Data mining of global sequence datasets markedly expands known diversity within the Tobamovirus genus
- 1Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia
Abstract
High-throughput sequencing has transformed RNA virus discovery, producing vast datasets that often contain unexplored sequences of known and novel viruses across diverse sample types. Large-scale reconstruction of viral genomes within specific taxonomic groups remains a lengthy, labor-intensive process constrained by fragmented assemblies, heterogeneous workflows, and the limitations of percent identity cut-offs for taxonomic placement. Here, we focused on Tobamovirus (family Virgaviridae), a genus that contains important emerging plant pathogens. Tobamoviruses are detectable in diverse environmental matrices and are thus expected to be present across a range of datasets. We mined global short-read datasets to uncover hidden tobamovirus diversity, combining 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 developed a reproducible Snakemake pipeline for quality control, de novo assembly, similarity searches, and preliminary taxonomic assignment, yielding 510 candidate tobamovirus contigs across 131 datasets. Following manual curation, we confirmed 228 tobamoviral contigs from 23 datasets based on BLAST similarity searches, coding sequence content, genome organization, and assessment of potential misassemblies. To expedite the curation process, we built a two-stage machine learning classifier of tobamoviral contigs. Based on alignment-derived features and sequence properties, each ORF is first scored to determine whether it is tobamoviral. ORF-level predictions are then merged into contig-level classification using stacked generalization. Through clustering and phylogenetic analyses, we identified at least 32 putative novel tobamoviruses in datasets derived from freshwater, wastewater, plants, animal gut, and soil. Most were placed within established Tobamovirus subclades, whereas a few highly divergent sequences formed distinct basal branches related to recently identified soil-associated sequences classified as Virgaviridae sp. These findings highlight the value of aquatic and wastewater systems for expanding our understanding of tobamoviral diversity and demonstrate how data mining and targeted curation can substantially expand recognized diversity within the genus. The workflow is readily adaptable to other viral taxa and supports continuous discovery as public datasets grow.
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BibTeX
@misc{arambasic2026data,
title = {{Data mining of global sequence datasets markedly expands known diversity within the Tobamovirus genus}},
author = {N. Pajek Arambasic and K. Bacnik and L. Kranjc and L. Vogrinec and T. Curk and D. Kutnjak},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.22.760918},
url = {https://www.biorxiv.org/content/10.64898/2026.10.22.760918v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Data mining of global sequence datasets markedly expands known diversity within the Tobamovirus genus
AU - N. Pajek Arambasic
AU - K. Bacnik
AU - L. Kranjc
AU - L. Vogrinec
AU - T. Curk
AU - D. Kutnjak
PY - 2026
DA - 2026/10/22
PB - bioRxiv
DO - 10.64898/2026.10.22.760918
UR - https://www.biorxiv.org/content/10.64898/2026.10.22.760918v1
AB - High-throughput sequencing has transformed RNA virus discovery, producing vast datasets that often contain unexplored sequences of known and novel viruses across diverse sample types. Large-scale reconstruction of viral genomes within specific taxonomic groups remains a lengthy, labor-intensive process constrained by fragmented assemblies, heterogeneous workflows, and the limitations of percent identity cut-offs for taxonomic placement. Here, we focused on Tobamovirus (family Virgaviridae), a genus that contains important emerging plant pathogens. Tobamoviruses are detectable in diverse environmental matrices and are thus expected to be present across a range of datasets. We mined global short-read datasets to uncover hidden tobamovirus diversity, combining 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 developed a reproducible Snakemake pipeline for quality control, de novo assembly, similarity searches, and preliminary taxonomic assignment, yielding 510 candidate tobamovirus contigs across 131 datasets. Following manual curation, we confirmed 228 tobamoviral contigs from 23 datasets based on BLAST similarity searches, coding sequence content, genome organization, and assessment of potential misassemblies. To expedite the curation process, we built a two-stage machine learning classifier of tobamoviral contigs. Based on alignment-derived features and sequence properties, each ORF is first scored to determine whether it is tobamoviral. ORF-level predictions are then merged into contig-level classification using stacked generalization. Through clustering and phylogenetic analyses, we identified at least 32 putative novel tobamoviruses in datasets derived from freshwater, wastewater, plants, animal gut, and soil. Most were placed within established Tobamovirus subclades, whereas a few highly divergent sequences formed distinct basal branches related to recently identified soil-associated sequences classified as Virgaviridae sp. These findings highlight the value of aquatic and wastewater systems for expanding our understanding of tobamoviral diversity and demonstrate how data mining and targeted curation can substantially expand recognized diversity within the genus. The workflow is readily adaptable to other viral taxa and supports continuous discovery as public datasets grow.
ER -