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

Authors: MARKIN, ALEXEY - Orise Fellow; WAGLE, SANKET - Iowa State University; Anderson, Tavis; EULENSTEIN, OLIVER - Iowa State University

Submitted to: Bioinformatics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/7/2022
Publication Date: 2/12/2022
Citation: Markin, A., Wagle, S., Anderson, T.K., Eulenstein, O. 2022. RF-Net 2: Fast inference of virus reassortment and hybridization networks. Bioinformatics. 38(8):2144-2152. Article btac075. https://doi.org/10.1093/bioinformatics/btac075.
DOI: https://doi.org/10.1093/bioinformatics/btac075

Interpretive Summary: The identification of genetically novel influenza A viruses (IAV) that contain genes derived from human-, swine-, or avian-origin IAV is critical for controlling infection in swine. These novel viruses may be undergoing rapid changes in genetic diversity that reduce the efficacy of vaccine control methods, and may also pose a greater risk to humans for zoonotic infection. In this study, we developed an algorithm that merged the evolutionary history of individual genes into a larger phylogenetic network describing the evolution of the virus. The accuracy of the algorithm was validated using whole genome swine IAV data with an evolutionary history that included transmission of human IAV into swine and subsequent reassortment. The algorithm was able to detect known reassortment events, along with additional events between divergent circulating swine IAV strains. The development of this algorithm provides computational support for swine IAV surveillance as it is able to objectively rank the novelty of swine IAV strains. These data may aid vaccine development through the objective targeting of novel IAV strains, and may help reduce the risk of interspecies transmission by identifying viruses that have pandemic potential.

Technical Abstract: Motivation: A phylogenetic network is a powerful model to represent entangled evolutionary histories with both divergent (speciation) and convergent (e.g., hybridization, reassortment, recombinaiton) 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. Results: Here we largely expand the functionality of RF-Net that makes it significantly more applicable in practice. Crucially, we introduce a fast extension to RF-Net, called Fast-RF-Net, that can handle large numbers or reticulations without sacrificing accuracy. Additionally, we develop automatic stopping criteria to select the appropriate number of reticulations heuristically and implement a beneficial feature for RF-Net to output error-corrected input gene trees. We then conduct a comprehensive study of the original method and its novel extensions and confirm their efficacy in practice using extensive simulation and empirical influenza virus studies. Availability: RF-Net 2 is available at https://github.com/flu-crew/rf-net-2.