Computational Advances in Statistical Phylogenomics

The advent of rapid and inexpensive DNA sequencing technologies has resulted in an abundance of data that pose both statistical and computational challenges to conventional phylogenetic methods. For example, the Covid-19 pandemic led to the availability of millions of viral sequences whose rapid analysis had impacts on the practice of public health. Similarly, recent genome sequencing projects (e.g., the 100,000 Genomes Project) have curated massive data sets that hold potential for advancing our understanding of evolutionary processes and the implications of these process on ecosystem sustainability. In this session, we discuss the recent advances in statistical phylogenetic models, statistical and computational inference techniques, and numerical implementations that aim at achieving inference breakthroughs in these scientific research domains. The proposed topics range from phylogenetic tree estimation to phylogenetic network inference, and from topological convergence assessment to its application in pathogen phylodynamic inference.