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Updated: Sep 15, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

ERSAtool: A User-Friendly R/Shiny Comprehensive Transcriptomic Analysis Interface Suitable for Education.

Sujith Taridalu¹, Ayyappa Kumar Sista Kameshwar¹, Masako Suzuki¹

¹Department of Nutrition, Texas A&M University, College Station, TX, USA.

Biorxiv : the Preprint Server for Biology

|July 16, 2025

Summary

This summary is machine-generated.

ERSAtool simplifies complex RNA sequencing (RNA-seq) data analysis with an R/Shiny interface, making transcriptomic studies accessible. This educational tool aids researchers and students in gene expression analysis and functional interpretation.

Keywords:

Differential gene expression Educational tool Functional enrichment Interactive visualization RNA-seq analysis Shiny application Transcriptomics

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

RNA sequencing (RNA-seq) is crucial for gene expression analysis but requires significant bioinformatics expertise.
The complexity of RNA-seq data analysis presents a barrier for many students and researchers.
A need exists for user-friendly tools to facilitate advanced transcriptomic analyses.

Purpose of the Study:

To introduce ERSAtool, an R/Shiny interface designed to simplify RNA sequencing data analysis.
To provide an intuitive graphical visualization of the entire RNA-seq workflow.
To enhance accessibility of sophisticated transcriptomic analyses for educational and research purposes.

Main Methods:

Developed ERSAtool as a comprehensive R/Shiny application.
Integrated established Bioconductor packages for high-standard analyses.
Supported various input formats and direct integration with Gene Expression Omnibus (GEO) for metadata.
Guided users through normalization, visualization, differential expression, and functional enrichment analyses (GO, GSEA).

Main Results:

ERSAtool offers an intuitive graphical interface for RNA-seq analysis.
The tool simplifies complex steps like normalization, differential expression, and functional interpretation.
Generated comprehensive, downloadable reports for enhanced reproducibility and knowledge sharing.
Facilitated educational use in transcriptomics courses.

Conclusions:

ERSAtool significantly lowers the technical barrier for RNA-seq data analysis.
The tool improves accessibility to advanced transcriptomic capabilities for a wider audience.
ERSAtool has the potential to accelerate discoveries in various biological fields by democratizing RNA-seq analysis.