multiTAD: an Attention-Based Deep Learning Model for Identifying TAD Boundaries through Multi-Size Feature Integration

Topologically associating domains (TADs) are fundamental 3D genome structures that facilitate key gene regulatory interactions. The boundaries of TADs are rich in functional elements critical for maintaining structural integrity, making their identification essential for understanding the relationship between genome organization and gene expression. However, existing algorithms for identifying TAD boundaries often rely on fixed boundary sizes and neglect the varying predictive power of different features. To address these limitations, we introduce multiTAD, an advanced attention-based deep learning model that leverages 12 epigenetic signals to accurately detect TAD boundaries of diverse sizes. multiTAD significantly outperforms mainstream approaches, revealing distinct boundary size preferences across different cell lines. Additionally, multiTAD demonstrates strong cross-cell line predictive capabilities, further highlighting its broad applicability in genomic research.