UniChrom: a universal deep learning architecture for cross-scale chromatin interaction prediction

Chromatin interactions regulate gene expression and genome organization, but computational prediction across cell types remains challenging. We developed UniChrom, a deep learning framework integrating DNA sequences and epigenomic features through attention-based neural networks to predict chromatin interactions. Evaluation across human lymphoblastoid, leukemia, and fibroblast cell lines demonstrates superior performance compared to existing methods, with fivefold cross-validation and Wilcoxon tests confirming statistical significance (p < 0.05). Distance-stratified analysis reveals robust performance across all genomic scales, including long-range interactions exceeding 1.77 megabases (AUC: 0.976). Independent validation on endothelial cells confirms cross-lineage generalization (AUC: 0.962). Bootstrapping analysis with 1,000 iterations validates performance stability with tight 95% confidence intervals. DeepSHAP interpretability identifies CTCF and cohesin components as dominant features alongside cell-type-specific histone modifications, while DeepLIFT reveals functional regulatory motifs at interaction anchors. UniChrom provides a statistically validated framework for investigating genome architecture across cellular contexts with potential applications in understanding gene regulation in development and disease.