The corrected gene proximity map for analyzing the 3D genome organization using Hi-C data

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Genome-wide ligation-based assays like Hi-C reveal genome spatial organization.
• Hi-C data is often visualized as chromosomal contact maps showing genomic locus co-location frequencies.
• Network frameworks are powerful for elucidating complex genomic interactions.

Purpose of the Study:

• To introduce a novel graph-theoretical framework, the Corrected Gene Proximity (CGP) map.
• To analyze the impact of 3D genome organization on transcriptional regulation.
• To provide a network framework for global 3D genome structure analysis.

Main Methods:

• Constructing a weighted gene proximity network from Hi-C contact frequencies.
• Deriving a null model based on 1D genomic distance to correct for 3D arrangements.
• Applying the CGP map to human cell lines and hematopoietic cell lines.

Main Results:

• The CGP map effectively detects and quantifies gene co-regulation and co-localization compared to raw contact maps.
• Gene positioning, as quantified by CGP, strongly correlates with expression changes in metabolic pathways.
• The CGP map can identify large-scale abnormalities like chromosomal translocations via an inter-chromosomal proximity map.

Conclusions:

• The Corrected Gene Proximity map offers a global view of the 3D genome structure.
• It enables more effective simultaneous analysis of intra- and inter-chromosomal interactions, gene co-regulation, and co-localization.
• The CGP map reveals associations between genome structure and gene expression, with potential for generalization to other Hi-C datasets.