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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 22, 2026

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Published on: August 10, 2017

Functional assessment of time course microarray data.

María José Nueda¹, Patricia Sebastián, Sonia Tarazona

¹Department of Statistics and Operation Research, University of Alicante, Ctra, San Vicente del Raspeig, S/N 03690 Alicante, Spain. mj.nueda@ua.es

BMC Bioinformatics

|June 19, 2009

Summary

This summary is machine-generated.

We developed three new methods to analyze gene expression dynamics over time, integrating functional information for deeper biological insights. These approaches reveal meaningful relationships between genes, functions, and co-expression patterns in biological systems.

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Last Updated: Jun 22, 2026

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

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Published on: August 10, 2017

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A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Time-course microarray experiments track gene expression changes over time under various conditions.
Existing methods often focus on gene clustering or differential expression, neglecting integrated functional analysis.
Assessing functional aspects of time-course transcriptomics requires methods that leverage the dynamic activation of gene sets.

Purpose of the Study:

To introduce novel methodologies for the functional assessment of time-course microarray data.
To provide tools that integrate gene expression dynamics with functional category information.
To offer complementary approaches for understanding molecular and functional events in biological systems.

Main Methods:

maSigFun: A regression-based method extending maSigPro to model time-dependent expression patterns and identify significant functional categories.
PCA-maSigFun: Utilizes Principal Component Analysis (PCA) to extract expression patterns within functional classes, then models their time-dependent dynamics.
ASCA-functional: Employs the ASCA model to rank genes by correlation with time expression patterns and performs Gene Set Analysis (GSA) for functional enrichment.

Main Results:

Simulated and experimental data demonstrated the ability of the novel methods to capture biologically relevant relationships between genes, functions, and co-expression.
The developed approaches provide distinct yet complementary insights into dynamic molecular and functional events.
Results highlight the utility of these methods for a more comprehensive understanding of time-course transcriptomic data.

Conclusions:

The three presented methodologies offer valuable tools for functional analysis of time-course gene expression data.
These methods provide complementary perspectives, enhancing the interpretation of complex biological dynamics.
The integration of functional information with time-course analysis advances our understanding of biological systems.