Comparative analysis of multiple DNA double-strand break repair pathways in CRISPR-mediated endogenous tagging
Bibliographic record
Abstract
CRISPR-mediated endogenous tagging is a powerful tool in biological research. Inhibiting the non-homologous end joining (NHEJ) pathway has been shown to improve the low efficiency of accurate knock-in via homology-directed repair (HDR). However, the influence of alternative double-stranded break (DSB) repair pathways on knock-in remains to be fully explored. In this study, our long-read amplicon sequencing analysis reveals various patterns of imprecise repair in CRISPR-mediated knock-in, even with NHEJ inhibition. Further suppressing either microhomology-mediated end joining (MMEJ) or single-strand annealing (SSA) reduces nucleotide deletions around the cut site, thereby elevating knock-in accuracy. Additionally, imprecise donor integration is reduced by inhibiting SSA, but not MMEJ. Particularly, SSA suppression reduced asymmetric HDR, a specific imprecise integration pattern, which we further confirm using a novel reporter system. These findings demonstrate the complex interplay of multiple DSB repair pathways in CRISPR-mediated knock-in and offer novel strategies, including SSA pathway targeting, to improve precise gene editing efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".