Sequence features associated with the cleavage efficiency of CRISPR/Cas9 system
Bibliographic record
Abstract
The CRISPR-Cas9 system has recently emerged as a versatile tool for biological and medical research. In this system, a single guide RNA (sgRNA) directs the endonuclease Cas9 to a targeted DNA sequence for site-specific manipulation. In addition to this targeting function, the sgRNA has also been shown to play a role in activating the endonuclease activity of Cas9. This dual function of the sgRNA likely underlies observations that different sgRNAs have varying on-target activities. Currently, our understanding of the relationship between sequence features of sgRNAs and their on-target cleavage efficiencies remains limited, largely due to difficulties in assessing the cleavage capacity of a large number of sgRNAs. In this study, we evaluated the cleavage activities of 218 sgRNAs using in vitro Surveyor assays. We found that nucleotides at both PAM-distal and PAM-proximal regions of the sgRNA are significantly correlated with on-target efficiency. Furthermore, we also demonstrated that the genomic context of the targeted DNA, the GC percentage, and the secondary structure of sgRNA are critical factors contributing to cleavage efficiency. In summary, our study reveals important parameters for the design of sgRNAs with high on-target efficiencies, especially in the context of high throughput applications.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".