Carleton University - School of Computer Science Honours Project
Winter 2017
AMR Detector
Jackson Eyres
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ABSTRACT
Whole genome sequencing has emerged as the premier solution for the monitoring and detection of antimicrobial resistance (AMR) among pathogens. However, there currently is a dearth of programs that incorporate the latest databases, are user friendly and support a wider variety of bacteria whole genome formats. AMR_Detector is a web application that predicts antibiotic resistance within bacteria using next generation sequencing analysis. It provides a user friendly interactive browser based solution for research biologists who may not have extensive Linux or command line experience. It was developed using Django, PostgreSQL, and deployed via Docker. In addition to prediction, AMR Detector augments data with prevalence data gathered from novel analysis of ~15000 priority pathogens offering users a clearer picture of strain and species level information.