What do we do?
We develop models and software to better understand the evolution of organisms and to test macroevolutionary hypotheses. We are looking at the ecological, genomic and morphological factors that constrain speciation and adaptation. We focus on phylogenetic methods, clownfish and plant genomics, the estimation of positive selection, modelling the evolution of DNA sequences and phenotypes, the mode and tempo of species evolution and the spatially explicit evolution of diversity. Our aim is to develop better models to analyse sequence data and quantitative models to estimate macroevolutionary patterns and processes.
The group is developing new ways to estimate the rate of species evolution by using Bayesian approaches and parallelizing Markov chain Monte Carlo algorithms. We couple this framework with the development of complex models of molecular and morphological evolution. The improved efficiency that we obtained enables us to analyse much larger datasets than previously thought and it opens the way to complex phylogenomic analyses. We are extending these approaches to combine the estimation of the phylogenetic tree with evolutionary models to have an efficient framework to test hypotheses about the evolution of large groups of organisms.
Main publications 2017
- Meyer X et al. Accelerating Bayesian inference for evolutionary biology models. Bioinformatics. 2017; 33:669
- Davydov I et al. State aggregation for fast likelihood computations in molecular evolution. Bioinformatics. 2017; 33:354
- Meyer X et al. Scheduling Finite Difference Approximations for DAG-Modeled Large Scale Applications. Proceedings of the Platform for Advanced Scientific Computing Conference. 2017; 6