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Integrating novel sequencing technologies into clinical microbiology: using Nanopore sequencing to address our superbug problem

Description 
Antimicrobial resistance (AMR) is an urgent global health challenge that threatens our healthcare system and many of the advances of modern medicine. In the face of this threat, novel tools such as whole genome sequencing (WGS) are providing crucial insights into how antimicrobial resistance develops and spreads. Through this research program, we aim to transform the way we track and respond to AMR infections and prevent their spread in healthcare. We have established a multi-disciplinary group of investigators across Monash Partner sites to integrate, apply and implement cutting-edge technologies of genome sequencing and artificial intelligence, with electronic healthcare data and infection prevention interventions. We have developed a technical pipeline focusing on Oxford Nanopore sequencing for detecting bacterial resistance determinants and isolate relatedness, which will allow us to diagnose superbug infections early and also understand if outbreaks are occurring in the hospital setting. This work will form the foundations of a transformative health service delivery known as an “Integrated AMR Infection Prevention Genomics Service”. Our research program will focus on the key enablers to implement such an initiative in healthcare, with the intention of using these findings to expand across Australia and beyond. The broad aims of the project are: Aim 1) Pilot implementation of our bacterial genomics pipeline in the Alfred Health clinical microbiology laboratory to detect superbugs and superbug outbreaks Aim 2) Conduct a pilot study to assess the feasibility of implementing an Integrated AMR Infection Prevention Genomics Service at Alfred Health. Aim 3) Conduct a multi-centre study across Monash Partners sites for implementing a bacterial genomics pipeline to detect superbugs and superbug outbreaks Students would be expected to develop skills in computational biology approaches (including command-line programs) to analyse and interpret large datasets. Prior experience using the Unix operating system and the R and Python programming languages is preferred but not essential.
Essential criteria: 
Minimum entry requirements can be found here: https://www.monash.edu/admissions/entry-requirements/minimum
Keywords 
Antimicrobial resistance; Superbugs; Genomics; Artificial Intelligence; Machine learning; Clinical research; Microbiology
Available options 
PhD/Doctorate
Masters by research
Masters by coursework
Honours
BMedSc(Hons)
Short projects
Time commitment 
Full-time
Part-time
Top-up scholarship funding available 
No
Physical location 
Alfred Centre
Co-supervisors 
Prof 
Anton Peleg

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