Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses.

ABSTRACT: Reassortment is an important source of genetic diversity in segmented viruses and is the main source of novel pathogenic influenza viruses. Despite this, studying the reassortment process has been constrained by the lack of a coherent, model-based inference framework. Here, we introduce a coalescent-based model that allows us to explicitly model the joint coalescent and reassortment process. In order to perform inference under this model, we present an efficient Markov chain Monte Carlo algorithm to sample rooted networks and the embedding of phylogenetic trees within networks. This algorithm provides the means to jointly infer coalescent and reassortment rates with the reassortment network and the embedding of segments in that network from full-genome sequence data. Studying reassortment

SUBMITTER: Muller NF 

PROVIDER: S-EPMC7382287 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature