Efficient <scp>CRISPR</scp> /Cas9 mediated large insertions using long single‐stranded oligonucleotide donors in <i>C. elegans</i>
Bibliographic record
Abstract
Highly efficient generation of deletions, substitutions, and small insertions (up to ~ 150 bp) into the Caenorhabditis elegans genome by CRISPR/Cas9 has been facilitated by the use of single-stranded oligonucleotide donors as repair templates. However, insertion of larger sequences such as fluorescent markers and other functional domains remains challenging due to uncertainty of optimal performance between single-stranded or double-stranded repair templates and labor-intensive as well as inefficient protocols for their preparations. Here, we simplify the generation of long ssDNA as donors in CRISPR/Cas9. High yields of ssDNA can be rapidly generated using a standard PCR followed by a single enzymatic digest with lambda exonuclease. Comparison of long ssDNA donors obtained using this method to dsDNA demonstrates orders of magnitude increased insertion frequency for ssDNA donors. This can be leveraged to simultaneously generate multiple large insertions as well as successful edits without the use of selection or co-conversion (co-CRISPR) markers when necessary. Our approach complements the CRISPR/Cas9 toolkit for C. elegans to enable highly efficient insertion of longer sequences with a simple, standardized, and labor-minimal protocol.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".