NanoViromics: long-read sequencing of dsRNA for plant virus and viroid rapid detection
Bibliographic record
Abstract
There is a global need for identifying viral pathogens, as well as for providing certified clean plant materials, in order to limit the spread of viral diseases. A key component of management programs for viral-like diseases is having a diagnostic tool that is quick, reliable, inexpensive, and easy to use. We have developed and validated a dsRNA-based nanopore sequencing protocol as a reliable method for detecting viruses and viroids in grapevines. We compared our method, which we term direct-cDNA sequencing from dsRNA (dsRNAcD), to direct RNA sequencing from rRNA-depleted total RNA (rdTotalRNA), and found that it provided more viral reads from infected samples. Indeed, dsRNAcD was able to detect all of the viruses and viroids detected using Illumina MiSeq sequencing (dsRNA-MiSeq). Furthermore, dsRNAcD sequencing was also able to detect low-abundance viruses that rdTotalRNA sequencing failed to detect. Additionally, rdTotalRNA sequencing resulted in a false-positive viroid identification due to the misannotation of a host-driven read. Two taxonomic classification workflows, DIAMOND & MEGAN (DIA & MEG) and Centrifuge & Recentrifuge (Cent & Rec), were also evaluated for quick and accurate read classification. Although the results from both workflows were similar, we identified pros and cons for both workflows. Our study shows that dsRNAcD sequencing and the proposed data analysis workflows are suitable for consistent detection of viruses and viroids, particularly in grapevines where mixed viral infections are common.
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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 | 0.001 | 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) | 0.002 | 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".