Recombination analysis of Soybean mosaic virus sequences reveals evidence of RNA recombination between distinct pathotypes
Bibliographic record
Abstract
RNA recombination is one of the two major factors that create RNA genome variability. Assessing its incidence in plant RNA viruses helps understand the formation of new isolates and evaluate the effectiveness of crop protection strategies. To search for recombination in Soybean mosaic virus (SMV), the causal agent of a worldwide seed-borne, aphid-transmitted viral soybean disease, we obtained all full-length genome sequences of SMV as well as partial sequences encoding the N-terminal most (P1 protease) and the C-terminal most (capsid protein; CP) viral protein. The sequences were analyzed for possible recombination events using a variety of automatic and manual recombination detection and verification approaches. Automatic scanning identified 3, 10, and 17 recombination sites in the P1, CP, and full-length sequences, respectively. Manual analyses confirmed 10 recombination sites in three full-length SMV sequences. To our knowledge, this is the first report of recombination between distinct SMV pathotypes. These data imply that different SMV pathotypes can simultaneously infect a host cell and exchange genetic materials through recombination. The high incidence of SMV recombination suggests that recombination plays an important role in SMV evolution. Obtaining additional full-length sequences will help elucidate this role.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".