Automation protocol for high-efficiency and high-quality genomic DNA extraction from Saccharomyces cerevisiae
Bibliographic record
Abstract
Although many protocols have been previously developed for genomic DNA (gDNA) extraction from S. cerevisiae, to take advantage of recent advances in laboratory automation and DNA-barcode sequencing, there is a need for automated methods that can provide high-quality gDNA at high efficiency. Here, we describe and demonstrate a fully automated protocol that includes five basic steps: cell wall and RNA digestion, cell lysis, DNA binding to magnetic beads, washing with ethanol, and elution. Our protocol avoids the use of hazardous reagents (e.g., phenol, chloroform), glass beads for mechanical cell disruption, or incubation of samples at 100°C (i.e., boiling). We show that our protocol can extract gDNA with high efficiency both from cells grown in liquid culture and from colonies grown on agar plates. We also show results from gel electrophoresis that demonstrate that the resulting gDNA is of high quality.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.011 |
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".