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

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MethAgingDB: a comprehensive DNA methylation database for aging biology.

Siyu Li¹, Songming Tang¹, Haocheng Ma²

¹School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China.

Scientific Data

|July 14, 2025

Summary

Researchers created MethAgingDB, a new database for DNA methylation data. This resource aids in understanding biological age and developing aging biomarkers by organizing epigenetic clock information.

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Targeted DNA Methylation Analysis by Next-generation Sequencing

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Published on: February 24, 2015

Area of Science:

Aging Biology
Epigenetics
Bioinformatics

Background:

Quantifying biological age is vital for aging research and interventions.
Epigenetic clocks using DNA methylation are key tools, but data accessibility is limited.
Existing resources lack comprehensive, uniformly formatted DNA methylation datasets and aging-specific differentially methylated sites/regions.

Purpose of the Study:

To develop MethAgingDB, a centralized, publicly accessible database for aging-related DNA methylation data.
To address challenges in data accessibility, standardization, and the identification of aging-specific epigenetic markers.
To facilitate research on epigenetic clocks and the development of aging biomarkers.

Main Methods:

Compiled 93 DNA methylation datasets, including 11,474 human and 1,361 mouse tissue profiles.
Preprocessed data into a consistent matrix format.
Integrated tissue-specific differentially methylated sites (DMSs) and regions (DMRs), gene-centric aging insights, and epigenetic clocks.

Main Results:

MethAgingDB provides a comprehensive resource with data from 13 human and 9 mouse tissues.
The database offers standardized DNA methylation data, DMSs, DMRs, and a collection of epigenetic clocks.
Facilitates easier access to crucial data for aging research.

Conclusions:

MethAgingDB significantly streamlines aging-related epigenetic research.
The database supports the development of robust, biologically informed aging biomarkers.
Enhances the investigation of epigenetic mechanisms underlying the aging process.