RESEARCH ARTICLE Inferring the Clonal Structure of Viral Populations from Time Series Sequencing

RNA virus populations will undergo processes of mutation and selection resulting in a mixed population of viral particles. High throughput sequencing of a viral population subse-quently contains a mixed signal of the underlying clones. We would like to identify the under-lying evolutionary structures. We utilize two sources of information to attempt this; within segment linkage information, and mutation prevalence. We demonstrate that clone haplo-types, their prevalence, and maximum parsimony reticulate evolutionary structures can be identified, although the solutions may not be unique, even for complete sets of information. This is applied to a chain of influenza infection, where we infer evolutionary structures, including reassortment, and demonstrate some of the difficulties of interpretation that arise from deep sequencing due to artifacts such as template switching during PCR amplification. Author Summary Any functional influenza virus particle is made up of eight distinct RNA segments. There can be in the order of 106 such particles per mL of infected tissue. Furthermore, on aver-age, each new virus particle has a single mutation distinguishing the virus from its paren