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.
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.