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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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Related Experiment Video

Updated: Apr 12, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data.

Jianguo Xia¹, Erin E Gill², Robert E W Hancock³

¹1] Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada. [2] Institute of Parasitology, and Department of Animal Science, McGill University, Ste. Ann de Bellevue, Québec, Canada. [3] Department of Microbiology and Immunology, McGill University, Montreal, Québec, Canada.

Nature Protocols

|May 8, 2015

Summary

This summary is machine-generated.

NetworkAnalyst is a web tool simplifying gene expression meta-analysis for biologists. It integrates statistics and visualization for robust molecular signature discovery and hypothesis generation.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Meta-analysis of gene expression data is crucial for identifying molecular signatures and understanding biological processes.
Complex analyses require advanced statistics and visualization for effective data interpretation and hypothesis generation.

Purpose of the Study:

To introduce NetworkAnalyst, a web-based tool for performing complex meta-analyses of gene expression data.
To enable bench researchers to conduct analyses via an intuitive interface, facilitating hypothesis generation.

Main Methods:

NetworkAnalyst integrates statistical procedures with data visualization techniques.
It supports network analysis, meta-analysis with metadata, and multi-dataset meta-analysis.
The tool offers visual analytics within protein-protein interaction networks, heatmaps, and chord diagrams.

Main Results:

NetworkAnalyst provides an accessible platform for biologists to perform gene expression meta-analyses.
The tool facilitates navigation of large datasets to identify key features, patterns, functions, and connections.
Analysis methods are supported by statistical and functional evidence.

Conclusions:

NetworkAnalyst empowers researchers to perform sophisticated gene expression meta-analyses with ease.
Its unique visual analytics and user-friendly interface accelerate biological discovery and hypothesis generation.