Samenvatting
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 genomeformula differences and comparing observations to a reference genome formula value. Third, we reanalyze published data to illustrate the applications and weigh the evidence for previous conclusions. Our reanalysis 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 genomeformula data.
Originele taal2  Engels 

Artikelnummer  270 
Tijdschrift  Viruses 
Volume  16 
Nummer van het tijdschrift  2 
DOI's  
Status  Gepubliceerd  08 feb. 2024 
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data and code from: Robust Approaches to the Quantitative Analysis of Genome Formula Variation in Multipartite and Segmented Viruses
Johnson, M. (Maker) & Zwart, M. (Maker), Zenodo, 14 feb. 2024
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