source code for EMO

Non-coding mutations play a critical role in regulating gene expression, yet predicting their effects across diverse tissues and cell types remains a challenge. Here, we present EMO, a transformer-based model that integrates DNA sequence with chromatin accessibility data (ATAC-seq) to predict the regulatory impact of non-coding single nucleotide polymorphisms (SNPs) on gene expression. A key component of EMO is its ability to incorporate personalized functional genomic profiles, enabling individual-level and disease-contextual predictions, and addressing critical limitations of current approaches. EMO generalizes across tissues and cell types by modeling both short- and long-range regulatory interactions and capturing dynamic gene expression changes associated with disease progression. In benchmark evaluations, the pretraining-based EMO framework outperformed existing models, with fine-tuning small-sample tissues enhancing the model's ability to fit target tissues. In single-cell contexts, EMO accurately identified cell-type-specific regulatory patterns and successfully captured the effects of disease-associated SNPs in conditions, linking genetic variation to disease-relevant pathways.