FORECasT

FORECasT predicts the distribution of mutations arising from cellular repair of CRISPR-Cas9-induced double-strand breaks to anticipate gRNA-dependent and cell-line-specific editing outcomes.

Key Features:

• Systematic Analysis: Built upon a study of more than 40,000 guide RNAs (gRNAs) tested across synthetic constructs, multiple genetic backgrounds, and different CRISPR-Cas9 reagents.
• Data-Driven Insights: Integrates empirical measurements from over one billion mutational outcomes to capture the diversity of repair events.
• Mutation Prediction: Predicts common mutation types including single-base insertions, short deletions, and microhomology-mediated deletions.
• Sequence Determinants: Analyzes the influence of flanking DNA sequences on repair outcomes to identify sequence features that bias mutation types.
• Cell-Line-Specific Bias: Accounts for cell-line-dependent variations in repair outcomes, providing gRNA-specific bias information across genetic backgrounds.

Scientific Applications:

• Enhanced Gene Editing Design: Informs selection and design of gRNAs to achieve more predictable edits for applications including therapeutic gene editing.
• Phenotypic Impact Assessment: Provides predicted mutational spectra to assess potential phenotypic consequences and support experimental risk assessment.
• Optimization of CRISPR Tools: Guides optimization of gRNA choice and experimental design to improve efficiency and accuracy of CRISPR-Cas9 experiments.

Methodology:

Measures edits generated from a large set of gRNAs across varied conditions, compiles empirical mutational outcomes, and derives predictive models that relate sequence context and cell-line background to mutation distributions.