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Related Concept Videos

Heterochromatin02:38

Heterochromatin

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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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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
PubMed
Summary

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 compartmentsdifferential analysisinterpretable networksingle-cell Hi-C

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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.