Efficient Mutagenesis by Cas9 Protein-Mediated Oligonucleotide Insertion and Large-Scale Assessment of Single-Guide RNAs
Bibliographic record
Abstract
The CRISPR/Cas9 system has been implemented in a variety of model organisms to mediate site-directed mutagenesis. A wide range of mutation rates has been reported, but at a limited number of genomic target sites. To uncover the rules that govern effective Cas9-mediated mutagenesis in zebrafish, we targeted over a hundred genomic loci for mutagenesis using a streamlined and cloning-free method. We generated mutations in 85% of target genes with mutation rates varying across several orders of magnitude, and identified sequence composition rules that influence mutagenesis. We increased rates of mutagenesis by implementing several novel approaches. The activities of poor or unsuccessful single-guide RNAs (sgRNAs) initiating with a 5' adenine were improved by rescuing 5' end homogeneity of the sgRNA. In some cases, direct injection of Cas9 protein/sgRNA complex further increased mutagenic activity. We also observed that low diversity of mutant alleles led to repeated failure to obtain frame-shift mutations. This limitation was overcome by knock-in of a stop codon cassette that ensured coding frame truncation. Our improved methods and detailed protocols make Cas9-mediated mutagenesis an attractive approach for labs of all sizes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".