The Comprehensive Antibiotic Resistance Database has been updated, http://card.mcmaster.ca
CARD Curation: Addition of HERA, TRU, & ACI beta-lactamases, sul4, and new quinolone efflux pumps.
Antibiotic Resistance Ontology: Expanded to include an entirely new branch describing AMR phenotypic testing methods. ARO additionally now officially available at the OBO Foundry, allowing formal integration with other ontological resources, most notably the Genomic Epidemiology Application Ontology (GenEpiO), https://github.com/genepio/genepio.
Resistance Gene Identifier: Resistome prediction for low quality or low coverage assemblies, merged metagenomics reads, and small plasmids or assembly contigs. Includes prediction of partial AMR genes. Support added for Docker operating-system-level virtualization (i.e. containerization).
Prevalence, Resistomes, & Variants: Expanded to 67 important pathogens, with a focus on ESKAPEs, WHO Priority Pathogens, and agents of sepsis.
The McArthur lab and the Comprehensive Antibiotic Resistance Database are proud to join the Canadian Anti-Infective Innovation Network, International Genomic Epidemiology Application Ontology Consortium, and Integrated Rapid Infectious Disease Analysis Project!
The Comprehensive Antibiotic Resistance Database has been updated, http://card.mcmaster.ca
This February 2018 release is our largest to date and includes new data types, a new classification system, an entirely new version of the Resistance Gene Identifier, and website improvements.
CARD Curation: 37 new ADC beta-lactamases, 21 PDC beta-lactamases, new MCR proteins, 23 rRNA mutations, resistant isoleucyl-tRNA synthetases, hundreds of new resistance mutations, and more. While in past releases all curated AMR mutations were those characterized from clinical isolates, CARD now additionally includes mutations discovered via in vitro selection experiments. Ontological improvements have been made to enable an entirely new classification system for CARD data and RGI results: resistance determinants are now systematically categorized by AMR Gene Family, Drug Class, and Resistance Mechanism. The Antibiotic Resistance Ontology is now additionally available via GitHub, https://github.com/arpcard.
Resistance Gene Identifier: Entirely new codebase, compatible with CARD data (card.json) version 2.0.0 and up (download separately). Open Reading Frame (ORF) prediction using Prodigal, homolog detection using BLAST (default) or DIAMOND, and Strict significance based on CARD curated bitscore cut-offs. Addition of rRNA mutation and efflux over-expression models. Hits of 95% identity or better are automatically listed as Strict. All results organized by revised ARO classification: AMR Gene Family, Drug Class, and Resistance Mechanism. Revised documentation, command line menu, and website graphical interface. The Resistance Gene Identifier is now additionally available via GitHub, https://github.com/arpcard.
Prevalence, Genomes, & Variants: Expansion of our computer-generated data set on the prevalence of AMR genes and variants among the sequenced genomes, plasmids, and whole-genome shotgun assemblies available at NCBI for clinically important pathogens. CARD Prevalence 2.0.0 is based on sequence data acquired from NCBI on August 28, 2017, analyzed using RGI 4.0.0 (DIAMOND homolog detection) and CARD 2.0.0. Now includes results for protein overexpression models and rRNA mutations. All results organized by the revised ARO classification: AMR Gene Family, Drug Class, and Resistance Mechanism. Download files now include 35000+ genome annotations and all predicted sequence variants.
Building upon her successful Biochem 3A03 project, Tammy Lau is staying in the lab for 2017-2018 as part of her Biochem 4T15 Research Thesis. Tammy’s research will be focussed on developing new classification and visualization tools for our Resistance Gene Identifier (RGI), plus extending the RGI towards k-mer approaches for predicting pathogen-of-origin for metagenomics antimicrobial resistance gene sequences.
Congratulations to this year’s crop of BiomedDC 4A15 thesis students for 8 month research projects well done! From left to right:
Suman Virdee – Developing a Galaxy based Pipeline for RNA-Seq Analysis in Stem Cell Biology
Kirill Pankov – The Cytochrome P450 (CYP) Superfamily in the Cnidarian Phylum
Jonsson Liu – Clinical virulence detection and Clostridium difficile clonality
Annie Cheng – Predicting Plasmid-Mediated Antimicrobial Resistance from Whole Genome Sequencing
Godwin Chan – Using the Galaxy Platform to Increase Accessibility for Structure Determination via Cryo-Electron Microscopy
Arjun Sharma is a 2cd year Biochemistry & Biomedical Sciences student how as a volunteer designed and created the new AMR Forums! Learn more about Arjun’s project at ‘New online AMR forum is a valuable learning resource’ or visit the AMR Forums.
Wright, G.D. & A.G. McArthur. 2015. A bioinformatic platform for the characterization of antibiotic resistance in bacterial genomes and metagenomes. Presentation at the 2015 Interscience Conference of Antimicrobial Agents and Chemotherapy, San Diego, California.
The increasingly routine sequencing of bacterial genomes in biomedical research and the clinical lab requires access to easy to use, efficient, and accurate bioinformatic tools for analysis of bacterial traits from virulence to drug resistance. To contribute to this growing need, we have developed a platform for the investigation of antibiotic resistance elements, the Comprehensive Antibiotic Resistance Database (http://arpcard.mcmaster.ca/). This resource includes a manually curated database of over 3000 resistance genes and associated literature, protein structures, and target antibiotics. Associated with this platform are tools to aid in the study of resistance including the Resistance Gene Identifier (RGI) that can analyze genomic data for the presence of resistance elements. Our goal is to accurately predict resistance phenotype from genomic data. Our analysis of many genomes and associated antibiograms reveals a reservoir of ‘silent’ resistance genes that are predicted to encode viable resistance elements yet the phenotype is drug sensitive. Our efforts to manage these issues along with identifying and adding new resistance genes will be presented.