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Epigenetic Regulation

Epigenetic Regulation

Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...

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Related Experiment Video

Updated: Jun 27, 2025

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

iDNA-OpenPrompt: OpenPrompt learning model for identifying DNA methylation.

Xia Yu^1,2, Jia Ren¹, Haixia Long²

¹School of Information and Communication Engineering, Hainan University, Haikou, Hainan, China.

Frontiers in Genetics

|May 1, 2024

Summary

This summary is machine-generated.

The iDNA-OpenPrompt model accurately identifies DNA methylation sites using a novel OpenPrompt framework. This approach improves upon existing deep learning methods for epigenetic analysis across various species and modifications.

Keywords:

BERT tokenizer DNA methylation OpenPrompt learning prompt template prompt verbalizer

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Last Updated: Jun 27, 2025

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Area of Science:

Epigenetics
Genomics
Bioinformatics

Background:

DNA methylation is a key epigenetic mechanism regulating gene expression.
Identifying DNA methylation sites is challenging due to complex patterns and limitations of traditional methods.
Existing deep learning models lack sufficient accuracy for DNA methylation site identification.

Purpose of the Study:

To introduce the iDNA-OpenPrompt model for accurate DNA methylation site identification.
To leverage the OpenPrompt learning framework for enhanced epigenetic analysis.
To overcome the limitations of current deep learning approaches in this field.

Main Methods:

Developed the iDNA-OpenPrompt model using a novel OpenPrompt learning framework.
Integrated a prompt template, prompt verbalizer, and Pre-trained Language Model (PLM).
Utilized a DNA vocabulary library, BERT tokenizer, and specific label words for precise site identification.

Main Results:

Evaluated the model's predictive, reliability, and consistency capabilities.
Tested on 17 benchmark datasets across various species.
Analyzed three DNA methylation modifications: 4mC, 5hmC, and 6mA.

Conclusions:

The iDNA-OpenPrompt model demonstrates superior performance and robustness.
The model consistently surpasses existing outstanding approaches in accuracy.
This work offers a significant advancement in high-throughput DNA methylation analysis.