TADCompare

TADCompare identifies and characterizes differential Topologically Associated Domains (TADs) between multiple Hi-C contact matrices using a spectral clustering-derived measure called the eigenvector gap for loci-by-loci comparison of boundary differences.

Key Features:

• Differential TAD Detection: Finds differential TADs between two Hi-C datasets by comparing boundaries of interacting domains.
• Temporal TAD Analysis: Identifies differential TADs over time and tracks boundary changes across time points.
• Consensus TAD Identification: Identifies consensus TADs across multiple Hi-C contact matrices.
• Boundary Classification Framework: Provides a systematic classification framework for different types of boundary changes.
• Colocalization Analysis: Performs colocalization analysis of different types of boundary changes.
• Gene Enrichment Analysis: Performs gene enrichment analysis of different types of boundary changes to demonstrate distinct biological functionality.

Scientific Applications:

• Developmental Biology: Quantifies dynamic behavior of interacting domains during development to understand genome regulation.
• Disease Research: Identifies condition-specific differences in TADs and chromatin loops associated with disease states.
• Comparative Cell Biology: Compares interacting domains between different cell types using Hi-C data.
• Temporal Chromatin Dynamics: Tracks boundary changes of interacting domains over time to study chromatin conformation dynamics.

Methodology:

TADCompare is based on a spectral clustering-derived measure called the eigenvector gap, which enables a loci-by-loci comparison of boundary differences between two or more Hi-C datasets.