Assessing relationships between chromatin interactions and regulatory genomic activities using the self-organizing...

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Visualizing genome organization from Hi-C data and its relationship with regulatory genomic activities is challenging with existing heatmap methods.
• High-dimensional Hi-C data requires effective dimensionality reduction for intuitive analysis.
• Current 3D models, while contextually appropriate, may not suit all 2D-based analytical needs.

Purpose of the Study:

• To develop a novel 2D visualization and analysis approach for chromatin organization using Self-Organizing Maps (SOM).
• To enable intuitive assessment of relationships between regulatory genomic activities and chromatin interactions.
• To provide a platform for integrative analysis of genome compartmentalization.

Main Methods:

• Application of the Self-Organizing Map (SOM) algorithm to high-dimensional Hi-C data.
• Generation of a 2D manifold representing the chromatin interaction space.
• Utilizing Lorenz curve analysis for quantifying genomic activity compartmentalization on the SOM grid.

Main Results:

• The SOM algorithm successfully creates a 2D representation of high-dimensional Hi-C data, adapting to the chromatin interaction space.
• The 2D SOM grid allows intuitive visualization of genome compartmentalization and segregation of regulatory activities.
• Demonstrated exploratory analysis of genome compartmentalization using a high-resolution Hi-C dataset from human GM12878 cells.

Conclusions:

• The SOM-based approach offers an intuitive visualization of large-scale Hi-C data structure.
• This method serves as a valuable platform for integrative analyses linking genomic activities to genome organization.
• Facilitates a deeper understanding of the interplay between chromatin structure and regulatory functions.