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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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Updated: Jun 22, 2025

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model.

Farhad Shokoohi¹, David A Stephens², Celia M T Greenwood^3,4,5

¹Department of Mathematical Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA.

|June 27, 2024

Summary

We developed DMCFB, a new Bayesian method for identifying differentially methylated cytosines (dmcs). DMCFB improves accuracy and consistency in detecting DNA methylation changes, crucial for understanding gene regulation and disease risk.

Keywords:

Gibbs sampler acute promyelocytic leukemia bisulfite sequencing missing values read depth

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Last Updated: Jun 22, 2025

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Area of Science:

Genomics
Epigenetics
Bioinformatics

Background:

DNA methylation is vital for gene regulation, disease susceptibility, and treatment outcomes.
Next-generation sequencing provides single-nucleotide resolution of methylation patterns.
Analyzing complex sequencing data to identify differential methylation presents significant computational challenges.

Purpose of the Study:

To develop an efficient and accurate method for identifying differentially methylated cytosines (dmcs).
To address the challenges posed by complex, high-dimensional sequencing data in methylation analysis.

Main Methods:

Development of DMCFB, a novel method employing Bayesian functional regression for dmc identification.
Performance evaluation using simulations to compare DMCFB against existing methods.
Application of DMCFB to analyze DNA methylation data from acute promyelocytic leukemia patients and controls.

Main Results:

DMCFB demonstrated superior performance compared to current methods in simulations, offering better data smoothing and imputation.
Analysis of acute promyelocytic leukemia data revealed numerous novel dmcs.
Enhanced consistency of differential methylation was observed within CpG islands and their shores.
Differential methylation was detected at additional binding sites of the key fused gene implicated in the cancer.

Conclusions:

DMCFB is an efficient and effective tool for identifying differentially methylated cytosines.
The method enhances the discovery and consistency of differential methylation patterns.
Findings provide new insights into the epigenetic landscape of acute promyelocytic leukemia, particularly concerning key gene targets.