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Related Experiment Video

Updated: Jun 7, 2026

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Published on: April 8, 2017

Generation and analysis of transcriptomics data.

Philip D Glaves¹, Jonathan D Tugwood

¹Molecular Toxicology Group, Safety Assessment Department, AstraZeneca Pharmaceuticals Ltd, Macclesfield, Cheshire, UK.

Methods in Molecular Biology (Clifton, N.J.)

|October 26, 2010

Summary

Transcriptomics, a method for analyzing mRNA abundance, generates large datasets. We present a simple volcano score method to identify significantly altered transcripts in biological samples using Affymetrix GeneChip microarrays.

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Last Updated: Jun 7, 2026

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Published on: April 8, 2017

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Transcriptomics enables simultaneous analysis of multiple mRNA transcripts in biological samples.
High-throughput transcriptomics experiments, such as those using Affymetrix GeneChip microarrays, generate large and complex datasets.
Analyzing these datasets is crucial for understanding biological processes and experimental outcomes.

Purpose of the Study:

To describe a method for analyzing transcriptomics data generated from Affymetrix GeneChip microarrays.
To introduce a simple approach for identifying transcripts with altered abundance.
To provide a method that considers both the magnitude and statistical significance of transcript abundance changes.

Main Methods:

Utilized Affymetrix GeneChip microarrays for transcriptomics data generation.
Employed a statistical approach for data analysis.
Calculated the "volcano score" to assess transcript abundance alterations.

Main Results:

The volcano score effectively identifies transcripts with significant changes in abundance.
This method integrates the magnitude of change with statistical significance.
Demonstrated a straightforward approach for interpreting complex transcriptomics data.

Conclusions:

The volcano score is a valuable tool for identifying differentially expressed genes in transcriptomics studies.
This method simplifies the identification of key transcripts affected by experimental conditions.
The approach enhances the analysis of large-scale transcriptomics datasets, facilitating biological discovery.