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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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Software note: using probe secondary structure information to enhance Affymetrix GeneChip background estimates.

Raad Z Gharaibeh¹, Anthony A Fodor, Cynthia J Gibas

¹Bioinformatics Research Center, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA.

Computational Biology and Chemistry

|March 28, 2007

Summary

Probe minimum folding energy and structure can improve background noise correction models for gene expression microarrays. This enhancement accounts for up to 3% of variation in Affymetrix microarray data.

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Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

High-density short oligonucleotide microarrays are essential for global gene expression analysis.
Background noise significantly impacts the accuracy of microarray data.
Existing statistical methods aim to correct for this background noise.

Purpose of the Study:

To enhance existing background noise correction models for microarrays.
To investigate the role of probe secondary structure in microarray data variation.

Main Methods:

Utilized probe minimum folding energy and structure as parameters.
Integrated these parameters into a previously established background noise correction model.
Analyzed data from Affymetrix microarrays.

Main Results:

Demonstrated that probe minimum folding energy and structure can improve background noise correction.
Estimated that probe secondary structure contributes up to 3% to the total variation observed on Affymetrix microarrays.

Conclusions:

Probe secondary structure is a significant factor influencing microarray data.
Incorporating probe structural properties offers a more accurate approach to background noise correction in gene expression studies.