Drying as an effective method to store soil samples for DNA-based microbial community analyses: a comparative study
Bibliographic record
Abstract
Soil sampling for environmental DNA in remote and semi-remote locations is often limited due to logistical constraints surrounding sample preservation, including no or limited access to a freezer. Freezing at - 20 °C is a common DNA preservation strategy, however, other methods such as desiccation, ethanol or commercial preservatives are available as potential alternative DNA preservation methods for room temperature storage. In this study, we assessed five preservation methods (CD1 solution, 95% Ethanol, Dry & Dry silica gel packs, RNAlater, LifeGuard) along with freezing at - 20 °C, against immediate extraction on organic and mineral soils for up to three weeks of preservation. We assessed direct effects on DNA concentration and quality, and used DNA metabarcoding to assess effects on bacterial and fungal communities. Drying with Dry & Dry led to no significant differences from immediate extraction. RNAlater led to lower DNA concentrations, but effects on community structures were comparable to freezing. CD1, LifeGuard and Ethanol either caused immediate significant shifts in community structure, degradation of DNA quality or changes in diversity metrics. Overall, our study supports the use of drying with silica gel packs as a cost-effective, and easily applied method for the short-term storage at room temperature for DNA-based microbial community analyses.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".