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Heterochromatin

Heterochromatin

The extent of chromatin compaction can be studied by staining chromatin using specific DNA binding dyes. Under the microscope, the dense-compacted regions that take up more dye are called heterochromatin. Heterochromatin is further classified into two forms – constitutive heterochromatin and facultative heterochromatin.
Constitutive heterochromatin: It is a highly compact region of chromatin that is mostly concentrated in the centromere and telomere. Unlike euchromatin, the amino acid at...

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Updated: Sep 17, 2025

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

DeepExDC interprets genomic compartmentalization changes in single-cell Hi-C data.

Hongqiang Lyu¹, Pei Cao², Wenyao Long¹

¹School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Beilin District, Xi'an, Shaanxi 710049, China.

Briefings in Bioinformatics

|June 29, 2025

Summary

This summary is machine-generated.

DeepExDC accurately analyzes A/B compartments in single-cell Hi-C (scHi-C) data. This interpretable deep learning method reveals genomic compartmentalization changes, improving understanding of cell function and phenotype.

Keywords:

A/B compartments differential analysis interpretable network single-cell Hi-C

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Last Updated: Sep 17, 2025

Capturing Chromosome Conformation Across Length Scales

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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

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

Area of Science:

Genomics
Computational Biology
Epigenetics

Background:

Single-cell Hi-C (scHi-C) technology allows studying higher-order chromatin structures in individual cells.
Understanding genomic compartmentalization changes is crucial for linking genome organization, function, and cellular phenotypes.
Computational methods for differential A/B compartment analysis in scHi-C data are limited.

Purpose of the Study:

To develop an interpretable deep learning method for genome-wide differential analysis of A/B compartments in scHi-C data.
To accurately detect and interpret compartmentalization changes across different cellular conditions.
To provide a robust tool for analyzing single-cell chromatin organization.

Main Methods:

Developed DeepExDC, an interpretable 1D convolutional neural network.
Applied DeepExDC to analyze single-cell Hi-C contact matrices without distribution assumptions.
Validated the method on simulated and experimental scHi-C data, and also tested on scRNA-seq and scATAC-seq data.

Main Results:

DeepExDC demonstrates high accuracy in detecting various compartmentalization changes in scHi-C data.
Interpretation values from DeepExDC reflect compartment changes across cell types and agree with bulk Hi-C methods.
The method effectively characterizes single-cell heterogeneity and shows biological relevance.
DeepExDC shows considerable power when applied to scRNA-seq and scATAC-seq data.

Conclusions:

DeepExDC offers a powerful and interpretable approach for differential A/B compartment analysis in scHi-C data.
The method advances the understanding of single-cell chromatin organization and its relation to cellular phenotypes.
DeepExDC's versatility extends to other single-cell omics data types, highlighting its broad applicability.