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Updated: May 13, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

DeCGR: an interactive toolkit for deciphering complex genomic rearrangements from Hi-C data.

Junping Li¹, Minghui Sun¹, Yusen Ye¹

¹Department of Computer Science, School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China.

|November 29, 2024

Summary

DeCGR is a new software tool that helps biologists analyze complex genomic rearrangements (CGRs) using Hi-C data. It simplifies the identification, assembly, and validation of CGRs, providing clear visualizations for better understanding of chromatin structure.

Keywords:

3D genome Complex genomic rearrangements Hi-C

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Last Updated: May 13, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

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Published on: May 6, 2010

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

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

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

Published on: October 14, 2022

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Complex genomic rearrangements (CGRs) alter chromatin architecture, creating anomalous interaction blocks in Hi-C data.
These blocks aid in identifying and assembling CGRs but lack dedicated interactive graphical software.
Accurate analysis of CGRs is crucial for understanding their impact on genome structure and function.

Purpose of the Study:

To develop user-friendly software for deciphering complex genomic rearrangements (CGRs) from Hi-C data.
To provide tools for efficient identification, assembly, validation, and visualization of CGRs.
To facilitate the association between genomic rearrangements and chromatin structure.

Main Methods:

Developed DeCGR, a Python toolbox with four modules: Breakpoint Filtering, Fragment Assembly, Validation CGRs, and Reconstruct Hi-C Map.
Implemented automated CGR assembly and visualization of the assembly process.
Integrated a validation module that simulates Hi-C maps with CGRs for accuracy assessment and comparison with original maps.

Main Results:

DeCGR successfully identifies, filters, and assembles CGRs from Hi-C data.
The software provides intuitive visualizations, linking anomalous interaction blocks to CGR events.
Validation and reconstruction modules enable assessment of CGR accuracy and impact on chromatin structure.

Conclusions:

DeCGR offers a comprehensive solution for biologists to explore CGRs in Hi-C data.
The tool ensures CGR completeness and correctness through its validation module.
DeCGR provides a user-friendly graphical interface for easy CGR analysis and Hi-C map reconstruction.