SelfTarget

SelfTarget predicts mutations resulting from cellular repair of CRISPR-Cas9-induced double-strand breaks to inform gene-editing experiment design.

Key Features:

• Mutation Prediction: Predicts specific mutation types, including single-base insertions, short deletions, and microhomology-mediated deletions, produced by repair of CRISPR-Cas9-induced double-strand breaks.
• Sequence Dependency: Accounts for the influence of flanking DNA sequence context on repair outcomes and identifies sequence determinants that bias mutation spectra.
• Guide RNA (gRNA) Specificity: Incorporates data from over 40,000 gRNAs tested in synthetic constructs across diverse genetic backgrounds and with different CRISPR-Cas9 reagents to capture gRNA- and cell-line-dependent effects.
• Data-Driven Insights: Analyzes more than one billion mutational outcomes to derive predictive patterns of DNA repair following Cas9 cleavage.
• Predictive Model (FORECasT): Implements the FORECasT computational predictor derived from extensive empirical mutational outcome data.

Scientific Applications:

• Gene editing design: Enables design of gene-editing experiments by predicting repair outcomes and informing gRNA selection.
• DNA repair research: Supports investigation of double-strand break repair mechanisms by revealing sequence determinants of mutational outcomes after CRISPR-Cas9 cleavage.

Methodology:

Predictive models were derived from systematic experiments using synthetic constructs across diverse genetic backgrounds and alternative CRISPR-Cas9 reagents, and from analysis of extensive mutational outcome datasets.