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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 5, 2026

Determining Genetic Expression Profiles in C. elegans Using Microarray and Real-time PCR

Determining Genetic Expression Profiles in C. elegans Using Microarray and Real-time PCR

Published on: July 30, 2011

Microarray time course experiments: finding profiles.

Itziar Irigoien¹, Sergi Vives, Concepción Arenas

¹Department of Computation Science and Artificial Intelligence, University of the Basque Country, Manuel de Lardizabal Pasealckua 1, 20080 Donostia, Spain. itziar.irigoien@ehu.es

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|January 15, 2011

Summary

This summary is machine-generated.

This study introduces a novel method for analyzing gene expression data from replicated microarray experiments. The approach effectively selects and clusters genes, improving the reliability of biomedical research findings.

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Last Updated: Jun 5, 2026

Determining Genetic Expression Profiles in C. elegans Using Microarray and Real-time PCR

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Published on: July 30, 2011

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

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Published on: December 3, 2010

Area of Science:

Bioinformatics
Genomics
Biotechnology

Background:

Microarray time course studies with experimental replicates are crucial for biomedical research.
Existing methods may not fully leverage the information from replicate experiments for gene selection and clustering.

Purpose of the Study:

To present an alternative approach for selecting and clustering genes using a new measure of gene association in replicated experiments.
To improve the accuracy and reliability of gene expression data analysis in the presence of experimental replicates.

Main Methods:

Normalization and standardization of gene expression profiles.
Identification of scaling parameters to minimize distance between gene replicates.
Filtering of genes with flat profiles and detection of significant differences between replicates.
Hierarchical clustering with a statistic to determine cluster compactness and number of classes.
Assignment of genes with significant replicate differences to existing or new profiles.

Main Results:

Demonstration of the procedure's effectiveness using simulated data.
Application to a representative microarray dataset with replication, yielding interesting results.
Successful separation of genes based on significant differences between replicates.

Conclusions:

The proposed method offers a robust approach to gene selection and clustering in replicated microarray experiments.
This technique enhances the ability to identify meaningful biological patterns from complex gene expression data.
The findings contribute to more reliable interpretation of results in time course microarray studies.