On the inference of complex phylogenetic networks for detecting hybridizations and introgressions
In this talk, I will present my recent work on phylogenetic networks, in collaboration with V. Berry and F. Pardi (LIRMM), JC Glaszmann (CIRAD) and C. Scornavacca (ISE-M).
Nowadays, to make the best use of the vast amount of genomic data at our disposal, there is a real need for methods able to model complex biological mechanisms such as hybridization and introgression. Understanding such mechanisms can help geneticists to elaborate strategies in crop improvement that may help reducing poverty and dealing with climate change. However, reconstructing such evolution scenarios is challenging. Indeed, the inference of phylogenetic networks, which explicitly model reticulation events such as hybridization and introgression, requires high computational resources. Then, on large data sets, biologists generally deduce reticulation events indirectly using species tree inference tools.
In this context, we present a new Bayesian method, called SnappNet, dedicated to phylogenetic network inference. Our method is competitive in terms of execution speed with respect to its competitors. This speed gain enables us to consider more complex evolution scenarios during Bayesian analyses. When applied to rice genomic data, SnappNet suggested a new evolution scenario, compatible with the existing ones: it posits cAus as the result of an early combination between the Indica and Japonica lineages, followed by a later combination between the cAus and Japonica lineages to derive cBasmati. This accounts for the well-documented wide hybrid compatibility of cAus.
INSCRIPTION OBLIGATOIRE pour participation sur place (nombre de places limité) :
WEBINAIRE ouvert à toutes et tous : https://umontpellier-fr.zoom.us/j/85813807839
Pour toute question, s’adresser à Paul Bastide