TreeKnit: Inferring ancestral reassortment graphs of influenza viruses

Pierre Barrat-Charlaix, Timothy G Vaughan and Richard A Neher

PLOS Computational Biology, 2022, vol. 18, issue 8, 1-19

Abstract: When two influenza viruses co-infect the same cell, they can exchange genome segments in a process known as reassortment. Reassortment is an important source of genetic diversity and is known to have been involved in the emergence of most pandemic influenza strains. However, because of the difficulty in identifying reassortment events from viral sequence data, little is known about their role in the evolution of the seasonal influenza viruses. Here we introduce TreeKnit, a method that infers ancestral reassortment graphs (ARG) from two segment trees. It is based on topological differences between trees, and proceeds in a greedy fashion by finding regions that are compatible in the two trees. Using simulated genealogies with reassortments, we show that TreeKnit performs well in a wide range of settings and that it is as accurate as a more principled bayesian method, while being orders of magnitude faster. Finally, we show that it is possible to use the inferred ARG to better resolve segment trees and to construct more informative visualizations of reassortments.Author summary: Influenza viruses evolve quickly and escape immune defenses which requires frequent update of vaccines. Understanding this evolution is key to an effective public health response. The genome of influenza viruses is made up of 8 pieces called segments, each coding for different viral proteins. Within each segment, evolution is an asexual process in which genetic diversity is generated by mutations. But influenza also diversifies through reassortment which can occur when two different viruses infect the same cell: offsprings can then contain a combination of segments from both viruses. Reassortment is akin to sexual reproduction and can generate viruses that combine segments from diverged viral lineages. Reassortment is a crucial component of viral evolution, but it is challenging to reconstruct where reassortments happened and which segments share history. Here, we develop a method called TreeKnit to detect reassortment events. TreeKnit is based on genealogical trees of single segments that can be reconstructed using standard bioinformatics tools. Inconsistencies between these trees are then used as signs of reassortment. We show that TreeKnit is as accurate as other recent methods, but runs much faster. Our method will facilitate the study of reassortment and its consequences for influenza evolution.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1010394

DOI: 10.1371/journal.pcbi.1010394

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