4-8 December 2023

To foster international participation, this course will be held online




Phylogenetic inference and divergence-time estimation with genomic data sets


Recent advances in sequencing technology, and the rapid increase in the availability of genetic data, have revolutionized the field of phylogenetics. While genomic data promise unprecedented insights into the evolution of the tree of life, they also pose new challenges that must be addressed to avoid misleading results and to fully leverage the potential of the genome-scale data sets. These challenges include the identification of orthologuous sequences that are suitable as phylogenetic markers, the selection of appropriate models of sequence evolution, and the detection of gene-tree discordance due to incomplete lineage sorting and introgression. In this workshop we will present theory and exercises to infer time-calibrated phylogenies from multi-locus genome data sets while accounting for these confounding factors.



Workshop Format

The workshop will be delivered over the course of five days. Each day will include an introductory lecture with class discussion of key concepts. The remainder of each day will consist of practical hands-on sessions. These sessions will involve a combination of both mirroring exercises with the instructors to demonstrate a skill as well as applying these skills on your own to complete individual exercises. After and during each exercise, interpretation of results will be discussed as a group.
Computing will be done using tools installed in a preconfigured AWS ec2 server. This will allow us to focus on the theory and options of the actual methods . We will devote short time slots to troubleshoot installation and running problems for those that want to use the software on their computers but this will not be part of the main workshop.

Who Should Attend

This workshop is aimed at researchers, PhD or postdoc level planning to infer phylogenetic relationships and divergence times from multilocus data and has no or little prior experience.