Eukaryotic large nucleo-cytoplasmic DNA viruses: Clusters of orthologous genes and reconstruction of viral genome evolution
Bibliographic record
Abstract
BACKGROUND: The Nucleo-Cytoplasmic Large DNA Viruses (NCLDV) comprise an apparently monophyletic class of viruses that infect a broad variety of eukaryotic hosts. Recent progress in isolation of new viruses and genome sequencing resulted in a substantial expansion of the NCLDV diversity, resulting in additional opportunities for comparative genomic analysis, and a demand for a comprehensive classification of viral genes. RESULTS: A comprehensive comparison of the protein sequences encoded in the genomes of 45 NCLDV belonging to 6 families was performed in order to delineate cluster of orthologous viral genes. Using previously developed computational methods for orthology identification, 1445 Nucleo-Cytoplasmic Virus Orthologous Groups (NCVOGs) were identified of which 177 are represented in more than one NCLDV family. The NCVOGs were manually curated and annotated and can be used as a computational platform for functional annotation and evolutionary analysis of new NCLDV genomes. A maximum-likelihood reconstruction of the NCLDV evolution yielded a set of 47 conserved genes that were probably present in the genome of the common ancestor of this class of eukaryotic viruses. This reconstructed ancestral gene set is robust to the parameters of the reconstruction procedure and so is likely to accurately reflect the gene core of the ancestral NCLDV, indicating that this virus encoded a complex machinery of replication, expression and morphogenesis that made it relatively independent from host cell functions. CONCLUSIONS: The NCVOGs are a flexible and expandable platform for genome analysis and functional annotation of newly characterized NCLDV. Evolutionary reconstructions employing NCVOGs point to complex ancestral viruses.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".