BACKGROUND: The three-dimensional (3D) structure of the genome plays a crucial 
role in gene expression regulation. Chromatin conformation capture technologies 
(Hi-C) have revealed that the genome is organized in a hierarchy of 
topologically associated domains (TADs), sub-TADs, and chromatin loops. 
Identifying such hierarchical structures is a critical step in understanding 
genome regulation. Existing tools for TAD calling are frequently sensitive to 
biases in Hi-C data, depend on tunable parameters, and are computationally 
inefficient.
METHODS: To address these challenges, we developed a novel sliding window-based 
spectral clustering framework that uses gaps between consecutive eigenvectors 
for TAD boundary identification.
RESULTS: Our method, implemented in an R package, SpectralTAD, detects 
hierarchical, biologically relevant TADs, has automatic parameter selection, is 
robust to sequencing depth, resolution, and sparsity of Hi-C data. SpectralTAD 
outperforms four state-of-the-art TAD callers in simulated and experimental 
settings. We demonstrate that TAD boundaries shared among multiple levels of the 
TAD hierarchy were more enriched in classical boundary marks and more conserved 
across cell lines and tissues. In contrast, boundaries of TADs that cannot be 
split into sub-TADs showed less enrichment and conservation, suggesting their 
more dynamic role in genome regulation.
CONCLUSION: SpectralTAD is available on Bioconductor, 
http://bioconductor.org/packages/SpectralTAD/ .