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Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
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Hi-Compass: a depth-aware deep learning framework for predicting cell-type-specific 3D genome organization from

Yuan-Chen Sun1, Wen-Jie Jiang2, Kang-Wen Cai1

  • 1Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China.

Nature Communications
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

Hi-Compass is a new deep learning tool that predicts 3D genome organization from chromatin accessibility. It accurately maps cell-type-specific interactions, aiding disease gene discovery.

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Area of Science:

  • Genomics
  • Computational Biology
  • Epigenetics

Background:

  • Three-dimensional (3D) genome organization is crucial for cell-type-specific gene expression.
  • Experimental limitations hinder comprehensive analysis of genome organization across diverse cell types.

Purpose of the Study:

  • To develop a computational framework, Hi-Compass, for predicting cell-type-specific 3D genome organization.
  • To enable robust predictions across varying sequencing depths, from single-cell to bulk data.

Main Methods:

  • Developed Hi-Compass, a depth-aware deep learning framework.
  • Utilized chromatin accessibility data as cell-type-specific input.
  • Benchmarked against experimental Hi-C data and existing methods.

Main Results:

  • Hi-Compass demonstrates superior concordance with experimental Hi-C data, especially for high-confidence chromatin loops.
  • Successfully resolved cell-type-specific chromatin interactions in peripheral blood and embryonic heart datasets.
  • Linked disease-associated variants to potential target genes and enabled spatially resolved predictions.

Conclusions:

  • Hi-Compass provides a robust method for predicting 3D genome organization using accessible chromatin data.
  • The framework enhances the study of genome regulation across different scales and species.
  • Facilitates the identification of gene regulatory elements and disease mechanisms.