Expansion of viral genomes with viral protein genome linked copies
Bibliographic record
Abstract
Virus protein-linked genome (VPg) proteins are required for replication. VPgs are duplicated in a subset of RNA viruses however their roles are not fully understood and the extent of viral genomes containing VPg copies has not been investigated in detail. Here, we generated a novel bioinformatics approach to identify VPg sequences in viral genomes using hidden Markov models (HMM) based on alignments of dicistrovirus VPg sequences. From metagenomic datasets of dicistrovirus genomes, we identified 717 dicistrovirus genomes containing VPgs ranging from a single copy to 8 tandem copies. The VPgs are classified into nine distinct types based on their sequence and length. The VPg types but not VPg numbers per viral genome followed specific virus clades, thus suggesting VPgs co-evolved with viral genomes. We also identified VPg duplications in aquamavirus and mosavirus genomes. This study greatly expands the number of viral genomes that contain VPg copies and indicates that duplicated viral sequences are more widespread than anticipated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".