Integrating after CEN Excision (ICE) Plasmids: Combining the ease of yeast recombination cloning with the stability of genomic integration
Bibliographic record
Abstract
Yeast recombination cloning is a straightforward and powerful method for recombining a plasmid backbone with a specific DNA fragment. However, the utility of yeast recombination cloning is limited by the requirement for the backbone to contain an CEN/ARS element, which allows for the recombined plasmids to propagate. Although yeast CEN/ARS plasmids are often suitable for further studies, we demonstrate here that they can vary considerably in copy number from cell to cell and from colony to colony. Variation in plasmid copy number can pose an unacceptable and often unacknowledged source of phenotypic variation. If expression levels are critical to experimentation, then constructs generated with yeast recombination cloning must be subcloned into integrating plasmids, a step that often abrogates the utility of recombination cloning. Accordingly, we have designed a vector that can be used for yeast recombination cloning but can be converted into the integrating version of the resulting vector without an additional subcloning. We call these "ICE" vectors, for "Integrating after CEN Excision." The ICE series was created by introducing a "rare-cutter" NotI-flanked CEN/ARS element into the multiple cloning sites of the pRS series yeast integration plasmids. Upon recovery from yeast, the CEN/ARS is excised by NotI digest and subsequently religated without need for purification or transfer to new conditions. Excision by this approach takes ~3 hr, allowing this refinement in the same time frame as standard recombination cloning.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".