Inferring the Clonal Structure of Viral Populations from Time Series Sequencing

TL;DR

This paper presents a method to infer the clonal structure of viral populations from time series sequencing data, revealing evolutionary relationships and reassortment events despite sequencing artifacts.

Contribution

It introduces an approach combining linkage and mutation prevalence data to identify viral clone haplotypes and evolutionary structures, including reticulate events.

Findings

Successfully inferred clone haplotypes and evolutionary structures.

Demonstrated the method on influenza infection data.

Highlighted challenges from sequencing artifacts like PCR switching.

Abstract

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 subsequently contains a mixed signal of the underlying clones. We would like to identify the underlying evolutionary structures. We utilize two sources of information to attempt this; within segment linkage information, and mutation prevalence. We demonstrate that clone haplotypes, 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.