Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

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.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

IL-17 Cytokines Induce IκBζ in Dermal Fibroblasts to Promote Pro-Inflammatory Gene Expression in Psoriasis.

International journal of molecular sciences·2026

Same author

Multi-omics integration uncovers metabolic control of H3K27ac in bovine inner cell masses†.

Biology of reproduction·2025

Same author

In vitro processes alter the embryonic disc epigenome and transcriptome in the pre-implantation elongated bovine embryo†.

Biology of reproduction·2025

Same author

A gentle introduction to pangenomics.

Briefings in bioinformatics·2024

Same author

GEDI: An R Package for Integration of Transcriptomic Data from Multiple Platforms for Bioinformatics Applications.

Current protocols·2024

Same author

CLAW: An automated Snakemake workflow for the assembly of chloroplast genomes from long-read data.

PLoS computational biology·2024

Search research articles

Related Experiment Video

Updated: May 17, 2026

Bacterial Gene Expression Analysis Using Microarrays

Bacterial Gene Expression Analysis Using Microarrays

Published on: May 28, 2007

Building gene co-expression networks using transcriptomics data for systems biology investigations: Comparison of

Haja N Kadarmideen¹, Nathan S Watson-Haigh

¹Department of Veterinary Clinical and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, 1870 Frederiksberg C, Copenhagen, Denmark ; Authors contributed equally.

|November 13, 2012

Summary

This study compares Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) for building gene co-expression networks (GCN). PCIT removes more connections than WGCNA, impacting hub gene analysis.

More Related Videos

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Related Experiment Videos

Last Updated: May 17, 2026

Bacterial Gene Expression Analysis Using Microarrays

Bacterial Gene Expression Analysis Using Microarrays

Published on: May 28, 2007

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

Systems Biology
Transcriptomics
Bioinformatics

Background:

Gene co-expression networks (GCN) are crucial for understanding gene function and biological pathways.
High-throughput gene expression data enables the construction of complex GCNs.
Comparing different GCN construction methods is essential for accurate biological interpretation.

Purpose of the Study:

To compare the Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) methods for building and analyzing GCNs.
To evaluate the impact of different GCN construction approaches on network properties, particularly hub gene identification.
To assess the biological relevance of differentially ranked genes identified by each method.

Main Methods:

Utilized ovine microarray transcriptomics datasets under Metyrapone treatment.
Performed rigorous microarray quality control and data filtering.
Applied WGCNA and PCIT methods to construct GCNs and compared node connectivity.
Identified highly differentially ranked (HDR) nodes between the two methods.
Conducted gene enrichment analyses on HDR nodes to determine biological relevance.

Main Results:

A total of 1,017 HDR nodes were identified across four networks.
The PCIT algorithm removed significantly more edges from highly connected nodes (hub genes) compared to WGCNA.
This edge removal by PCIT can alter downstream analyses and biological interpretations of hub genes.
Gene enrichment analysis revealed biological relevance for HDR nodes.

Conclusions:

The choice of GCN construction method (WGCNA vs. PCIT) significantly impacts network topology and hub gene identification.
PCIT's aggressive edge removal may lead to loss of potentially important biological information.
Researchers should consider the implications of method choice for biological interpretation, especially for large-scale GCNs.
Computational resources, including large computer clusters with ample shared memory, are recommended for large GCN construction.