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Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

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Published on: October 14, 2022

Advantages of using graph databases to explore chromatin conformation capture experiments.

Daniele D'Agostino¹, Pietro Liò², Marco Aldinucci³

¹Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy, Genoa, Italy. dagostino@ieiit.cnr.it.

BMC Bioinformatics

|April 27, 2021

Summary

This study introduces NeoHiC, a graph database application for analyzing chromosome conformation capture (Hi-C) data. NeoHiC enables efficient visualization and comparison of 3D genome folding patterns across experiments.

Keywords:

Chromatin capture Graph databases Graph visualisation Hi-C

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

High-throughput Chromosome Conformation Capture (Hi-C) generates genome-wide DNA interaction data, typically in matrix format.
Matrix representations struggle to capture the complex 3D topology of DNA in eukaryotic cells.
Graph-based representations offer advantages for describing intricate DNA spatial organization.

Purpose of the Study:

To explore the utility of graph databases for storing and analyzing large-scale Hi-C data.
To address the limitations of existing visualization tools in managing the vast number of contacts in graph representations of Hi-C data.
To leverage graph databases for efficient spatial pattern analysis and comparison of Hi-C experiments.

Main Methods:

Utilizing a graph database to store and analyze Hi-C experimental data.
Implementing strategies to manage a large number of edges (contacts) connecting nodes (genes).
Employing graph database capabilities for both analysis and visualization of spatial patterns in Hi-C data.

Main Results:

Developed NeoHiC, an open-source web application for visualizing and analyzing Hi-C networks.
NeoHiC is based on the Neo4j graph database.
The application facilitates progressive visualization and analysis of Hi-C data.

Conclusions:

NeoHiC provides a user-friendly platform for analyzing Hi-C data.
Future accumulation of experiments will enhance its utility for comparing gene neighbors and functional domains.
The tool aids in identifying changes in genomic compartments and co-organization across different conditions.