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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Phase II Reactions: Methylation Reactions01:17

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Methylation is a phase II biotransformation process involving the attachment of a methyl group to a substrate. Enzymes known as methyltransferases orchestrate this reaction.
The mechanism of methylation unfolds in two stages. The first stage sees a methyltransferase enzyme facilitating the transfer of a methyl group from S-adenosylmethionine (SAM) to the substrate, forming S-adenosylhomocysteine (SAH). The second stage involves further metabolism of SAH into homocysteine, which can be recycled...
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Updated: Apr 1, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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Estimating DNA methylation levels by joint modeling of multiple methylation profiles from microarray data.

Tao Wang1, Mengjie Chen2, Hongyu Zhao1

  • 1Department of Biostatistics, Yale University, New Haven, Connecticut, 06520, U.S.A.

Biometrics
|October 5, 2015
PubMed
Summary

This study introduces a new statistical model for DNA methylation analysis, improving accuracy by considering probe effects and neighboring genomic correlations. The method enhances the detection of differentially methylated regions, offering more reliable insights from array-based data.

Keywords:
DNA methylation indexGroup-fused lassoModed-based analysisProbe effect

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • High-throughput array-based platforms have transformed DNA methylation studies.
  • Current methods often analyze methylation data probe-by-probe, neglecting crucial probe-specific effects and spatial correlations.

Purpose of the Study:

  • To develop a novel statistical model for analyzing DNA methylation microarray data.
  • To improve the estimation of methylation values by accounting for probe affinity and neighboring site correlations.
  • To create a robust procedure for detecting differentially methylated regions.

Main Methods:

  • A statistical model is proposed to pool probe information across samples for estimating probe affinity.
  • The model borrows strength from neighboring probe sites to refine methylation value estimation.
  • A simulation study was conducted to validate the model's accuracy.

Main Results:

  • The proposed method provides accurate model-based estimates of methylation values.
  • A new procedure for detecting differentially methylated regions was developed using the statistical model.
  • The method demonstrated superior performance compared to a state-of-the-art approach in a data application.

Conclusions:

  • The developed statistical model enhances the analysis of DNA methylation microarray data.
  • Accounting for probe-specific effects and genomic correlations leads to more reliable findings.
  • The new procedure offers an improved approach for identifying differentially methylated regions.