An improved view of influenza evolution with CoalRe
Channel: SIB - Swiss Institute of Bioinformatics
Date: 2020-10-16

What you will discover: a framework (and BEAST2 add-on) for phylogenetic inference on viral reassortment networks, including a tutorial on how to use CoalRe

You are: an evolutionary biologist or a virologist    

Speaker:  Ugnė Stolz, PhD student at ETH Zurich (thank you to Nicola F. Müller and Timothy G. Vaughan who contributed to the presentation!)

Click to access specific sections of the talk: 
1. Introduction to reassortment networks 00:37
2. The framework underlying CoalRe 03:18
3. Application to influenza 04:45
4. Tutorial 07:33

Reference paper
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.1918304117 

Other resources mentioned in this talk: 
- Tutorial to set up your own analysis: https://taming-the-beast.org/tutorials/Reassortment-Tutorial/
- BEAST2, a Bayesian phylogenetic analysis software: http://www.beast2.org
- CoalRe code repository (mostly for developers): https://github.com/nicfel/CoalRe

Any question about this talk? Contact Ugnė: ugne.stolz[at]bsse.ethz.ch

More about the speaker: 
Ugnė Stolz has a bachelor in Applied Mathematics from Vilnius University (VU) and masters 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 master 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. 

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This video is available under the creative commons license CC-BY-4.0 https://creativecommons.org/licenses/by/4.0/