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Related Experiment Video

Updated: Sep 15, 2025

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

Genome structure mapping with high-resolution 3D genomics and deep learning.

Clarice K Y Hong^1,2,3,4, Fan Feng⁵, Varshini Ramanathan^1,2,3,4

¹Department of Biological Engineering, Massachusetts Institute of Technology; Cambridge, MA 02139, USA.

Biorxiv : the Preprint Server for Biology

|July 14, 2025

Summary

This summary is machine-generated.

Scientists developed Cleopatra, a deep learning model, to map 3D genome interactions. This tool reveals cell-type-specific gene regulation by identifying thousands of DNA loops, enhancing our understanding of gene expression.

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Last Updated: Sep 15, 2025

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

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Published on: May 6, 2010

Mapping Mammalian 3D Genome Interactions with Micro-C-XL

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Published on: November 3, 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:

Gene expression is regulated by distal enhancers via cell-type-specific 3D looping interactions.
Comprehensive mapping of these interactions across diverse cell types is experimentally challenging.

Purpose of the Study:

To develop a computational framework for generating ultra-high-resolution, genome-wide 3D contact maps.
To investigate cell-type-specific 3D genome organization and its relationship with gene expression.

Main Methods:

Generation of ultra-deep Region Capture Micro-C (RCMC) and Micro-C data.
Development and application of Cleopatra, an attention-based deep learning model, for predicting 3D genome maps.
Analysis of cell-type-specific microcompartments and DNA looping interactions.

Main Results:

Cleopatra accurately predicts 3D genome maps at sub-kilobase resolution across four human cell types.
Over 900,000 DNA loops were identified, with approximately half being cell-type-specific.
Gene expression was found to increase monotonically with the number of promoter-associated loops.

Conclusions:

The study establishes a framework for ultra-high-resolution 3D genome mapping using deep learning.
The generated maps provide a resource for understanding cell-type-specific gene regulation and enhancer-promoter interactions.
The findings highlight the role of DNA looping in modulating gene expression levels.