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Using Sniper-Cas9 to Minimize Off-target Effects of CRISPR-Cas9 Without the Loss of On-target Activity Via Directed Evolution
Published on: February 26, 2019
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Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities
Guishan Zhang1, Ye Luo1, Xianhua Dai2,3
1College of Engineering, Shantou University, Shantou 515063, China.
Briefings in Bioinformatics
|September 29, 2023
Summary
Deep learning models excel at predicting CRISPR/Cas9 single guide RNA (sgRNA) on- and off-target editing, especially with larger datasets. Performance on imbalanced datasets for off-target prediction requires further improvement.
Area of Science:
- Genomics and Molecular Biology
- Bioinformatics and Computational Biology
Background:
- CRISPR/Cas9 gene editing relies on effective single guide RNA (sgRNA) design for specificity and efficiency.
- Deep learning methods have shown promise in predicting sgRNA on-target activity and off-target mutations.
Approach:
- Systematic survey and performance evaluation of 10 deep learning on-target predictors across nine public datasets.
- Unbiased experimental comparison of eight off-target prediction methods on 12 public datasets with varying sample imbalance.
Key Points:
- Deep learning models generally perform better on large- and medium-scale datasets for on-target prediction.
- Most off-target prediction methods perform well on balanced datasets but struggle with imbalanced data.
- Performance of predictive models is sensitive to dataset size and sample balance.
Conclusions:
- This review offers comprehensive insights into the current state of CRISPR/Cas9 sgRNA activity prediction.
- Identifies areas for improvement in deep learning model development for both on- and off-target prediction.
- Highlights the need for robust methods that can handle imbalanced datasets in off-target analysis.
