Minh is the leader of the Computational Phylogenomics Lab, which lies at the interface between evolutionary biology, computer science and statistics. His motto is to enable evolutionary research in the genomic era. Driven by the rapid accumulation of next-generation sequence data, his lab has developed widely-used bioinformatic methods and statistical models for phylogenetic inference from ultra-large genomic data. His lab has developed and maintained the widely-used IQ-TREE software. One current theme is to improve the scalability of IQ-TREE for COVID-19 genome data analysis.
Minh’s lab focuses on three main research directions, which will help to close the gap between the rapidly growing amount of data and the current methods and models to analyse the data.
Theme 1: Next-generation computational methods for the post-genomic era
The field of phylogenomics is now moving towards the post-genomic era. We are now seeing global DNA sequencing efforts on a never-before-seen scale, such as the COVID-19 genome data and other ultra-large datasets. This presents computational challenges and calls for a new generation of bioinformatics tools. To address these bottlenecks His lab will work on, but not limited to, the following topics:
- Real-time algorithms for continuous updates of global SARS-CoV-2 trees and other epidemiological datasets
- Machine-learning techniques to significantly speed up traditional phylogenetic methods
- New high-performance computing techniques, e.g. based on Graphical Processing Units
- Tailored meta-heuristics to further improve the accuracy of phylogenetic inference
Theme 2: Next-generation statistical models of genomic complexity
The availability of entire genome data provided us an unprecedented opportunity to understand the complexity of biological systems. But at the same time this complexity challenges existing statistical models that are based on simplifying assumptions and have become increasingly distant from the truth. “All models are wrong, but some are useful” (Box, 1979).
Minh’s lab will work on a new suite of models that can capture more realisms in genomic evolution, including:
- Sequencing error models for SARS-CoV-2 and other datasets.
- Mixture models that account for heterogeneous evolutionary processes along the genome.
- Unified model selection framework to automatically determine the best models for the data.
- Theme 3: Phylogenetic applications
Applications to analyse real data play an important role in the lab. In collaborations with biologists, Minh’s lab helps towards resolving long-standing questions in evolutionary biology, ranging from virus to bacteria and eukaryotes. This is not only to show the usefulness of bioinformatics methods but also to identify potential limitations of existing models. Some projects are to:
- Study the origin and transmission of SARS-CoV-2 and other pathogens.
- Disentangle contentious phylogenetic relationships across the tree of life.
- Rooting the tree of life.
Minh obtained his B.Sc. in Computer Science (2001) from Vietnam National University, M.Sc. in Applied Computer Science (2005) from the University of Freiburg (Germany) and Ph.D. in Bioinformatics (2009) from the University of Vienna (Austria). After a Postdoc at Max Perutz Labs, he joined the Australian National University (ANU) as a Senior Research Fellow (2018), a Lecturer (2019), and a Senior Lecturer (2021) at the School of Computing.
Visit his personal home page at https://bqminh.github.io for more information.
Activities & Awards
- 2019: Australian Field Leader in Evolutionary Biology, The Australian.
- 2003-2005: Scholarship for the Masters study by the Konrad-Adenauer-Stiftung, Germany.