Embryo-Based Large Fragment Knock-in in Mammals: Why, How and What’s Next
Bibliographic record
Abstract
Endonuclease-mediated genome editing technologies, most notably CRISPR/Cas9, have revolutionized animal genetics by allowing for precise genome editing directly through embryo manipulations. As endonuclease-mediated model generation became commonplace, large fragment knock-in remained one of the most challenging types of genetic modification. Due to their unique value in biological and biomedical research, however, a diverse range of technological innovations have been developed to achieve efficient large fragment knock-in in mammalian animal model generation, with a particular focus on mice. Here, we first discuss some examples that illustrate the importance of large fragment knock-in animal models and then detail a subset of the recent technological advancements that have allowed for efficient large fragment knock-in. Finally, we envision the future development of even larger fragment knock-ins performed in even larger animal models, the next step in expanding the potential of large fragment knock-in in animal models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".