A new framework to improve phylogenetic inference on viral reassortment networks

An improved view of influenza evolution with CoalRe

“Accounting for reassortment events reduces bias in influenza inference”. In this in silico talk, SIB’s Ugnė Stolz from Tanja Stadler’s Group at ETH Zurich introduces CoalRe, a framework enabling to take into account reassortment when inferring virus evolution and infection pathways. In a recent paper published in PNAS, the team showed that this framework permits better estimates of effective population size and evolutionary rates. CoalRe is freely available as an add-on to the BEAST2 phylogenetic software.

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Speaker
Name:
Ugnė Stolz
Institute:
ETH Zurich
Group:

Ugnė Stolz has a bachelor's degree in Applied Mathematics from Vilnius University (VU) and a master's degree in Computational Biology and Bioinformatics from the Swiss Federal Institute of Technology in Zürich (ETH Zürich). During her masters, she joined Tanja Stadler’s Computational Evolution group and worked on a project led by Nicola F. Müller and Timothy G. Vaughan. Together they implemented a CoalRe package for inference under the coalescent with reassortment model, which is an important tool in improving our understanding of the segmented viruses evolution. After her masters, Ugnė has stayed in the group and is currently a PhD student supervised by Timothy G. Vaughan and Tanja Stadler. She is continuing the work on reassortment networks as well as novel Bayesian methods for pathogen transmission trees inference.

Video
Duration:
11 minutes 12 seconds
License:

This video is available under the creative commons license CC-BY-4.0

This work was featured as part of the latest SIB Remarkable Outputs 2019: a list of ‘must-read’ works by SIB Members produced in 2019 and selected by a committee of SIB Group Leaders and Researchers

The pathway of a viral infection is usually inferred from a ‘snapshot’ of the current viral population by sampling infected individuals. This enables the reconstruction of the virus’ phylogenetic tree, which offers a way to look back in time at the history of an epidemic.

However, this inference approach is made much more difficult for specific viruses, such as influenza, whose genome is made up of several distinct RNA molecules. Such ‘segmented viruses’ have the property of exchanging the different parts of their parental genome as they replicate within hosts (reassortment events), leading to a hybrid progeny. A person infected by two different strains of the virus can thus exhibit novel variant strains.

For such viruses, infection pathways can no longer be defined by trees, but by networks, to include the cross-over aspect of reassortment. New evolutionary models are needed for this, and CoalRe is one such framework.

Listen to Ugnė as she presents the model, demonstrates its use for several influenza A and B strains, and guides you through a tutorial to understand how to apply and run CoalRe for your datasets.

Reference(s)

Müller N et al., Bayesian Inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses. PNAS 2020

DOI:
https://doi.org/10.1073/pnas.191830411

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