Dr Minh Bui

Minh Bui joined the Research School of Computer Science in 2019. Prior to this, he was a Research Fellow at the Research School of Biology (ANU), a Postdoc at the Max F. Perutz Laboratories (Vienna, Austria). His research focuses on computational biology and bioinformatics, and he has been working on methods and models development for phylogenomics, i.e., the reconstruction of the Tree of Life from genome-scale data. He is leading the IQ-TREE project (http://www.iqtree.org), a widely used software (>2000 Google Scholar citations) for phylogenomic inference using maximum likelihood.

Minh Bui received his PhD in Bioinformatics from University of Vienna (Austria), an MSc in Applied Computer Science from University of Freiburg (Germany), and a BSc in Computer Science from Vietnam National University (Vietnam).

Visit my personal home page at https://bqminh.github.io for more information.

Motivated by the rapid accumulation of molecular sequence data, especially those generated by deep sequencing techniques, my research career has focused on developing efficient bioinformatics methods, statistical models, algorithms and high performance computing techniques to analyse huge phylogenetic datasets. 

Research Theme 1: Bioinformatics method and software for evolutionary biology 

I have developed three widely used bioinformatics methods for the phylogenetic community: a fast and accurate model selection approach (ModelFinder), an effective tree reconstruction method (IQ-TREE) and a novel ultrafast bootstrap approach (UFBoot). These methods represent three key steps in every phylogenetic analysis.

A significant outcome from this research theme is the widely used IQ-TREE phylogenetic software (www.iqtree.org), which I have continuously developed since 2011. IQ-TREE has received a lot of user enthusiasms and integrated in many phylogenetic pipelines. Currently, I am leading an international team from Austria, Australia and Vietnam to jointly develop IQ-TREE.

Research Theme 2: Statistical models for phylogenomics 

Driven by the rapid growth of large phylogenomic datasets coupled with increasing impact of model violation, I jointly developed many new statistical models for phylogenomics including partition models, polymorphism-aware models, distribution free models, mixture models and site-specific models. These advanced models have been implemented in IQ-TREE, representing the first time availability in a prominent maximum likelihood framework.

Research Theme 3: Phylogenetic applications 

Applications play an important role in my search, not only to show the usefulness of bioinformatics methods but also to identify potential limitations of existing models. Hence, I have collaborated with many biologists to analyse empirical datasets ranging from virus to bacteria and eukaryotes. Such collaborations include: the origin of photosynthetic enzymes, deep insect phylogeny using transcriptomic data, origin of helizoan protists, Algae and Cryptista using phylogenomic data, HIV full-genome phylogeny.

Research Theme 4: Efficient algorithms for biodiversity optimisation 

During my PhD study I introduced efficient algorithms for many biodiversity conservation questions such as: Which species/areas should we prioritise for conservation, such that the phylogenetic diversity conserved is maximised? How to measure biodiversity when evolution is not treelike? The algorithms that I developed range from greedy algorithm to dynamic programming and integer linear programming. Especially the last technique generally works for extended conservation problems under budget constraint or prey-predator interactions.

Current student projects

Full list of publications is available at: https://scholar.google.com.au/citations?user=UI0xN...

Selected publications:

  • D.T. Hoang, O. Chernomor, A. von Haeseler, B.Q. Minh§§, and L.S. Vinh (2018) UFBoot2: Improving the ultrafast bootstrap approximation. Mol. Biol. Evol., 35, 518-522. (>340 Google Scholar citations)

  • H.C. Wang, B.Q. Minh§, S. Susko, and A.J. Roger (2018) Modeling site heterogeneity with posterior mean site frequency profiles accelerates accurate phylogenomic estimation. Syst. Biol., 67, 216-235. (>50 Google Scholar citations)

  • S. Kalyaanamoorthy, B.Q. Minh§, T.K.F. Wong, A. von Haeseler, and L.S. Jermiin (2017) ModelFinder: Fast model selection for accurate phylogenetic estimates. Nature Methods, 14, 587-589.(>700 Google Scholar citations)

  • O. Chernomor, A. von Haeseler, and B.Q. Minh (2016) Terrace aware data structure for phylogenomic inference from supermatrices. Syst. Biol., 65, 997-1008. (>190 Google Scholar citations)

  • J. Trifinopoulos, L.T. Nguyen, A. von Haeseler, and B.Q. Minh(2016) W-IQ-TREE: A fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res., 44, W232-W235.(>360 Google Scholar citations)

  • L.-T. Nguyen, H.A. Schmidt, A. von Haeseler, and B.Q. Minh (2015) IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol. Biol. Evol., 32, 268-274. (>2,200 Google Scholar citations)

  • B.Q. Minh, M.A.T. Nguyen, and A. von Haeseler(2013) Ultrafast approximation for phylogenetic bootstrap.Mol. Biol. Evol., 30, 1188-1195.(>750 Google Scholar citations)

Nov 2018: Organiser, CompBioFest (Computational Biology Fest), Australian National University.

2017: PC member for the workshop on Mathematical and Statistical Aspects of Molecular Biology (MASAMB).

2012-2016: PC member for the International Conference on Bioinformatics (InCoB).

2015: PC member for the German Conference on Bioinformatics (GCB).

Reviewer for various scientific journals (in alphabetical order): Algorithms for Molecular Biology, Applied Mathematics Letters, Bioinformatics, BMC Evolutionary Biology, BMC Bioinformatics, Journal of Mathematical Biology, Journal of Theoretical Biology, Methods in Ecology and Evolution, Molecular Biology and Evolution, PLoS ONE, Systematic Biology, IEEE Transaction on Computational Biology and Bioinformatics.

Updated:  1 June 2019/Responsible Officer:  Head of School/Page Contact:  CECS Marketing