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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 21, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Gene set analysis methods applied to chicken microarray expression data.

Axel Skarman¹, Li Jiang, Hornshøj Henrik

¹Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus University, DK-8830 Tjele, Denmark. Axel.Skarman@agrsci.dk

BMC Proceedings

|July 21, 2009

Summary

Different gene set analysis methods yield varied biological interpretations from chicken expression data. Predicting gene functions requires careful validation of results from these analyses.

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Gene set analysis enhances biological interpretation of gene expression patterns.
This study examines various gene set analysis methods using chicken DNA microarray data.

Purpose of the Study:

To compare different gene set analysis approaches for biological interpretation.
To evaluate methods for predicting gene annotations from expression data.

Main Methods:

Application of diverse gene set analysis techniques.
Utilizing chicken DNA microarray expression data.
Employing a predictive method for gene annotation.

Main Results:

Gene Ontology term rankings differed across various gene set analyses.
The predictive method for gene annotation was applied to the dataset.

Conclusions:

Biological interpretation is method-dependent in gene set analysis.
Predictive methods for gene function annotation show promise but require rigorous validation.