Revisiting high throughput sequencing data used for plant virus detection in order to find evidence of nonviralplant pathogens and pests
Bibliographic record
Abstract
High-throughput sequencing (HTS), more specifically RNA-seq of plant tissue, has become an indispensable tool for plant virologists to detect and identify plant viruses. During the data analysis step, plant virologists typically compare the obtained sequences to reference virus databases only, which lead to our hypothesis that they might be missing possible traces of other pathogens in the data. In this study, we set up a community effort to re-analyze existing RNA-seq datasets used for virus detection to check for the potential presence of non-viral pathogens or pests. In total 101 datasets from 15 participants derived from 51 different plant species were re-analyzed, of which 37 were selected for subsequent in-depth analyses. In 29 of the 37 selected samples (78%), we found convincing traces of non-viral plant pathogens or pests (>100 reads per million). The most observed organism categories were fungi (15/37 datasets), insects (13/37) and mites (9/37). Nematodes were not observed and only a few samples showed the presence of plant pathogenic phytoplasmas (1/37), bacteria (3/37) and oomycetes (4/37). In conclusion, we were able to show that it is possible to detect non-viral pathogens or pests in these metatranscriptomics datasets, in this case primarily fungi, insects and mites. With this study, we hope to raise awareness among plant virologists that their data might be useful for fellow plant pathologists in other disciplines (bacteriology, mycology, entomology) as well.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".