CRISCross: Integration of Epigenetic Information for Deep-Learning-Based Prediction of CRISPR-Cas9 Off-Targets Across Cell Types
CRISPR-Cas9 enables precise genome editing but remains limited by off-target effects, raising safety concerns and necessitating accurate prediction. Although epigenetic features influence Cas9 binding and cleavage, their predictive signals are often weak, highlyvariable, and context-dependent, limiting the effectiveness of existing machine learning approaches. Cross-cell-type settings are especially challenging: epigenetic landscapes differ substantially, and models trained on one cell type frequently fail to generalize. Here, we systematically investigate the role of epigenetic context in off-target prediction and demonstrate that its utility depends on modeling interactions with sequence features rather than treating it as an independent signal. Tabular and deep learning experiments demonstrate that epigenetic features provide a consistent but modestly complementary signal, and that late-stage fusion strategies are insufficient to effectively exploit this information. To address this, we introduce CRISCross, a cross-attention-based architecture that captures sequence-epigenetic interactions at the nucleotide level, alongside CRISPRAT, a synthetic pretraining strategy that learns transferable representations from genome-derived sgRNA-off-target pairs without relying on dataset-specific pretraining. CRISCross, incorporating AlphaGenome-derived chromatin accessibility signals, shows better predictive performance than sequence-only baselines across three independent test datasets spanning two cell types, demonstrating improved generalization across guide RNAs, datasets, and cellular contexts. This is particularly relevant for therapeutic applications involving cell types or patient-derived contexts that lack dedicated experimental datasets. In such settings, predicted chromatin accessibility may provide scalable contextual information when direct epigenomic profiling is unavailable or impractical. Together, these results show that accurate CRISPR off-target prediction benefits from interaction-aware integration of epigenetic context alongside generalizable pretraining strategies.