HAT

HAT models and analyzes multi-way biological interactions using hypergraphs to interpret multi-way chromosome conformation capture (3C) and other multi-dimensional datasets.

Key Features:

• Hypergraph representation: Represents multi-way interactions as hypergraphs where hyperedges can connect multiple nodes simultaneously.
• Mathematical framework: Provides a mathematically robust framework for modeling and analyzing complex multi-way interaction datasets.
• Hypergraph theory application: Leverages hypergraph theory to detect and characterize structural and functional relationships within biological networks.
• Analysis tools: Offers analytical methods for studying properties of multi-dimensional and multi-way interaction data.
• Visualization: Includes capabilities for visualizing multi-way and multi-dimensional interaction datasets.
• Support for 3C data: Specifically supports analysis of multi-way chromosome conformation capture (3C) datasets.

Scientific Applications:

• Genomics: Analyzing spatial organization and interaction patterns of chromosomes to inform genomic studies.
• Gene regulation: Investigating how multi-way chromosomal interactions relate to regulation of gene expression.
• Chromosomal architecture: Characterizing chromosomal architecture from multi-way interaction data.
• Disease mechanisms: Studying alterations in multi-way interaction patterns associated with disease mechanisms.
• Systems and computational biology: Supporting systems biology and computational biology investigations of complex biological networks.

Methodology:

Represents interaction data as hypergraphs (hyperedges connecting multiple nodes) and applies hypergraph theory for analysis and visualization of multi-way interaction datasets such as multi-way chromosome conformation capture (3C).