TreeKnit: Inferring ancestral reassortment graphs of influenza viruses

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 difficult...

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Veröffentlicht in:PLoS computational biology Jg. 18; H. 8; S. e1010394
Hauptverfasser: Barrat-Charlaix, Pierre, Vaughan, Timothy G., Neher, Richard A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: San Francisco Public Library of Science 19.08.2022
PLOS
Public Library of Science (PLoS)
Schlagworte:
Analysis
Bayesian analysis
Bioinformatics
Biology and Life Sciences
Chemical processes
Computer and Information Sciences
Computer Science
Evolution
Genealogy
Genetic diversity
Genomes
Genomics
Graphs
Influenza
Influenza viruses
Medicine and Health Sciences
Methods
Mutation
Orthomyxoviridae
Pandemics
Physical Sciences
Proteins
RNA
Segments
Trees
Trees (mathematics)
Viruses
Brazil
ISSN:1553-7358, 1553-734X, 1553-7358
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
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The authors have declared that no competing interests exist.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1010394