciences, National Autonomous University of Mexico , Cuernavaca, Morelos 62210, Mexico
                [8 ] Department of Genetics, Harvard Medical School, Harvard University , Boston, MA 02115, USA
                [9 ] Department of Pathology and Laboratory Medicine, University of British Columbia , Vancouver, V6T 2B5, British Columbia, Canada
                [10 ] Department of Molecular Biology and Biochemistry, Simon Fraser University , Burnaby, British Columbia, V5A 1S6, Canada
                [11 ] Faculty of Computer Science, Dalhousie University , Halifax, Nova Scotia, B3H 1W5, Canada
                [12 ] British Columbia Centre for Disease Control Public Health Laboratory , Vancouver, British Columbia, V5Z 4R4, Canada
                [13 ] National Microbiology Laboratory, Public Health Agency of Canada , Winnipeg, Manitoba, R3E 3R2, Canada
                [14 ] Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba , Winnipeg, Manitoba, R3E 0J9, Canada
                Author notes
                To whom correspondence should be addressed. Tel: +1 905 525 9140 (Ext. 21663); Fax: +1 905 528 5330; Email: mcarthua@ 123456mcmaster.ca
                Author information
                http://orcid.org/0000-0001-6848-1638
                http://orcid.org/0000-0003-1139-4458
                Article
                Publisher ID: gkz935
                DOI: 10.1093/nar/gkz935
                PMC ID: 7145624
                PubMed ID: 31665441
                SO-VID: d01d70ea-7a3c-4a6e-b765-3a4dda0249cb
                Copyright © © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date accepted : 08 October 2019
                Date revision received : 03 October 2019
                Date received : 23 September 2019
                Page count
                Pages: 9
                Funding
                Funded by: Canadian Institutes of Health Research 10.13039/501100000024
                Award ID: PJT-156214
                Funded by: Genome Canada 10.13039/100008762
                Funded by: Cisco Systems 10.13039/100004351
                Funded by: Cisco Research Chair in Bioinformatics
                Funded by: Ontario Graduate Scholarship
                Categories
                Subject: Database Issue

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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