The genome of a bunyavirus cannot be defined at the level of the viral particle but only at the scale of the viral population

Aspects of the lifestyle of multipartite viruses apply to monopartite segmented and perhaps nonsegmented viruses

Recent research on faba bean necrotic stunt virus, aiming to understand how multipartite viruses function and potentially their existence, revealed three surprising features: a non-uniform segment frequency distribution (genome formula), a multicellular functioning, and the non-concomitant transmission of genomic segments. We review the occurrence of these features in other multipartite viruses and discuss their potential operation in monopartite viruses with segmented genomes and perhaps even in viruses with nonsegmented genomes.

Robust Approaches to the Quantitative Analysis of Genome Formula Variation in Multipartite and Segmented Viruses

When viruses have segmented genomes, the set of frequencies describing the abundance of segments is called the genome formula. The genome formula is often unbalanced and highly variable for both segmented and multipartite viruses. A growing number of studies are quantifying the genome formula to measure its effects on infection and to consider its ecological and evolutionary implications. Different approaches have been reported for analyzing genome formula data, including qualitative description, applying standard statistical tests such as ANOVA, and customized analyses. However, these approaches have different shortcomings, and test assumptions are often unmet, potentially leading to erroneous conclusions. Here, we address these challenges, leading to a threefold contribution. First, we propose a simple metric for analyzing genome formula variation: the genome formula distance. We describe the properties of this metric and provide a framework for understanding metric values. Second, we explain how this metric can be applied for different purposes, including testing for genome-formula differences and comparing observations to a reference genome formula value. Third, we re-analyze published data to illustrate the applications and weigh the evidence for previous conclusions. Our re-analysis of published datasets confirms many previous results but also provides evidence that the genome formula can be carried over from the inoculum to the virus population in a host. The simple procedures we propose contribute to the robust and accessible analysis of genome-formula data.