Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• CRISPR genome editing is powerful but limited by off-target effects.
• Existing computational tools struggle with accurate genome-wide off-target prediction due to limited mechanistic understanding.

Purpose of the Study:

• To develop an integrated computational tool suite, CRISOT, for analyzing CRISPR systems.
• To improve the prediction, evaluation, and optimization of CRISPR targeting specificity.

Main Methods:

• Incorporation of molecular dynamics (MD) simulations into computational analysis.
• Development of CRISOT tool suite with modules for fingerprint generation, off-target prediction, specificity evaluation, and optimization.
• Comprehensive computational and experimental validation.

Main Results:

• CRISOT outperforms existing tools in genome-wide off-target prediction.
• CRISOT demonstrates high accuracy in predicting off-target effects for Cas9, base, and prime editors.
• The derived RNA-DNA interaction fingerprint captures underlying mechanisms across different CRISPR systems.

Conclusions:

• CRISOT offers an efficient and generalizable framework for CRISPR off-target analysis.
• The tool suite enables improved targeting specificity in genome editing applications.
• CRISOT's approach has broad applicability to various CRISPR-based editing systems.