Investigation of recombination-intense viral groups and their genes in the Earthâs virome
Bibliographic record
Abstract
© 2018, The Author(s). Bacteriophages (phages), or bacterial viruses, are the most abundant and diverse biological entities that impact the global ecosystem. Recent advances in metagenomics have revealed their rampant abundance in the biosphere. A fundamental aspect of bacteriophages that remains unexplored in metagenomic data is the process of recombination as a driving force in evolution that occurs among different viruses within the same bacterial host. Here, we systematically examined signatures of recombination in every gene from 211 species-level viral groups in a recently obtained dataset of the Earth’s virome that contain corresponding information on the host bacterial species. Our study revealed that signatures of recombination are widespread (84%) among the diverse viral groups. We identified 25 recombination-intense viral groups, widely distributed across the viral taxonomy, and present in bacterial species living in the human oral cavity. We also revealed a significant inverse association between the recombination-intense viral groups and Type II restriction endonucleases, that could be effective in reducing recombination among phages in a cell. Furthermore, we identified recombination-intense genes that are significantly enriched for encoding phage morphogenesis proteins. Changes in the viral genomic sequence by recombination may be important to escape cleavage by the host bacterial immune systems.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".