Data from: Systematic evaluation of horizontal gene transfer between eukaryotes and viruses
Bibliographic record
Abstract
Gene exchange between viruses and their hosts acts as a key facilitator of horizontal gene transfer and is hypothesized to be a major driver of evolutionary change. Our understanding of this process comes primarily from bacteria and phage co-evolution, but the mode and functional importance of gene transfers between eukaryotes and their viruses remains anecdotal. Here we systematically characterized viral-eukaryotic gene exchange across eukaryotic and viral diversity, identifying thousands of transfers, and revealing their frequency, taxonomic distribution, and projected functions. Eukaryote-derived viral genes, abundant in the Nucleocytoviricota, highlighted common strategies for viral host-manipulation, including metabolic reprogramming, proteolytic degradation, and extracellular modification. Furthermore, viral-derived eukaryotic genes implicate genetic exchange in the early evolution and diversification of eukaryotes, particularly through viral-derived glycosyltransferases, which have impacted structures as diverse as algal cell walls, trypanosome mitochondria, and animal tissues. These findings illuminate the nature of viral-eukaryotic gene exchange and its impact on the evolution of viruses and their eukaryotic hosts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".