Graph-CRISPR: a gene editing efficiency prediction model based on graph neural network with integrated sequence and secondary structure feature extraction.

Clustered regularly interspaced short palindromic repeats (CRISPR) gene-editing technology has transformed molecular biology. Predicting editing efficiency is crucial for optimization, and numerous computational models have been created. However, many current models struggle to generalize across diverse editing systems, often experiencing performance drops with varying conditions or systems. Additionally, most models focus on ribonucleic acid (RNA) sequence and thermodynamic features, overlooking the importance of secondary structure information. Here, we present the first graph-based model (Graph-CRISPR) that integrates both sequence and secondary structure features of single guide RNA enhancing editing efficiency prediction. Tests show Graph-CRISPR consistently surpasses baseline models across systems like CRISPR-Cas9, prime editing, and base editing. It also demonstrates strong resilience, maintaining robust performance under varying experimental conditions. This work highlights the potential of integrating sequence and structural information through graph-based modeling to enhance predictive accuracy and adaptability in gene editing applications. The datasets and source codes are publicly available at: https://github.com/MoonLBH/Graph-CRISPR.