<i>N</i> -Ethyl- <i>N</i> -Nitrosourea Mutagenesis: Boarding the Mouse Mutant Express
Bibliographic record
Abstract
In the mouse, random mutagenesis with N-ethyl-N-nitrosourea (ENU) has been used since the 1970s in forward mutagenesis screens. However, only in the last decade has ENU mutagenesis been harnessed to generate a myriad of new mouse mutations in large-scale genetic screens and focused, smaller efforts. The development of additional genetic tools, such as balancer chromosomes, refinements in genetic mapping strategies, and evolution of specialized assays, has allowed these screens to achieve new levels of sophistication. The impressive productivity of these screens has led to a deluge of mouse mutants that wait to be harnessed. Here the basic large- and small-scale strategies are described, as are the basics of screen design. Finally, and importantly, this review describes the mechanisms by which such mutants may be accessed now and in the future. Thus, this review should serve both as an overview of the power of forward mutagenesis in the mouse and as a resource for those interested in developing their own screens, adding onto existing efforts, or obtaining specific mouse mutants that have already been generated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".