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The Eukaryotic Promoter Region

The Eukaryotic Promoter Region

The eukaryotic promoter region is a segment of DNA located upstream of a gene. It contains an RNA polymerase binding site, a transcription start site, and several cis-regulatory sequences. The proximal promoter region is located in the vicinity of the gene and has cis-regulatory sequences and the core promoter. The core promoter is the binding site for RNA polymerase and is usually located between -35 and +35 nucleotides from the transcription start site. The distal promoter regions are...

The Eukaryotic Promoter Region

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RNA Polymerase II Accessory Proteins

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Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...

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Updated: Nov 28, 2025

A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers

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Published on: July 18, 2025

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction.

Xiaoping Min¹, Fengqing Lu¹, Chunyan Li²

¹School of Informatics, Xiamen University, Xiamen 361005, China.

Current Pharmaceutical Design

|November 25, 2020

Summary

This summary is machine-generated.

Identifying enhancer-promoter interactions (EPIs) is crucial for understanding gene regulation and disease. This review surveys sequence-based deep learning methods for predicting EPIs, discussing their frameworks, data, and challenges.

Keywords:

Enhancer-promoter interactions attention mechanism convolutional neural network deep learning interpretable model prediction recurrent neural network sequence features word embedding

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Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

Published on: June 28, 2018

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Enhancer-promoter interactions (EPIs) are critical for precise gene expression control.
Understanding EPIs aids in deciphering gene regulation and disease mechanisms.
Experimental identification of EPIs is resource-intensive.

Purpose of the Study:

To provide a comprehensive review of sequence-based deep learning methods for EPI prediction.
To summarize existing frameworks, datasets, and evaluation strategies for computational EPI identification.
To highlight challenges and future opportunities in the field.

Main Methods:

Literature review focusing on sequence-based deep learning approaches for EPI prediction.
Analysis of existing computational frameworks and their technical details.
Discussion of datasets, data pre-processing, and evaluation metrics used in EPI prediction studies.

Main Results:

Deep learning methods offer a viable computational alternative to experimental EPI identification.
Sequence-based deep learning models show promise in accurately predicting EPIs.
The review categorizes and details various sequence-based deep learning frameworks.

Conclusions:

Sequence-based deep learning methods are powerful tools for identifying enhancer-promoter interactions.
Further research is needed to address current challenges and explore future opportunities in computational EPI prediction.
This review serves as a valuable reference for researchers in the field of gene regulation.