GraphChIAr: genome-wide super-resolution reconstruction of protein-mediated remote chromatin interactions by...

Protein-mediated chromatin interactions are fundamental to gene regulation. However, experimental approaches such as Chromatin Interaction Analysis by Paired-End Tag sequencing are limited by data scarcity and high cost, while existing computational models are constrained by limited resolution and challenges in effectively integrating heterogeneous genomic data. To address these issues, we propose GraphChIAr, a regression-based deep learning framework that estimates chromatin interaction strength by augmenting Hi-C contact maps with various ChIP-seq profiles and genomic sequence information. A key advantage of GraphChIAr is its super-resolution capability, enabling accurate estimations of chromatin interactions from conventional resolutions down to ultra-high near-nucleosome resolution (e.g. 200 bp). By introducing genomic shift distance in GraphChIAr, we enabled it to predict remote interactions between distant genomic loci at a genome-wide scale. Cross-referencing results demonstrate high predictive accuracy for key mediating proteins such as CTCF, highlighting the benefits of integrating complementary genomic features. Together, GraphChIAr provides an effective computational tool to augment experimental data and advance the study of 3D genome organization. The source code of GraphChIAr is available at (https://github.com/don194/GraphChIAr).