Selected Events

Ancestral recombination graphs (ARGs) are the extension of phylogenetic trees to include recombination, a powerful evolutionary process that shapes the genetic diversity of many species. The topology of this graph gives us important information on the evolution of a species, but algorithms to reconstruct an ARG from species data are often reliant on sample sequences carrying informative patterns of mutations.In this talk I will present exact results concerning the probability of recovering the true topology of an ARG under the coalescent with recombination and gene conversion. These expressions give us an indication of the uncertainty in reconstructed ARGs, and we see that for parameter values realistic for biological species (in particular SARS-CoV-2), the probability of reconstructing genealogies that are close to the truth is low. This is joint work with Anastasia Ignatieva and Jotun Hein (https://arxiv.org/abs/2110.04848). [more]

Reassortment is a form of recombination in viruses with segmented genomes, such as influenza. It is characterized by different viral strains exchanging their segments upon the co-infection of a single host cell. Therefore, the shared history of such viruses has to be depicted by a network, not a tree. In this talk Ugne Stolz will present a coalescent with reassortment (CoalRe) package for the inference of such networks from the genetic sequence data and its extension SCoRe which allows for population structure. Stolz will discuss extensions to the classic coalescent framework and how these packages can be used in order to explore reassortment influence on viral fitness and jumps between different host types. [more]

Over the past 35 years many statistical and bioinformatic tools have been developed to detect recombination, gene conversion, or horizontal gene transfer in sequence data. Five separate statistical signals have been commonly used to detect recombinant sequences, and two of these - mosaic signals and phylogenetic incongruence signals - have emerged as the preferred methods for generating evidence for recombination. I will review the derivation of a non-parametric mosaic statistic called Delta_mn2 that forms the basis of the 3SEQ recombination detection algorithm. The sensitivity, specificity, and exact p-values reported by 3SEQ give it some advantages as a screening tool for recombination in large data sets. I will show how to derive clonal subsegments, or breakpoint-free regions (BFRs), using this approach. And I will show how we have used this screening approach inidentifying recombinants in sarbecoviruses and SARS-CoV-2. [more]

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