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DNA Microarrays

DNA Microarrays

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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Updated: Jun 2, 2026

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

Preprocessing differential methylation hybridization microarray data.

Shuying Sun¹, Yi-Wen Huang, Pearlly S Yan

¹Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, 44106, USA. ssun5211@yahoo.com.

|May 18, 2011

Summary

This summary is machine-generated.

Preprocessing DNA methylation microarray data is crucial for understanding tumor suppressor gene silencing. New LOESS normalization methods using internal control probes improve data stability and accuracy compared to global normalization.

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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

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Area of Science:

Genomics
Cancer Biology
Bioinformatics

Background:

DNA methylation is key in tumor suppressor gene silencing.
Differential Methylation Hybridization (DMH) microarrays generate genome-wide methylation data.
Preprocessing methods for DMH data require optimization.

Purpose of the Study:

To evaluate background correction and normalization methods for DMH microarray data.
To identify optimal preprocessing strategies for accurate methylation analysis.
To develop novel normalization methods for DMH data.

Main Methods:

Compared 20 preprocessing methods (5 background correction x 4 normalization).
Utilized internal control probes with theoretically zero log ratios (M).
Developed and compared two novel LOESS normalization methods against global LOESS and no normalization.

Main Results:

Background correction methods showed similar performance.
Normalization methods varied significantly in effectiveness.
All LOESS normalization methods outperformed no normalization.

Conclusions:

Within-array normalization is essential for DMH data.
Novel LOESS methods using DMH internal controls yield superior stability and results.
Recommended LOESS normalization for DMH microarray data analysis.