RESUMO
Scientific literature is published at a rate that makes manual data extraction a highly time-consuming task. The Comprehensive Antibiotic Resistance Database (CARD) utilizes literature to curate information on antimicrobial resistance genes and to enable time-efficient triage of publications we have developed a classification algorithm for identifying publications describing first reports of new resistance genes. Trained on publications contained in the CARD, CARD*Shark downloads, processes and identifies publications recently added to PubMed that should be reviewed by biocurators. With CARD*Shark, we can minimize the monthly scope of articles a biocurator reviews from hundreds of articles to a few dozen, drastically improving the speed of curation while ensuring no relevant publications are overlooked. Database URL http://card.mcmaster.ca.
Assuntos
Algoritmos , Publicações , Bases de Dados Factuais , Resistência Microbiana a Medicamentos/genética , PubMed , Mineração de Dados , Curadoria de DadosRESUMO
The Comprehensive Antibiotic Resistance Database (CARD; card.mcmaster.ca) combines the Antibiotic Resistance Ontology (ARO) with curated AMR gene (ARG) sequences and resistance-conferring mutations to provide an informatics framework for annotation and interpretation of resistomes. As of version 3.2.4, CARD encompasses 6627 ontology terms, 5010 reference sequences, 1933 mutations, 3004 publications, and 5057 AMR detection models that can be used by the accompanying Resistance Gene Identifier (RGI) software to annotate genomic or metagenomic sequences. Focused curation enhancements since 2020 include expanded ß-lactamase curation, incorporation of likelihood-based AMR mutations for Mycobacterium tuberculosis, addition of disinfectants and antiseptics plus their associated ARGs, and systematic curation of resistance-modifying agents. This expanded curation includes 180 new AMR gene families, 15 new drug classes, 1 new resistance mechanism, and two new ontological relationships: evolutionary_variant_of and is_small_molecule_inhibitor. In silico prediction of resistomes and prevalence statistics of ARGs has been expanded to 377 pathogens, 21,079 chromosomes, 2,662 genomic islands, 41,828 plasmids and 155,606 whole-genome shotgun assemblies, resulting in collation of 322,710 unique ARG allele sequences. New features include the CARD:Live collection of community submitted isolate resistome data and the introduction of standardized 15 character CARD Short Names for ARGs to support machine learning efforts.
Assuntos
Curadoria de Dados , Bases de Dados Factuais , Resistência Microbiana a Medicamentos , Aprendizado de Máquina , Antibacterianos/farmacologia , Genes Bacterianos , Funções Verossimilhança , Software , Anotação de Sequência MolecularRESUMO
Diagnosing antimicrobial resistance (AMR) in the clinic is based on empirical evidence and current gold standard laboratory phenotypic methods. Genotypic methods have the potential advantages of being faster and cheaper, and having improved mechanistic resolution over phenotypic methods. We generated and applied rule-based and logistic regression models to predict the AMR phenotype from Escherichia coli and Pseudomonas aeruginosa multidrug-resistant clinical isolate genomes. By inspecting and evaluating these models, we identified previously unknown ß-lactamase substrate activities. In total, 22 unknown ß-lactamase substrate activities were experimentally validated using targeted gene expression studies. Our results demonstrate that generating and analysing predictive models can help guide researchers to the mechanisms driving resistance and improve annotation of AMR genes and phenotypic prediction, and suggest that we cannot solely rely on curated knowledge to predict resistance phenotypes.
Assuntos
Antibacterianos/farmacologia , Biologia Computacional/métodos , Farmacorresistência Bacteriana , Escherichia coli/enzimologia , Pseudomonas aeruginosa/enzimologia , beta-Lactamases/metabolismo , Algoritmos , Simulação por Computador , Curadoria de Dados , Escherichia coli/efeitos dos fármacos , Escherichia coli/genética , Perfilação da Expressão Gênica , Regulação Bacteriana da Expressão Gênica , Sequenciamento de Nucleotídeos em Larga Escala , Modelos Logísticos , Pseudomonas aeruginosa/efeitos dos fármacos , Pseudomonas aeruginosa/genética , Sequenciamento Completo do GenomaRESUMO
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.
