Tammy Lau has been awarded a prestigious Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Summer Student Fellowship for her work on development of k-mer approaches to predicting pathogen-of-origin for metagenomics antimicrobial resistance gene sequences. More details here.

Congratulations Tammy!

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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.

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The Comprehensive Antibiotic Resistance Database: Expanded tools for molecular surveillance of antimicrobial resistance (AMR) in the environment, agriculture, and clinic. A.G. McArthur (PI), G. Van Domselaar (co-I, Public Health Agency of Canada), R. Beiko (Co-I, Dalhousie University), F. Brinkman (co-I, Simon Frasier University). CIHR Project Grant.

Preventing Clostridium difficile infections by identifying asymptomatic carriers. D. Mertz, M. Loeb, J. Pernica, S. Khan, M. Smieja, A.G. McArthur (Co-Applicants). Hamilton Health Sciences Research Strategic Initiative Program.

Developing a strain-specific test for rapid diagnosis of Clostridium difficile. Y. Li (PI), A.G. McArthur (co-I), C. Lee (co-I). CIHR Antimicrobial Resistance: Point of Care Diagnostics in Human Health.
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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.

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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.

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header-aa7f836a57fbef4ef15c560ab4ce386fPawlowski AC, Wang W, Koteva K, Barton HA, McArthur AG, & Wright GD.

Nat Commun. 2016 Dec 8;7:13803.

Antibiotic resistance is ancient and widespread in environmental bacteria. These are therefore reservoirs of resistance elements and reflective of the natural history of antibiotics and resistance. In a previous study, we discovered that multi-drug resistance is common in bacteria isolated from Lechuguilla Cave, an underground ecosystem that has been isolated from the surface for over 4 Myr. Here we use whole-genome sequencing, functional genomics and biochemical assays to reveal the intrinsic resistome of Paenibacillus sp. LC231, a cave bacterial isolate that is resistant to most clinically used antibiotics. We systematically link resistance phenotype to genotype and in doing so, identify 18 chromosomal resistance elements, including five determinants without characterized homologues and three mechanisms not previously shown to be involved in antibiotic resistance. A resistome comparison across related surface Paenibacillus affirms the conservation of resistance over millions of years and establishes the longevity of these genes in this genus.

See more: McMaster Daily News

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genome-canada-1A cross-national research consortia co-led by McMaster’s Andrew McArthur is receiving two of 16 federal grants to further develop a big data solution to the growing problem of antimicrobial resistance (AMR). The government’s investment, totaling more than $4M, is the result of Genome Canada’s 2015 Bioinformatics and Computational Biology Competition, a partnership with the Canadian Institutes of Health Research (CIHR). McArthur and his colleagues will receive $500,000 over two years. McArthur will work closely with researchers from the University of British Columbia, Simon Fraser University, Dalhousie University and the Public Health Agency of Canada to design and develop novel software and database systems that will empower public health agencies and the agri-food sector to rapidly respond to threats posed by infectious disease outbreaks and food-borne illnesses.

Full Coverage: Faculty of Health Sciences, Genome Canada, Newswire, Hamilton Spectator

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McArthur, A.G.B. Jia, A.R. Raphenya, P. Guo, K. Tsang, B. Dave, B. Alcock, B. Lago, N. Waglechner, & G.D. Wright. 2016. The Comprehensive Antibiotic Resistance Database – A Platform for Antimicrobial Resistance Surveillance. Invited presentation at the 2nd Conference Rapid Microbial NGS and Bioinformatics: Translation Into Practice, Hamburg, Germany.

Antimicrobial resistance (AMR) is among the most pressing public health crises of the 21st Century. Despite the importance of resistance to health, this field has been slow to take advantage of genome scale tools. Phenotype based criteria dominate the epidemiology of antibiotic action and effectiveness. There is a poor understanding of which antibiotic resistance genes are in circulation, which a threat, and how clinicians and public health workers can manage the crisis of resistance. However, DNA sequencing is rapidly decreasing in cost and as such we are on the cusp of an age of high-throughput molecular epidemiology. What are needed are tools for rapid, accurate analysis of DNA sequence data for the genetic underpinnings of antibiotic resistance. In an effort to address this problem, we have created the Comprehensive Antibiotic Resistance Database (card.mcmaster.ca). This database is a rigorously curated collection of known antibiotics, targets, and resistance determinants. It 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 raw genome sequences using the novel Resistance Gene Identifier (RGI). Here we review the current state of the CARD, particularly recent advances in the curation of resistance determinants and the structure of the ARO. We will also present our plans for development of semi- and fully-automated text mining algorithms for curation of broader AMR data, construction of meta-models for improved AMR phenotype prediction, and release of portable command-line genome analysis tools.


* presenter underlined, trainees in bold
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image2Congratulations to Kara Tsang and Zachary Lin on completion of their Biomedical Discovery and Commercialization (BDC) 4A15 thesis research! Both Kara & Zachary presented their research results at the 2016 BDC Engage Symposium.

Zachary Lin: Adapting Galaxy bioinformatics to outbreak- associated Clostridium difficile

Kara Tsang: The translation of biocuration to metagenomic analysis for combatting multi-drug resistant Pseudomonas aeruginosa

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