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Updated: May 24, 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

DSLE2 random-effects meta-analysis model for high-throughput methylation data.

Nan Wang^1,2, Yang Zhou¹, Fengping Zhu³

¹School of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.

|March 5, 2025

Summary

This summary is machine-generated.

A new meta-analysis model, DSLE2, improves the analysis of high-throughput sequencing data. This model enhances statistical power and identifies key methylation sites related to lung cancer and Parkinson's disease.

Keywords:

Between-study variance Meta-analysis Methylation sequencing data Random-effects model

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Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Area of Science:

Bioinformatics
Genomics
Statistical Genetics

Background:

High-throughput sequencing generates massive datasets stored in public databases (e.g., EBI, GEO).
Secondary data mining and meta-analysis of this data offer valuable insights.
Meta-analysis increases sample size and statistical power for reliable conclusions.

Purpose of the Study:

To propose a novel between-study variance estimator (Em).
To develop a new random-effects meta-analysis model, DSLE2, using the Em estimator.
To evaluate the performance of the DSLE2 model against existing meta-analysis methods.

Main Methods:

Development of a new non-negative between-study variance estimator (Em).
Construction of the DSLE2 (two-step estimation) random-effects meta-analysis model.
Application and evaluation of DSLE2 using lung cancer and Parkinson's methylation data.

Main Results:

The proposed Em estimator satisfies general conditions for between-study variance estimators.
The DSLE2 model demonstrated superior accuracy and evaluation metrics compared to six other models.
DSLE2 identified significantly differentially methylated sites and related genes in lung cancer and Parkinson's disease.

Conclusions:

The DSLE2 random-effects meta-analysis model, based on the Em estimator, is effective.
DSLE2 shows strong performance, particularly for analyzing methylation data.
The model successfully identified disease-relevant methylation sites in complex diseases.