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What is Gene Expression?

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
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What is Gene Expression?

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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
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Related Experiment Video

Updated: Jul 2, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Geometric interpretation of gene coexpression network analysis.

Steve Horvath¹, Jun Dong

¹Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America.

Plos Computational Biology

|August 16, 2008

Summary

Gene coexpression network analysis merges network theory and microarray data. This study clarifies network concepts and their relationships, offering a dictionary for computational biologists and systems biology applications.

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Area of Science:

Computational biology
Bioinformatics
Systems biology

Background:

Gene coexpression network analysis integrates network theory and microarray data analysis.
Computational biologists often have limited network theory vocabulary.
Existing methods can be enhanced by a deeper understanding of network concepts.

Purpose of the Study:

To review and propose useful network concepts for gene coexpression networks.
To clarify the relationship between network theory and microarray data analysis.
To provide a translation dictionary between the two fields.

Main Methods:

Utilizing the angular interpretation of correlations for geometric interpretation of network concepts.
Employing singular value decomposition of module expression data to characterize gene coexpression networks.
Analyzing relationships among modules and module genes using high and low-level network views.

Main Results:

Derived unexpected relationships among network theoretic concepts using an angular correlation interpretation.
Characterized approximately factorizable gene coexpression networks.
Demonstrated that intramodular connectivity can serve as a fuzzy measure of module membership.

Conclusions:

Unifying gene coexpression network methods with traditional data mining enhances systems biology.
The proposed network concepts and translations facilitate broader application in biological research.
Results are illustrated with human, mouse, and yeast gene expression data.