RF-Net 2: fast inference of virus reassortment and hybridization networks

Abstract

A phylogenetic network is a powerful model to represent entangled evolutionary histories with both divergent (speciation) and convergent (e.g. hybridization, reassortment, recombination) evolution. The standard approach to inference of hybridization networks is to (i) reconstruct rooted gene trees and (ii) leverage gene tree discordance for network inference. Recently, we introduced a method called RF-Net for accurate inference of virus reassortment and hybridization networks from input gene trees in the presence of errors commonly found in phylogenetic trees. While RF-Net demonstrated the ability to accurately infer networks with up to four reticulations from erroneous input gene trees, its application was limited by the number of reticulations it could handle in a reasonable amount of time. This limitation is particularly restrictive in the inference of the evolutionary history of segmented RNA viruses such as influenza A virus (IAV), where reassortment is one of the major mechanisms shaping the evolution of these pathogens.

Document Details

Document Type
Pub Defense Publication
Publication Date
Feb 12, 2022
Source ID
10.1093/bioinformatics/btac075

Entities

People

  • Alexey Markin
  • Oliver Eulenstein
  • Sanket Wagle
  • Tavis K Anderson

Organizations

  • Agricultural Research Service
  • Iowa State University
  • National Institute of Allergy and Infectious Diseases
  • National Institutes of Health
  • National Science Foundation
  • Oak Ridge Institute for Science and Education
  • United States Department of Defense
  • United States Department of Health and Human Services

Tags

Fields of Study

  • Biology
  • Computer science

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Educational Psychology
  • Virology (or Medical Virology).

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference