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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: May 3, 2026

Processing the Loblolly Pine PtGen2 cDNA Microarray

Processing the Loblolly Pine PtGen2 cDNA Microarray

Published on: March 20, 2009

Methods for processing microarray data.

Cold Spring Harbor Protocols

|February 5, 2014

Summary

This summary is machine-generated.

This study outlines essential quality control measures for microarray experiments, emphasizing proper experimental design and data analysis. Following these steps ensures reliable results for gene expression and function studies.

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Last Updated: May 3, 2026

Processing the Loblolly Pine PtGen2 cDNA Microarray

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Published on: March 20, 2009

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

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High-Density DNA and RNA microarrays - Photolithographic Synthesis, Hybridization and Preparation of Large Nucleic Acid Libraries

High-Density DNA and RNA microarrays - Photolithographic Synthesis, Hybridization and Preparation of Large Nucleic Acid Libraries

Published on: August 12, 2019

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Microarray experiments require stringent quality control from RNA isolation to statistical analysis.
Experimental design is critical for data analysis and interpretation, influencing the utility of results.

Purpose of the Study:

To provide a comprehensive guide for analyzing microarray data.
To ensure the reliability and interpretability of microarray experiment outcomes.

Main Methods:

Careful experimental design, including defining the biological question and determining the number of replicates.
Rigorous sample preparation, quality control checks for integrity and representation, hybridization, and scanning.
Normalization of data to minimize systematic bias, followed by statistical filtering and testing for gene expression changes.
Validation of a subset of data using alternative methods like reverse transcription-polymerase chain reaction (RT-PCR).

Main Results:

Identification of genes with expression above background.
Detection of genes with differential expression across samples.
Characterization of changes in RNA processing or protein binding patterns.

Conclusions:

Adherence to quality control protocols throughout the microarray process is paramount.
Validated findings allow for functional classification of gene sets to generate biological hypotheses.