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

## TL;DR

UniChrom is a deep learning model that accurately predicts chromatin interactions across different cell types and genomic scales, offering insights into genome organization and gene regulation.

## Contribution

UniChrom introduces a novel attention-based deep learning framework for cross-scale and cross-lineage chromatin interaction prediction.

## Key findings

- UniChrom outperforms existing methods in predicting chromatin interactions across multiple cell lines.
- It achieves high accuracy for long-range interactions exceeding 1.77 megabases (AUC: 0.976).
- The model generalizes well to endothelial cells, showing strong cross-lineage performance (AUC: 0.962).

## Abstract

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.

The online version contains supplementary material available at 10.1186/s12864-026-12625-x.