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Updated: May 12, 2026

Combining Analysis of DNA in a Crude Virion Extraction with the Analysis of RNA from Infected Leaves to Discover New Virus Genomes
Published on: July 27, 2018
Comprehensive hallmark gene sequence, genomic and structural analysis clarifies new and established taxa within the
Richard Mayne1, Donald B Smith1, Katherine Brown2
1Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine, University of Oxford, 3 South Parks Road, Oxford OX1 3SY, United Kingdom.
Abstract:
The order Picornavirales is a group of highly diverse RNA viruses that includes many pathogens of significance to human and veterinary health, agriculture, and the wider environment. However, the wide range of viruses assigned to the order, together with their genomic variability, and the recent description of numerous 'picorna-like' viruses derived from metagenomic analyses of environmental samples, challenge the established taxonomic classification of members of the order and the criteria for their classification. Here, we combine the existing gold standard, hallmark RNA-directed RNA-polymerase (RdRP) gene sequence-based analysis with helicase sequence-based phylogeny, RdRP structural prediction through the use of ColabFold and Fold Tree, and analysis of coding-complete genomes using GRAViTy-V2, to genetically classify 525 picornaviral genomes and recently described 'picorna-like' viruses. All analyses were conducted with a bespoke, fully automated pipeline for retrieval of genome sequences, domain prediction and extraction, phylogenetic analysis, and output conditioning, which is available as open-source software. Our results reveal broad support for established families as well as for 6 novel families, and 32 new genera. In instances where inconsistencies were found between classification methods, we demonstrate how examination of the pipeline's output may be used to reconcile differences with respect to the genomic features quantified by the analysis. Automated multimodal taxonomic analysis may save significant resources over manual methods and better define demarcation criteria for families and genera.
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