Abstract

Any growing population of RNA viruses will undergo processes of mutation and selection resulting in a mixed population of viral particles. High throughput sequencing of a viral population subsequently contains a mixed signal of the underlying haplotypes that need identifying. We utilize a pigeon-hole principle to extract haplotype information, estimate the prevalence of underlying clones, and examine the evolutionary structure. The method utilises both linkage information from paired reads and depth information. The method utilises tree-like and recombination network evolution structures and has best applicability to time series data sets of high prevalence mutations within viral segments. Daily data from influenza are used to highlight the method, where both recombination within segments and re-assortment between segments are observed.