Seeing the forest through the trees: prioritising potentially functional interactions from Hi-C

Area of Science:

• Genomics
• Molecular Biology
• Cell Biology

Background:

• Eukaryotic genomes are organized in 3D nuclear space, enabling distal regulatory elements to interact with gene promoters.
• Chromosome conformation capture techniques like Hi-C provide insights into chromatin organization, compartmentalization, and gene expression.

Purpose of the Study:

• To review and classify downstream analysis methods for Hi-C data.
• To focus on techniques that prioritize the identification of potentially functional 3D genome interactions.

Main Methods:

• Collation and examination of existing downstream analysis approaches for Hi-C data.
• Classification of methods into structural-based discovery, statistical interaction detection, and epigenomic data integration.

Main Results:

• Hi-C data analysis is complicated by a large number of interactions, many non-functional.
• Three main categories of methods for prioritizing functional interactions are identified.
• Structural methods (A/B compartments, TADs), statistical detection, and epigenomic integration are key approaches.

Conclusions:

• Identifying functional 3D genome interactions from Hi-C data is challenging.
• A combination of structural, statistical, and epigenomic approaches is crucial for successful identification.
• Prioritizing functional interactions is key to understanding genome organization and gene regulation.