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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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Related Experiment Video

Updated: Jun 10, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of

Leming Shi¹, Gregory Campbell, Wendell D Jones

¹National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA.

Nature Biotechnology

|August 3, 2010

Summary

This summary is machine-generated.

The MicroArray Quality Control II (MAQC-II) project assessed the reliability of gene expression data for predicting health outcomes. Results show model performance varies by endpoint and team expertise, offering guidance for gene expression analysis methods.

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Last Updated: Jun 10, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR (qPCR) Arrays

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR (qPCR) Arrays

Published on: December 3, 2010

Performing Custom MicroRNA Microarray Experiments

Performing Custom MicroRNA Microarray Experiments

Published on: October 28, 2011

Area of Science:

Genomics
Toxicology
Biomarker Discovery

Background:

Gene expression data from microarrays are increasingly used for predicting preclinical and clinical outcomes.
The reliability and reproducibility of these predictive models remain largely unestablished.

Purpose of the Study:

To evaluate the reliability of predictive models generated from microarray gene expression data.
To assess the performance of models across diverse endpoints and analytical approaches.

Main Methods:

Thirty-six independent teams analyzed six microarray datasets to build predictive models for 13 different endpoints.
Models were developed for rodent toxicity and human diseases including breast cancer, multiple myeloma, and neuroblastoma.
Models were tested on independent datasets to simulate real-world application.

Main Results:

Model performance was highly dependent on the specific biological endpoint and the proficiency of the analyzing team.
Diverse analytical methods yielded models of comparable performance for many endpoints.
Over 30,000 models were generated, highlighting the complexity of gene expression data analysis.

Conclusions:

The study provides crucial insights into the variability and reliability of gene expression-based predictive models.
Findings offer recommendations for regulatory agencies and researchers evaluating global gene expression analysis methods.
Team proficiency and endpoint characteristics significantly influence model outcomes.