An Innovative Binding‐Protein‐Based <scp>dsRNA</scp> Extraction Method: Comparison of Cost‐Effectiveness of Virus Detection Methods Using High‐Throughput Sequencing
Bibliographic record
Abstract
Viral diseases represent a threat to global food production. Managing the impact of viruses on crop production requires the ability to monitor viruses, study their ecology and anticipate outbreaks. Double-stranded RNA (dsRNA) sequencing is a well-established and reliable method of detecting viruses and studying virome-host interactions and ecology. Compared to total RNA extraction, dsRNA extraction eliminates the majority of host RNAs, improving the recovery of viral RNAs. In this study, we developed and evaluated a novel dsRNA extraction method for high-throughput sequencing (HTS) applications based on the Flock House virus (FHV) B2 protein (B2-based method), and compared its performance with that of established cellulose-based and DRB4-based methods (commercial kit), as well as total RNA extraction techniques. The electrostatic properties of B2 have been instrumental in developing a bead-free and resin-free dsRNA extraction method. The B2-based method demonstrated high viral read recovery, achieving proportions exceeding 20% in most samples, and provided better dsRNA purity with less low weight molecule co-extracted RNA than the DRB4-based method and cellulose-based methods. Despite producing overall fewer total reads than the DRB4-based method, the B2-based enrichment for viral-derived dsRNA was better, with a higher percentage of viral reads, making it effective in virome profiling. Furthermore, it had an excellent detection specificity (0.97) and a good detection sensitivity (0.71), minimising false positives and false negatives. In addition, the B2-based method proved to be highly cost-effective, with a per-reaction cost of $4.47, compared to $35.34 for the DRB4-based method. This method offers a practical solution for laboratories with limited resources or for large-scale sampling for viral ecology studies. Future improvements to the B2-based method should focus on optimising sensitivity to Vitivirus species and developing scalable, automated workflows for high-throughput viral 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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".