Predicting Mutations Generated by Cas9, Base Editing, and Prime Editing in Mammalian Cells

Area of Science:

• Molecular Biology
• Bioinformatics
• Genetics

Background:

• CRISPR-Cas gene editing is advancing into clinical applications for genetic diseases.
• Precise control over gene editing outcomes, such as mutations, is crucial for therapeutic success.
• Editing outcomes can vary significantly based on the specific DNA target site.

Purpose of the Study:

• To review the current understanding and predictive capabilities for CRISPR-Cas cutting, base editing, and prime editing in mammalian cells.
• To provide foundational knowledge on DNA repair mechanisms and machine learning relevant to gene editing prediction.
• To consolidate information on datasets, methods, and insights for characterizing gene editing events at scale.

Main Methods:

• Overview of DNA repair pathways involved in CRISPR-Cas mediated editing.
• Introduction to machine learning principles underpinning predictive models.
• Survey of existing datasets and methodologies for large-scale characterization of gene edits.
• Analysis of insights derived from these datasets and methods.

Main Results:

• CRISPR-Cas, base editing, and prime editing outcomes can be predicted in mammalian cells.
• Machine learning models, trained on comprehensive datasets, offer insights into editing variations.
• Understanding DNA repair and editing patterns is essential for model development.

Conclusions:

• Predictive models are foundational for designing efficient gene editing experiments.
• Enhanced control over CRISPR-based gene editing outcomes is achievable through computational approaches.
• This review synthesizes current knowledge to guide future therapeutic development and research.