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Updated: Jun 30, 2025

3D Multicolor DNA FISH Tool to Study Nuclear Architecture in Human Primary Cells

3D Multicolor DNA FISH Tool to Study Nuclear Architecture in Human Primary Cells

Published on: January 25, 2020

Machine and deep learning methods for predicting 3D genome organization.

Brydon P G Wall¹, My Nguyen², J Chuck Harrell^3,4,5

¹Center for Biological Data Science, Virginia Commonwealth University, Richmond, VA, 23284, USA.

|March 18, 2024

Summary

This summary is machine-generated.

This review explores computational tools for predicting three-dimensional (3D) chromatin interactions, like enhancer-promoter interactions (EPIs), to improve gene expression regulation. It highlights machine learning

Keywords:

Hi-C TADs chromatin deep learning enhancer-promoter interactions loops machine learning software

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Last Updated: Jun 30, 2025

3D Multicolor DNA FISH Tool to Study Nuclear Architecture in Human Primary Cells

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Published on: January 25, 2020

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Published on: November 3, 2023

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
Molecular Biology

Background:

Three-dimensional (3D) chromatin interactions, including enhancer-promoter interactions (EPIs), loops, Topologically Associating Domains (TADs), and A/B compartments, are crucial for regulating gene expression.
Advances in chromatin conformation capture technologies allow genome-wide profiling of 3D structures, even at the single-cell level.
Existing 3D structure catalogs are limited by technological variations, tool inconsistencies, and low data resolution, necessitating improved prediction methods.

Conclusions:

Computational tools, particularly those employing machine learning, offer a promising avenue for overcoming limitations in current 3D chromatin structure catalogs.
Further development is needed to address obstacles in computational prediction, aiming for more complete and reliable 3D interaction data.
Future research should focus on refining prediction algorithms and integrating diverse genomic data for a comprehensive understanding of 3D genome organization and gene regulation.