Abstract CRISPR/Cas9 specificity is critically affected by off-target effects. However, the complex patterns of mismatches and their combinations at off-target sites remain difficult to capture, and existing approaches show limited capacity to identify informative features. Here, we present CrisprPr, a hybrid-driven off-target prediction framework that integrates both prior information and data-driven modeling to improve the characterization of off-target activity. CrisprPr employs a synchronous updating strategy that jointly optimizes prior-knowledge and deep-learning modules, together with multi-source integration, to deliver accurate and stable off-target predictions. Evaluations on independent test sets indicate that CrisprPr achieves competitive predictive performance and generalization compared with existing deep learning methods, with statistically significant improvements observed on several datasets. Beyond predictive performance, its analysis module examines the patterns of prior embedding-space updates to reveal distinctive target-site features supported by literature evidence. Overall, CrisprPr proposes a novel framework that demonstrates competitive predictive performance while offering new insights into the characteristics of off-target effects.
Wu et al. (Sun,) studied this question.
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