Soil Metatranscriptomics: An Improved RNA Extraction Method Toward Functional Analysis Using Nanopore Direct RNA Sequencing
Bibliographic record
Abstract
Soil microbes play an undeniable role in sustainable agriculture, plant health, and soil management. A deeper understanding of soil microbial composition and function has been gained through next-generation sequencing. Although soil metagenomics has provided valuable information about microbial diversity, issues stemming from RNA extraction, low RNA abundance in some microbial populations (e.g., viruses), and messenger RNA enrichment have slowed the progress of soil metatranscriptomics. A variety of soil RNA extraction methods have been developed thus far yet none of the available protocols can obtain RNA with high quality, purity, and yield for third-generation sequencing. The latter requires RNA with high quality and large quantities (with no or low contamination such as humic acids). Also, use of commercial kits for in-batch soil RNA extraction is quite expensive, and these commercial kits lack buffer composition details, which prevents the optimization of protocols for different soil types. An improved and cost-effective method for extracting RNAs from mineral and organic soils is presented in this article. An acidic sodium acetate buffer and phosphate buffer with modifications to bead beating and nucleic acid precipitation lead to higher RNA yields and quality. Using this method, we obtained almost DNA-free RNA. By using nanopore's direct RNA sequencing, the extracted contamination-free RNAs were successfully sequenced. Finally, taxonomic groups such as bacteria, fungi, archaea, and viruses were classified and profiled, and functional annotation of the datasets was carried out using an in-house customized bioinformatics 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".