Comparable analysis of multiple DNA double-strand break repair pathways in CRISPR-mediated endogenous tagging
Bibliographic record
Abstract
Abstract CRISPR-mediated endogenous tagging, utilizing the homology-directed repair (HDR) of DNA double-strand breaks (DSBs) with exogenously incorporated donor DNA, is a powerful tool in biological research. Inhibition of the non-homologous end joining (NHEJ) pathway has been proposed as a promising strategy for improving the low efficiency of accurate knock-in via the HDR pathway. However, the influence of alternative DSB repair pathways on gene knock-in remains to be fully explored. In this study, our long-read amplicon sequencing analysis reveals various patterns of imprecise repair in CRISPR/Cas-mediated knock-in, even under conditions where NHEJ is inhibited. Suppression of the microhomology-mediated end joining (MMEJ) or the single strand annealing (SSA) repair mechanisms leads to a reduction in distinct patterns of imprecise repair, thereby elevating the efficiency of accurate knock-in. Furthermore, a novel reporter system shows that the SSA pathway contributes to a specific pattern of imprecise repair, known as asymmetric HDR. Collectively, our study uncovers the involvement of multiple DSB repair pathways in CRISPR/Cas-mediated gene knock-in and proposes alternative approaches to enhance the efficiency of precise gene knock-in.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".