PSV-36 Optimization of Collection, Storage, and Extraction of Samples for Large-Scale 16s Sequencing Project
Bibliographic record
Abstract
Abstract Correlations between the microbiome of the horse and various physiological parameters continue to be determined. Most evaluations of the equine microbiome have been regionally focused with limited numbers or variability. In order to elucidate the role of the microbiome on equine health, a large-scale trial has been initiated with a primary objective of analyzing and characterizing the microbiome of the horse. A study of this scope relies on the horse owner to collect samples from various locations and ship them to a centralized lab. Therefore, collection protocols and extraction techniques need to be optimized to maintain sample integrity. A trial was conducted to evaluate the storage conditions and extraction methodologies necessary to develop an optimized method of sample collection, preparation, and extraction. Samples were collected from a single horse and subjected to one of four treatments in duplicate (1-fresh, 2-frozen, 3-stored in 99% EtOH, and 4-stored in a commercially available DNA/RNA transport medium; DNA/RNA Shield®, Zymo Research, Irvine, CA). Frozen samples were placed at -80◦ C immediately following collection and stored for 48-hr. Frozen samples were thawed immediately prior to extraction. All other treatments were stored as dictated by treatment at room temperature for 48-hr prior to extraction to mimic shipping. Samples were processed utilizing a commercially available DNA extraction kit (Quick-DNA™ Fecal/Soil Microbe Miniprep, Zymo Research). DNA extract was analyzed for quantity utilizing a Qubit Fluorometer (ThermoFisher, Waltham, MA), and quality via spectrophotometry (NanoDrop 2000; ThermoFisher, Waltham, MA). Swabs stored in DNA/RNA Shield® yielded higher concentrations of DNA with superior 260/280 values than other treatments. These data indicate that storage of fecal swabs in an appropriate buffer combined with optimized extraction protocols can deliver adequate extracts that can be utilized for 16s sequencing and validates the use of this method for large-scale microbiome analysis.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".