Research

Our motto - Enabling evolutionary research in the genomic era


research areas
Our research lies at the interface between evolutionary biology, computer science and statistics. We develop bioinformatic methods, statistical models of molecular evolution, efficient algorithms and high performance computing techniques for phylogenetic inference from ultra-large genomic data.


Tree reconstruction


phylogenetic tree
We proposed an effective stochastic algorithm (IQ-TREE) to infer phylogenies under maximum likelihood. IQ-TREE was inspired by iterated local search and evolution strategy meta-heuristics. An independent benchmark showed that IQ-TREE outperformed RAxML and PhyML in terms of likelihood maximization and required similar computation times.

Ultrafast bootstrap


phylogenetic bootstrap
We introduced an ultrafast bootstrap approximation (UFBoot) for phylogenetics. UFBoot runs 100 times faster than the standard bootstrap while provideing approximately unbiased branch support values. A recent improvement can correct for model violations and gene resamplings.

Model selection


DNA model
We introduced a fast and accurate model selection (ModelFinder). ModelFinder is 10 to 100 times faster and provides many more models than jModelTest and ProtTest. ModelFinder also finds best partitioning scheme like PartitionFinder.

Bioinformatics software


IQ-TREE logo
A significant outcome is the widely used IQ-TREE phylogenetic software, which has been continuously developed since 2011. IQ-TREE has received a lot of user enthusiasms and integrated in many phylogenetic pipelines. IQ-TREE has jointly been developed by an international team from Australia, Austria, and Hungary.

Empirical data analysis


IQ-TREE logo
Applications play an important role in our search, not only to show the usefulness of the methods but also to identify potential limitations of existing models. We have collaborated with many biologists to analyse empirical datasets and answer many unresolved questions across the Tree of Life.


© 2019 The Minh Lab @ Research School of Computer Science and Biology, Australian National University