dsRNA-based viromics: A novel tool unveiled hidden soil viral diversity and richness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Viruses play a crucial role in agroecosystem functioning. However, few studies have examined the diversity of the soil virome, especially when it comes to RNA viruses. Despite the great progress in viral metagenomics and metatranscriptomics (metaviromics) toward RNA viruses characterization, soil RNA viruses’ ecology is embryonic compared to DNA viruses. We currently lack a wet lab. method to accurately unhide the true soil viral diversity. To overcome this limitation, we developed dsRNA-based methods capitalizing on our expertise in soil RNA extraction and dsRNA extraction ported from studies of phyllosphere viral diversity. This proposed method detected both RNA and DNA viruses and is proven to capture a greater soil virus diversity than existing methods, virion-associated nucleic enrichment, and metaviromics. Indeed, using this method we detected 284 novel RNA-dependent RNA polymerases and expanded the diversity of Birnaviridae and Retroviridae viral families to agricultural soil, which, to our knowledge, have never been reported in such ecosystem. The dsRNA-based method is cost-effective in terms of affordability and requirements for data processing, facilitating large-scale and high-throughput soil sample processing to unlock the potential of the soil virome and its impact on biogeochemical processes (e.g. carbon and nutrient cycling). This method can also benefit future studies of viruses in complex environments, for example, to characterize RNA viruses in the human gut or aquatic environment where RNA viruses are less studied mainly because of technical limitations.
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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 | 0.000 | 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 | 0.001 | 0.003 |
| 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 it