A chromatin-structure-guided framework for predictive and interpretable regulatory genomics.

Chromatin organization shapes gene regulation by linking distal elements across megabase scales, yet most predictive genomics models still treat the genome as linear, without incorporating 3D structure. Hi-C provides genome-wide chromatin conformation information, but its contact maps are population-averaged, distance-biased, and noisy, obscuring biologically specific contacts. We present CHROME, a framework built on a self-avoiding polymer ensemble null model that identifies physically specific, nonrandom Hi-C contacts. By integrating these contacts into graph representations, CHROME enables efficient information transfer across spatially connected loci. It integrates sequence, chromatin accessibility, or pretrained embeddings into a graph attention architecture to predict cell-line-specific ChIP-seq profiles, improving performance over matched local encoder baselines. In a held-out cell line, CHROME demonstrates improved performance in selected settings, suggesting potential for cross-cell-type transfer. The resulting graph embeddings also enhance prediction on tissue-specific eQTL and ClinVar variant pathogenicity, compared with local sequence-based embeddings. Beyond predictive performance, CHROME provides interpretability through attention-derived neighbor-to-center contributions that reveal how spatially connected loci influence local regulatory activity over multi-megabase distances. Together, these results highlight the value of incorporating physically validated chromatin interactions for improving regulatory prediction and variant interpretation.