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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: Dec 30, 2025

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

CoMeBack: DNA methylation array data analysis for co-methylated regions.

Evan Gatev^1,2,3,4, Nicole Gladish^3,4,5, Sara Mostafavi^3,4,5,6

¹Graduate Program in Bioinformatics, University of British Columbia, Vancouver, BC V5T 4S6, Canada.

Bioinformatics (Oxford, England)

|January 28, 2020

Summary

This summary is machine-generated.

We developed CoMeBack, a new method to analyze DNA methylation (DNAm) array data by identifying co-methylated regions (CMRs). This approach improves statistical power and reduces false positives in epigenome-wide association studies.

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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

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Published on: September 18, 2020

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DNA Methylation: Bisulphite Modification and Analysis

DNA Methylation: Bisulphite Modification and Analysis

Published on: October 21, 2011

Area of Science:

Genomics
Epigenetics
Bioinformatics

Background:

High-dimensional DNA methylation (DNAm) array data present statistical challenges due to the large number of probes and correlations between adjacent sites.
Multiple-test corrections reduce statistical power, potentially limiting reproducibility in DNAm association studies.

Purpose of the Study:

To develop a novel method that accounts for correlations between proximal CpG probes in DNAm array data.
To improve the specificity, discovery, and interpretation of statistical associations in DNAm data.

Main Methods:

Developed Co-Methylation with genomic CpG Background (CoMeBack) to estimate DNA co-methylation and identify co-methylated regions (CMRs).
CoMeBack utilizes all genomic CpG sites, not just array probes, and does not require phenotypic variables.
Validated CMRs in whole blood using Illumina 450K array data from over 5000 individuals.

Main Results:

CoMeBack successfully identified CMRs, which were enriched for enhancer chromatin states and transcription factor binding sites relevant to blood physiology.
CMRs demonstrated improved discovery and reduced false positives in epigenome-wide association studies, exemplified by associations with chronological age.

Conclusions:

CoMeBack offers a robust approach to analyze DNAm array data by leveraging genomic context and probe correlations.
This method enhances the power and reliability of epigenome-wide association studies, leading to more accurate biological insights.