Application of Next Generation Sequencing for Diagnostic Testing of Tree Fruit Viruses and Viroids
Bibliographic record
Abstract
Conventional detection of viruses and virus-like diseases of plants is accomplished using a combination of molecular, serological, and biological indexing. These are the primary tools used by plant virologists to monitor and ensure trees are free of known viral pathogens. The biological indexing assay, or bioassay, is considered to be the "gold standard" as it is the only method of the three that can detect new, uncharacterized, or poorly characterized viral disease agents. Unfortunately, this method is also the most labor intensive and can take up to three years to complete. Next generation sequencing (NGS) is a technology with rapidly expanding possibilities including potential applications for the detection of plant viruses. In this study, comparisons are made between tree fruit testing by conventional and NGS methods, to demonstrate the efficacy of NGS. A comparison of 178 infected trees, many infected with several viral pathogens, demonstrated that conventional and NGS were equally capable of detecting known viruses and viroids. Comparable results were obtained for 170 of 178 of the specimens. Of the remaining eight specimens, some discrepancies were observed between viruses detected by the two methods, representing less than 5% of the specimens. NGS was further demonstrated to be equal or superior for the detection of new or poorly characterized viruses when compared with a conventional bioassay. These results validated both the effectiveness of conventional virus testing methods and the use of NGS as an additional or alternative method for plant virus detection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".