sgRNA-PSM

sgRNA-PSM predicts the on-target activity of single-guide RNAs (sgRNAs) for CRISPR-Cas9 genome editing and synthetic biology applications.

Key Features:

• On-target prediction: Predicts on-target activity of single-guide RNAs (sgRNAs) within the CRISPR-Cas9 system.
• Long-range sequence integration: Integrates long-range sequence information.
• Evolutionary data incorporation: Incorporates evolutionary data into predictions.
• Dimensionality reduction: Reduces the dimensionality of the feature vector to mitigate overfitting.
• Cross-validation strategy: Evaluated using leave-one-gene-out cross-validation.
• Benchmark datasets: Evaluated on benchmark datasets comprising 11 human genes and 6 mouse genes.
• Independent testing: Tested against an independent dataset and reported superior performance compared to existing methods.

Scientific Applications:

• Guide selection for precise genome modifications: Supports selection of highly active sgRNAs for precise genome editing with CRISPR-Cas9.
• Synthetic biology: Facilitates CRISPR-Cas9 applications in synthetic biology workflows.
• Theoretical RNA research: Enables theoretical research into RNA functions by improving sgRNA activity prediction.

Methodology:

Integrates long-range sequence information and evolutionary data, applies dimensionality reduction to the feature vector, and was validated using leave-one-gene-out cross-validation on benchmark datasets of 11 human genes and 6 mouse genes and tested on an independent dataset.