Overview
Background
I am a bioinformatician developing computational methods and open-source software that make pathogen genome sequencing faster, cheaper, and more clinically useful. My research applies long-read (Oxford Nanopore) and short-read whole-genome sequencing to detect antimicrobial resistance, reconstruct microbial genomes, and trace how infections spread.
A core part of my work is building widely used, open-source bioinformatics tools and reproducible analysis pipelines that researchers and laboratories around the world rely on. I also contributed to the World Health Organization catalogue of tuberculosis drug-resistance mutations, a global reference for genome-based diagnosis.
Working closely with clinical and public-health laboratories, I help translate these methods out of research and into real diagnostic and outbreak-response settings — including sequencing pathogens directly from patient samples, without the delays of laboratory culture.
My areas of expertise include:
- Pathogen genomics and genomic epidemiology - Antimicrobial resistance and drug-resistance prediction - Long-read sequencing, genome assembly, and metagenomics - Culture-free, direct-from-sample diagnostics - Open-source scientific software development (Rust and Python)
You can explore my open-source tools on my GitHub profile (https://github.com/mbhall88).
Availability
- Dr Michael Hall is:
- Available for supervision
Fields of research
Research impacts
My research helps clinicians and laboratories detect drug-resistant infections and track how they spread, so hospitals can respond faster and patients receive effective treatment sooner. As antimicrobial resistance rises worldwide, the speed and accuracy of genome-based testing is increasingly central to patient safety and public health.
A recent example is my genomic analysis of a hospital outbreak of drug-resistant Enterococcus, which supported the investigation and the infection control decisions that followed. My methods also make it possible to sequence pathogens directly from patient samples, avoiding the days or weeks usually lost to laboratory culture.
Much of my impact comes from free, open-source software used every day by the global genomics community. My tools have been downloaded by laboratories and researchers worldwide — one of them more than 70,000 times — and are built into widely used analysis pipelines, lowering the cost and technical barriers to genome sequencing.
I have also contributed to international tuberculosis genomics, including the World Health Organization's catalogue of drug-resistance mutations used by laboratories worldwide.
Real-world outcomes of my work include:
- Faster detection and tracking of drug-resistant infections - Support for hospital outbreak response and infection control - Open-source tools adopted by laboratories around the world - Lower cost and complexity of genome sequencing
Works
Search Professor Michael Hall’s works on UQ eSpace
2021
Journal Article
Freshwater monitoring by nanopore sequencing
Urban, Lara, Holzer, Andre, Baronas, J. Jotautas, Hall, Michael B., Braeuninger-Weimer, Philipp, Scherm, Michael J., Kunz, Daniel J., Perera, Surangi N., Martin-Herranz, Daniel E., Tipper, Edward T., Salter, Susannah J. and Stammnitz, Maximilian R. (2021). Freshwater monitoring by nanopore sequencing. eLife, 10 e61504, 1-27. doi: 10.7554/elife.61504
2021
Journal Article
Sustainable data analysis with Snakemake
Köster, Johannes, Mölder, Felix, Jablonski, Kim Philipp, Letcher, Brice, Hall, Michael B., Tomkins-Tinch, Christopher H., Sochat, Vanessa, Forster, Jan, Lee, Soohyun, Twardziok, Sven O., Kanitz, Alexander, Wilm, Andreas, Holtgrewe, Manuel, Rahmann, Sven and Nahnsen, Sven (2021). Sustainable data analysis with Snakemake. F1000Research, 10 33, 33. doi: 10.12688/f1000research.29032.2
2019
Journal Article
Correction to: Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning
Teng, Haotian, Cao, Minh Duc, Hall, Michael B., Duarte, Tania, Wang, Sheng and Coin, Lachlan J M (2019). Correction to: Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning. GigaScience, 8 (5) giz049. doi: 10.1093/gigascience/giz049
2019
Journal Article
Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with mykrobe [version 1; peer review: 2 approved, 1 approved with reservations]
Hunt, Martin, Bradley, Phelim, Lapierre, Simon Grandjean, Heys, Simon, Thomsit, Mark, Hall, Michael B., Malone, Kerri M., Wintringer, Penelope, Walker, Timothy M., Cirillo, Daniela M., Comas, Iñaki, Farhat, Maha R., Fowler, Phillip, Gardy, Jennifer, Ismail, Nazir, Kohl, Thomas A., Mathys, Vanessa, Merker, Matthias, Niemann, Stefan, Omar, Shaheed Vally, Sintchenko, Vitali, Smith, Grace, van Soolingen, Dick, Supply, Philip, Tahseen, Sabira, Wilcox, Mark, Arandjelovic, Irena, Peto, Tim E.A., Crook, Derrick W. and Iqbal, Zamin (2019). Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with mykrobe [version 1; peer review: 2 approved, 1 approved with reservations]. Wellcome Open Research, 4 191, 191. doi: 10.12688/wellcomeopenres.15603.1
2018
Journal Article
Chiron: Translating nanopore raw signal directly into nucleotide sequence using deep learning
Teng, Haotian, Cao, Minh Duc, Hall, Michael B., Duarte, Tania, Wang, Sheng and Coin, Lachlan J. M. (2018). Chiron: Translating nanopore raw signal directly into nucleotide sequence using deep learning. GigaScience, 7 (5). doi: 10.1093/gigascience/giy037
Supervision
Availability
- Dr Michael Hall is:
- Available for supervision
Looking for a supervisor? Read our advice on how to choose a supervisor.
Supervision history
Current supervision
-
Master Philosophy
Understanding plasmid evolution using genomics
Associate Advisor
Other advisors: Associate Professor David Whiley, Dr Leah Roberts
Media
Enquiries
For media enquiries about Dr Michael Hall's areas of expertise, story ideas and help finding experts, contact our Media team: