Application of a sequence-based taxonomic classification method to uncultured and unclassified marine single-stranded RNA viruses in the order Picornavirales
Bibliographic record
Abstract
Abstract Metagenomics has altered our understanding of microbial diversity and ecology. This includes its applications to viruses in marine environments that have demonstrated their enormous diversity. Within these are RNA viruses, many of which share genetic features with members of the order Picornavirales; yet, very few of these have been taxonomically classified. The only recognized family of marine RNA viruses is the Marnaviridae, which was founded based on discovery and characterization of the species Heterosigma akashiwo RNA virus. Two additional genera of marine RNA viruses, Labyrnavirus (one species) and Bacillarnavirus (three species), were subsequently defined within the order Picornavirales but not assigned to a family. We have defined a sequence-based framework for taxonomic classification of twenty marine RNA viruses into the family Marnaviridae. Using RNA-dependent RNA polymerase (RdRp) phylogeny and distance-based analyses, we assigned the genera Labyrnavirus and Bacillarnavirus to the family Marnaviridae and created four additional genera in the family: Locarnavirus (four species), Kusarnavirus (one species), Salisharnavirus (four species) and Sogarnavirus (six species). We used pairwise capsid protein comparisons to delineate species within families, with 75 per cent identity as the species demarcation threshold. The family displays high sequence diversities and Jukes–Cantor distances for both the RdRp and capsid genes, suggesting that the classified viruses are not representative of all of the virus diversity within the family and that there are many more extant taxa. Our proposed taxonomic framework provides a sound classification system for this group of viruses that will have broadly applicable principles for other viral groups. It is based on sequence data alone and provides a robust taxonomic framework to include viruses discovered via metagenomic studies, thereby greatly expanding the realm of viruses subject to taxonomic classification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".