SS-VIME: Single-Source Virome-Microbiome Extraction Protocol v1
Bibliographic record
Abstract
SS-VIME: Single-Source Virome-Microbiome Extraction Protocol For simultaneous profiling of viral, bacterial, and fungal communities from a single soil sample Naser Poursalavati, PhD Candidate in Soil Virology This extended cellulose column chromatography protocol represents an advancement based on our previous methods developed for soil samples: an improved total nucleic acids extraction technique [1] and a method for capturing double-stranded RNA (dsRNA) from soil extracts using cellulose column chromatography [2]. The latter was subsequently adapted for use in other media, such as fungal mycelium and plant tissues [3-5]. This updated protocol now captures not only dsRNA but also other microbiome components from a single sample, enabling simultaneous profiling of both the virome and other microbiome members. The DNA component has been successfully used to capture bacterial and fungal profiles, with potential to include other members like arbuscular mycorrhizal fungi (AMF). RNA can also be recovered separately using this method, making it suitable for evaluating functional aspects of the soil microbiome. This protocol is highly cost-effective, while providing excellent yield. For the first time, it allows the capture of both DNA and dsRNA in a unified process, offering a comprehensive view of the soil’s viral and microbial communities. Note: The associated manuscript, titled"SS-VIME: A Single-Source Virome-Microbiome Extraction Protocol Toward Comprehensive Soil Community Analysis" has been accepted for publication in Microbiology Spectrum. Until the manuscript DOI is available, please cite this protocol using its protocols.io DOI.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.056 | 0.081 |
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".