Nucleotide Excision Repair in Saccharomyces cerevisiaeWhole-Cell Extracts
Bibliographic record
Abstract
Nucleotide excision repair (NER) is a particularly versatile pathway of DNA repair capable of removing a broad spectrum of DNA lesions in both prokaryotes and eukaryotes ( 1 – 3 ). NER involves steps of damage recognition, incision and excision of the lesion and its flanking DNA, and repair DNA synthesis to fill in the resulting single-stranded gap. Here we describe the techniques used to prepare extracts from Saccharomyces cerevisiae cells capable of performing NER reactions and the details of this in vitro NER assay. Studies of DNA repair in S. cerevisiae have the advantage of being amenable to powerful genetic analyses within a completely sequenced yeast genome. In fact, most of the earlier work on NER in yeast relied on the genetic analyses of rad mutants. On the other hand, the potential for biochemical analysis of NER in yeast has not yet been fully realized owing in part to the lack of a simple in vitro repair system. This is in contrast to the situation in human cells in which the in vitro system developed by Wood and his colleagues ( 4 ; see Chapter 29 ) has proven instrumental in dissecting the human pathway of NER. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".