Identification of differential topologically associating domains from low sequencing depth and pseudo-bulk chromatin...

Topologically associating domains (TADs) are fundamental units of 3D genome architecture that shape gene regulation. Comparative analyses of TADs across biological conditions have revealed their involvement in development and disease. However, accurately identifying differential TADs from low sequencing depth and pseudo-bulk chromatin contact maps remains challenging. Here, we present HiDT, a graph neural network-based algorithm with an attention-based, edge-enhanced layer to capture structural differences between TADs. HiDT integrates a depth-specific normalization module and is trained across a wide range of sequencing depths, enabling robust detection of differential TADs under low sequencing depth conditions. Comprehensive benchmarking demonstrates that HiDT consistently outperforms existing methods at both TAD and subTAD levels, maintaining accuracy even in datasets with only a few million contacts. We further apply it to multiple low sequencing depth and pseudo-bulk datasets that are challenging for existing methods, revealing TAD reorganization linked to oncogene dysregulation during tumor progression, capturing differential TADs associated with underlying transcriptional heterogeneity in single-cell Hi-C data, and identifying haplotype-specific TADs associated with allele-specific structural variations. Overall, HiDT provides a robust tool for differential TAD analysis and facilitates insights into chromatin structure-function relationships.