DiCARN-DNase: Enhancing Cell-to-Cell Hi-C Resolution Using Dilated Cascading ResNet with Self-Attention and DNase-seq Chromatin Accessibility Data.

Abstract

Motivation: The spatial organization of chromatin is fundamental to gene regulation and essential for proper cellular function. The Hi-C technique remains the leading method for unraveling 3D genome structures, but the limited availability of high-resolution Hi-C data poses significant challenges for comprehensive analysis. Deep learning models have been developed to predict high-resolution Hi-C data from low-resolution counterparts. Early CNN-based models improved resolution but struggled with issues like blurring and capturing fine details. In contrast, GAN-based methods encountered difficulties in maintaining diversity and generalization. Additionally, most existing algorithms perform poorly in cross-cell line generalization, where a model trained on one cell type is used to enhance high-resolution data in another cell type.

Results: In this work, we propose DiCARN (Dilated Cascading Residual Network) to overcome these challenges and improve Hi-C data resolution. DiCARN leverages dilated convolutions and cascading residuals to capture a broader context while preserving fine-grained genomic interactions. Additionally, we incorporate DNase-seq data into our model, providing a robust framework that demonstrates superior generalizability across cell lines in high-resolution Hi-C data reconstruction.

Availability and implementation: DiCARN is publicly available at https://github.com/OluwadareLab/DiCARN.

Supplementary information: Supplementary figures and tables supporting this study are available in the Supplementary Materials document.