Comparison of Tools That Predict CRISPR-SpCas9 Guide RNA Effectiveness

Deep learning models exhibit greater accuracy and higher Spearman correlation coefficients in predicting CRISPR-Cas9 editing efficiency across multiple datasets.

• Seven machine learning and deep learning-based tools were benchmarked for predicting gRNA activities.
• The evaluation covered nine CRISPR datasets from six cell types and three species.
• Deep learning models outperformed traditional machine learning models in predictive performance.
• A comprehensive GuideNet resource web portal was compiled to facilitate sharing of CRISPR datasets.
• Key features influencing CRISPR gene editing activity were summarized, providing insights for future model development.

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