Mining public datasets for novel tobamoviruses
Shotgun high-throughput sequencing has transformed RNA virus discovery, producing vast datasets that, even if not generated for virus research, often contain unexplored sequences of known and novel viruses across diverse sample types. These could be accessed through data mining; however, despite the emergence of new data mining tools, large-scale reconstruction of viral genomes within specific taxonomic groups remains a lengthy process, constrained by fragmented assemblies, heterogeneous workflows, and the limitations of percent identity cut-offs for taxonomic placement. We aimed to address these challenges by focusing on the genus \textit{Tobamovirus} (family \textit{Virgaviridae}), whose members have virions that remain infectious and detectable in diverse environmental matrices and are thus expected to be present across a range of datasets. Recent outbreaks caused by emerging tobamoviruses further highlight the need to increase knowledge of the diversity of this genus. We mined global short-read datasets to uncover hidden tobamoviral diversity and combined expert curation with supervised machine learning to streamline the discovery of novel tobamoviruses. Using the Serratus PalmID tool, we first identified 253 candidate datasets that are most likely to contain novel tobamoviruses. We then built a reproducible Snakemake workflow that performed quality control, \textit{de novo} assembly, similarity searches, and preliminary taxonomic assignment, yielding 510 candidate tobamovirus contigs across 131 datasets. We manually curated the list of candidate contigs by integrating BLAST similarity search results, coding sequence content, genome organization, and checks for potential misassemblies. In parallel, to expedite this process, we built an open reading frame (ORF) level machine learning classifier that incorporated alignment-derived features and sequence properties, and we combined ORF predictions into contig-level labels through stacked generalization. Applying this framework, we identified at least 32 putative novel tobamoviruses across freshwater metatranscriptomes (Amazon River), European wastewater, plant-associated transcriptomes, animal gut datasets, and soil. Clustering and phylogenetic analyses placed most candidates within established \textit{Tobamovirus} subclades. At the same time, a few highly divergent sequences formed distinct basal branches related to recently identified soil-associated sequences classified as \textit{Virgaviridae} sp. These findings highlight aquatic and wastewater systems as rich informational reservoirs of tobamoviral diversity and demonstrate how scalable data mining, paired with targeted curation, can substantially expand recognized diversity within the genus. This workflow is readily adaptable to other viral taxa and supports continuous discovery as public datasets grow.