Improving grapevine virus diagnostics: Comparative analysis of three dsRNA enrichment methods for high-throughput sequencing
Bibliographic record
Abstract
The extraction of double stranded (ds) RNA is a common enrichment method for the study, characterization, and detection of RNA viruses. In addition to RNA viruses, viroids, and some DNA viruses, can also be detected from dsRNA enriched extracts which makes it an attractive method for detecting a wide range of viruses when coupled with HTS. Several dsRNA enrichment strategies have been developed. The oldest utilizes the selective binding properties of dsRNA to cellulose. More recent methods are based on the application of anti-dsRNA antibodies and viral proteins with a specific affinity for dsRNA. All three methods have been used together with HTS for plant virus detection and study. To our knowledge, this is the first comparative study of three alternative dsRNA enrichment methods for virus and viroid detection through HTS using virus-infected, and healthy grapevine test plants. Extracts were performed in triplicate using methods based on, the anti-dsRNA antibody mAb rJ2 (Millipore Sigma Canada Ltd, Oakville, ON, Canada), the B2 dsRNA binding protein, and ReliaPrep™ Resin (Promega Corporation, Madison, WI, USA). The results show that the workflows for all three methods are effectively comparable, apart from purification steps related to antibody and binding protein construct. Both the cellulose resin and dsRNA binding protein construct methods provide highly enriched dsRNA extracts suitable for HTS with the B2 method providing a 36× and the ReliaPrep™ Resin a 163× increase in dsRNA enrichment compared to the mAb rJ2 antibody. The overall consistency and cost effectiveness of the ReliaPrep™ cellulose resin-based method and the potentially simpler adaptation to robotics made it the method of choice for future transfer to a semi-automated workflow.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".