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Related Experiment Video

Updated: May 26, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

The use of open source bioinformatics tools to dissect transcriptomic data.

Benjamin M Nitsche¹, Arthur F J Ram, Vera Meyer

¹Department Molecular Microbiology and Biotechnology, Institute of Biology Leiden, Leiden University, Leiden, The Netherlands.

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

|December 21, 2011

Summary

This study demonstrates how to use R and Bioconductor for analyzing fungal transcriptomic data from microarrays. Protocols are provided for identifying differentially expressed genes and building gene coexpression networks in Aspergillus niger.

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Area of Science:

Fungal transcriptomics
Bioinformatics
Gene expression analysis

Background:

Microarrays are essential for studying fungal physiology at the transcriptomic level.
Data preprocessing, including quality control and normalization, is crucial for microarray analysis.
Statistical methods like differential gene expression and network construction are key to interpreting transcriptomic data.

Purpose of the Study:

To provide a guide for analyzing fungal microarray data using R and Bioconductor.
To detail protocols for identifying differentially expressed genes.
To demonstrate the construction of gene coexpression networks.

Main Methods:

Utilizing Bioconductor, an open-source software suite for R.
Applying preprocessing steps: quality control, background correction, normalization, and summarization.
Performing statistical analyses: differential gene expression and gene coexpression network construction.

Main Results:

Successfully applied R and Bioconductor to analyze publicly available Aspergillus niger microarray datasets.
Detailed protocols enable researchers to identify differentially expressed genes.
Gene coexpression networks were constructed, offering insights into fungal gene regulation.

Conclusions:

R and Bioconductor offer a powerful, open-source platform for fungal transcriptomic analysis.
The provided protocols facilitate the identification of key genes and regulatory networks.
This approach enhances the study of fungal physiology through transcriptomic data dissection.