David Braley Centre for Antibiotic Discovery gives researchers a fighting chance against antimicrobial resistance.
A forward-looking McMaster donor is investing $7 million in a new research centre dedicated specifically to tackling the growing global threat of antimicrobial resistance.
David Braley, whose gifts to the university include a $50-million investment in McMaster teaching, learning and health-care research and delivery, has allocated $7 million from that 2007 gift towards the new David Braley Centre for Antibiotic Discovery.
The centre will operate from the Michael G. DeGroote Institute for Infectious Disease Research, whose labs and offices are located on campus in the Michael G. DeGroote Centre for Learning and Discovery.
Dr. McArthur and PhD student Kara Tsang taught together at the 2019 MacData Institute Summer School, with Dr. McArthur reviewing biocuration and bioinformatics for genomic surviellence of antimicrobial resistance and Kara following up with a lecture on machine learning techniques to predict clinical antimicrobial resistance from raw genomic sequence.
Also congratulations to Kara for being awarded a 2019 Faculty of Health Sciences Graduate Programs Excellence Award!
Updated August 6, 2019: Congratulations to Kara for also winning an Ontario Graduate Scholarship!
The Comprehensive Antibiotic Resistance Database has been updated, http://card.mcmaster.ca
CARD Curation: Expanded MCR, OXA & IMP beta-lactamase, and macrolide phosphotransferase (MPH) sequence curation. Updated nomenclature for MPHs and drug resistant dihydrofolate reductases (dfr). Updated classification of ADC beta-lactamases.
Ontologies: Addition of 518 draft virulence ontology (VIRO) terms.
Prevalence, Resistomes, & Variants: Expansion to 82 pathogens (more Brucella species), 81,000+ resistomes, and 173,000+ AMR allele sequences based on sequence data acquired from NCBI on 28-Feb-2019, analyzed using RGI 4.2.2 (DIAMOND homolog detection) and CARD 3.0.1.
During McMaster Spring Mid-Term Recess (February 18-24), the McArthur lab is pleased to present a series of lectures, demonstrations, and training sessions for the Comprehensive Antibiotic Resistance Database (card.mcmaster.ca) and its associated Resistance Gene Identifier (RGI) software, sponsored by the Michael G. DeGroote Institute for Infectious Disease Research (IIDR).
Questions? Email email@example.com
Workshop & Lecture material will be available here: https://github.com/arpcard/state-of-the-card-2019
Antimicrobial Resistance: Emergence, Transmission, and Ecology (ARETE). R. Beiko (PI; Dalhousie University), F. Brinkman (co-PI, Simon Fraser University), A.G. McArthur (co-Applicant) + 4 additional co-Applicants. Genome Canada Bioinformatics and Computational Biology Competition.
Bioinformatics Tools to Improve Data Sharing and Re-use in Public Health – applications in antimicrobial resistance profiling and source tracking. W. Hsiao (PI; University of British Columbia), A.G. McArthur (co-Applicant) + 8 additional co-Applicants. CIHR Project Grant.
Some invitations are more special than others. Dr. Peixoto da Cruz and I went to graduate school together in British Columbia (a long time ago!) and while we have since lived in different hemispheres, the bond remains strong. It was great to visit PUG Goiás and learn about Peixoto’s impressive training program in genetic screening and counselling, plus talk about our AMR surveillance efforts.
Bioinformatics of antimicrobial resistance in the age of molecular epidemiology. Invited Keynote presentation by A.G. McArthur at Reunião de Citogenética do Brasil Central & XII Workshop de Genética da PUC Goiás, Goiânia, Brazil, October 2018.
Maguire, F., B. Alcock, F.S. Brinkman, A.G. McArthur, & R.G. Beiko. 2018. AMRtime: Rapid Accurate Identification of Antimicrobial Resistance Determinants from Metagenomic Data. Oral presentation at the Third American Society for Microbiology Meeting on Rapid Applied Microbial Next-Generation Sequencing and Bioinformatics Pipelines, Washington, D.C.
Abstract: Metagenomics, the direct sequencing of the mixture of genomes present in a sample, is an increasingly common workflow within the life sciences. It is frequently used to investigate previously intractable problems such as the functional characterisation of entire microbial environments. One such use-case of global and national public-health importance is analysing the nature and transmission dynamics of antimicrobial resistance (AMR) determinants in human, agri-food and environmental samples. Recently some tools have been developed to profile AMR from metagenomes, however, these are generally limited to profiling at the level of AMR genes clustered by % sequence identity, which may or may not be biologically meaningful. By exploiting the expertly curated ontological structure of the Comprehensive Antibiotic Resistance Database (CARD) and new CARD Prevalence datasets, we have developed an approach using a hierarchical set of machine learning classifiers. This allows us to produce gene-specific AMR profiles to 2386 determinants as well as profiles for higher order, biologically informed, AMR gene family groups. Firstly, DIAMOND based heuristically accelerated homology searches are used to filter out non-AMR related metagenomic reads. This filtering has been optimised to prioritise minimisation of false negatives over minimising false positives. Features generated from these homology searches as well as sequence features are then used to train a random forest classifier to classify filtered reads into one of 227 CARD AMR gene families (e.g. MCR phosphoethanolamine transferase). For each gene family an additional random forest classifier is trained to classify reads into one of the specific AMR determinants belonging to that family (e.g. MCR-1, MCR-2, MCR-3 etc.). This process involves very little computational overhead when classifying beyond the initial homology search. On a fully held out test-set of MiSeq reads simulated from the CARD canonical gene sequences this method resulted in an average precision and recall of 0.993 and 0.987 at the AMR gene family level. Within the 227 AMR families, 70% (158) had an average F1-score greater than 0.99 for classification to specific AMR determinants. A further 10% (24) averaged F1-scores between 0.8 and 0.99. In comparative analyses on the same dataset this outperformed homology searches alone, read mapping and variation graph based methods in terms of average overall accuracy and precision. Further work will aim to improve classification within certain families and expand AMRtime to include variant based AMR models as well as meta-models (e.g. multi-component efflux pump systems).