A busy year for Dr. McArthur outside of academia…
- Integrated Health Biosystems Chair – Combatting Antimicrobial Resistance in the Age of Molecular Epidemiology. Invited presentation at the Cisco Research Chairs Summit, Toronto.
- The Comprehensive Antibiotic Resistance Database (CARD) and the Resistance Gene Identifier – prediction of antimicrobial resistance genes for genomic and metagenomic sequencing data. Invited presentation at the Integrated Rapid Infectious Disease Analysis (IRIDA) Annual General Meeting, Winnipeg, Manitoba.
- The Comprehensive Antibiotic Resistance Database. Invited presentation at the Agriculture & Agri-Food Canada and Canadian Food Inspection Agency Genomics Research and Development Initiative: GRDI-AMR Annual General Meeting.
- Facilitator & Speaker, Artificial Intelligence: The Art of the Possible, Niagara Health Board of Directors Retreat (Niagara-on-the-Lake, Ontario).
- Represented McMaster University at McMaster – Queen’s Park Government Reception, Gardiner Museum, Toronto.
- Represented McMaster University at Research Canada’s Health Research Caucus – Reshaping Health Research and Innovation: Artificial Intelligence and Machine Learning (Parliament Hill, Ottawa, Ontario).
- Panellist – Artificial Intelligence in Healthcare, Norwegian Health Ministry & Government of Canada Round Table hosted by Hamilton Health Sciences (Hamilton, Ontario).
Dr. David Speicher has joined the McArthur Lab as our new Molecular Epidemiology Postdoctoral Fellow! David joins us via clinical epidemiology research in infectious disease at St. Joseph’s Healthcare Hamilton plus extensive training and experience in Cambodia, India, Sri Lanka, Kenya, and Australia. David has a depth of experience in infectious disease, virology, molecular biology, epidemiology and biostatistics, microbiology, and diagnostic techniques and will be leading infectious disease molecular epidemiology collaborations with McMaster Children’s Hospital, St. Joseph’s Healthcare Hamilton, and Hamilton Health Sciences with an emphasis on antimicrobial resistance, C. difficile, H. pylori, Shigella, Chlamydia trachomatis, and Mycoplasma genitalium. Hear Dr. Speicher talk about his research program on CFMU radio.
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.
Congratulations to Kara Tsang for winning the 2018 Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Michael Kamin Hart Memorial Scholarship (MSc), the highest academic honour for graduate students in the IIDR. Awarded during the 2018 IIDR Trainee Day, the award was accompanied by a talk by Kara on her Ph.D. research: (Machine) Learning about antibiotic resistance genotype- phenotype relationships”. Well done Kara!
Congratulations to #TeamVirulence for winning the 2018 McMaster Innovation Showcase People’s Choice Poster Award for their poster entitled, “Examining the relationship between virulence and antimicrobial resistance via expansion of the Comprehensive Antibiotic Resistance Database (CARD)”! Left to right: Anatoly MiroshnichenkoHiu-Ki Rachel Tran, Sally Yue Min, and Rafik El Werfalli.
#TeamVirulence also presented their work at the 2018 Michael G. DeGroote Institute for Infectious Disease Research (IIDR) Trainee Day!
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).