Nanovirseq: dsRNA sequencing for plant virus and viroid detection by Nanopore sequencing
Bibliographic record
Abstract
Abstract Worldwide, there is a need for certified clean plant materials to limit viral diseases spread. In order to design a robust and proactive viral-like disease certification, diagnostics, and management program, it is essential to have a fast, inexpensive, and user-friendly tool. The purpose of this study was to determine whether dsRNA-based nanopore sequencing can be a reliable method for the detection of viruses and viroids in grapevines or not. Compared to direct RNA sequencing from rRNA-depleted total RNA (rdTotalRNA), direct-cDNA sequencing from dsRNA (dsRNAcD) yielded more viral reads and detected all grapevine viruses and viroids detected using Illumina MiSeq sequencing (dsRNA-MiSeq). With dsRNAcD sequencing it was possible to detect low abundance viruses (e.g., Grapevine red globe virus) where rdTotalRNA sequencing failed to detect them. Indeed, even after removing rRNA, rdTotalRNA sequencing yielded low viral read numbers. rdTotalRNA sequencing was not sensitive enough to detect all the viruses detected by dsRNA-MiSeq. In addition, there was a false positive identification of a viroid in the rdTotalRNA sequencing that was due to misannotation of a host-driven read. For quick and accurate reads classification, two different taxonomical classification workflows based on protein and nucleotide homology were evaluated in this study, namely DIAMOND&MEGAND (DIA&MEG) and Centrifuge&Recentrifuge (Cent&Rec), respectively. Virome profiles from both workflows were similar except for grapevine endophyte endornavirus (GEEV), which was only detected using DIA&MEG. However, because DIA&MEG’s classification is based on protein homology, it cannot detect viroid infection despite giving more robust results. Even though Cent&Rec’s virus and viroid detection workflow was faster (30 minutes) than DIA&MEG’s (two hours), it could not provide the details and information DIA&MEG was able to provide. As demonstrated in our study, nanopore dsRNAcD sequencing and the proposed data analysis workflows are suitable and reliable for viruses and viroids detection, especially in grapevine where viral mixed infection is common.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".