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EpiExpr: Predicting gene expression using epigenetic data and chromatin interactions.

Sourya Bhattacharyya¹, Ferhat Ay^1,2

¹La Jolla Institute for Immunology, La Jolla, CA, USA.

Biorxiv : the Preprint Server for Biology

|March 23, 2026

Summary

This summary is machine-generated.

EpiExpr, a deep learning framework, predicts gene expression using epigenomic data. It effectively models gene regulation by integrating 1D epigenetic tracks and 3D chromatin interactions for enhanced genomic analysis.

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Area of Science:

Genomics
Computational Biology
Epigenetics

Background:

Predicting gene expression from epigenomic data is crucial but challenging.
Understanding gene regulation requires integrating local and long-range genomic interactions.

Purpose of the Study:

To develop a deep learning framework, EpiExpr, for predicting gene expression from epigenomic landscapes.
To integrate 1D epigenetic tracks and 3D chromatin interactions for comprehensive regulatory analysis.

Main Methods:

EpiExpr utilizes residual convolutional networks and graph neural networks (including attention and transformer models).
The framework predicts gene expression from 1D epigenetic data (EpiExpr-1D) and integrates 3D chromatin interactions (EpiExpr-3D).

Main Results:

EpiExpr-1D and EpiExpr-3D significantly improved gene expression prediction accuracy in GM12878 and K562 cells compared to existing methods.
EpiExpr-3D accurately prioritized regulatory elements, validated by CRISPRi-FlowFISH experiments.
EpiExpr achieved performance comparable to sequence-based models without needing sequence embeddings, offering computational efficiency.

Conclusions:

EpiExpr provides a scalable, multi-resolution deep learning framework for dissecting gene regulation.
The framework effectively models both local and distal regulatory influences from epigenomic and 3D genome organization data.
EpiExpr offers a computationally efficient alternative for gene expression prediction across diverse cell types and experimental conditions.