Using Time-Structured Data to Estimate Evolutionary Rates of Double-Stranded DNA Viruses
Bibliographic record
Abstract
Double-stranded (ds) DNA viruses are often described as evolving through long-term codivergent associations with their hosts, a pattern that is expected to be associated with low rates of nucleotide substitution. However, the hypothesis of codivergence between dsDNA viruses and their hosts has rarely been rigorously tested, even though the vast majority of nucleotide substitution rate estimates for dsDNA viruses are based upon this assumption. It is therefore important to estimate the evolutionary rates of dsDNA viruses independent of the assumption of host-virus codivergence. Here, we explore the use of temporally structured sequence data within a Bayesian framework to estimate the evolutionary rates for seven human dsDNA viruses, including variola virus (VARV) (the causative agent of smallpox) and herpes simplex virus-1. Our analyses reveal that although the VARV genome is likely to evolve at a rate of approximately 1 x 10(-5) substitutions/site/year and hence approaching that of many RNA viruses, the evolutionary rates of many other dsDNA viruses remain problematic to estimate. Synthetic data sets were constructed to inform our interpretation of the substitution rates estimated for these dsDNA viruses and the analysis of these demonstrated that given a sequence data set of appropriate length and sampling depth, it is possible to use time-structured analyses to estimate the substitution rates of many dsDNA viruses independently from the assumption of host-virus codivergence. Finally, the discovery that some dsDNA viruses may evolve at rates approaching those of RNA viruses has important implications for our understanding of the long-term evolutionary history and emergence potential of this major group of 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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".