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Related Experiment Video

Updated: Feb 13, 2026

A User-friendly and Powerful R Analysis of Large-scale Datasets
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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, Texas, USA.

Genes to Cells : Devoted to Molecular & Cellular Mechanisms
|February 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces ERSAtool, an R/Shiny application simplifying RNA sequencing (RNA-seq) data analysis for researchers and students. This tool enhances transcriptomic analysis accessibility and reproducibility in biomedical research.

Keywords:
RNA‐seq analysiseducational toolfunctional enrichmentinteractive visualizationshiny applicationtranscriptomics differential gene expression

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • RNA sequencing (RNA-seq) is crucial for gene expression analysis in biomedical research.
  • Complex bioinformatics expertise is a barrier for many researchers using RNA-seq.
  • Need for accessible tools to analyze transcriptomic data.

Purpose of the Study:

  • To develop an intuitive graphical interface for RNA-seq analysis.
  • To lower the technical barrier for sophisticated transcriptomic analyses.
  • To support educational use in bioinformatics courses.

Main Methods:

  • Developed ERSAtool, an R/Shiny application.
  • Utilized established Bioconductor packages for analysis.
  • Integrated with Gene Expression Omnibus (GEO) for metadata.
  • Implemented normalization, visualization, differential expression, and functional enrichment analyses.

Main Results:

  • ERSAtool provides a user-friendly graphical interface for the complete RNA-seq workflow.
  • Supports various input formats and integrates with GEO for metadata.
  • Facilitates differential expression analysis and functional interpretation (GO, GSEA).
  • Generates comprehensive, downloadable reports for reproducibility.

Conclusions:

  • ERSAtool significantly reduces the expertise needed for advanced RNA-seq analysis.
  • Enhances accessibility of transcriptomic analysis for education and research.
  • Aims to accelerate biological discoveries by democratizing RNA-seq data analysis.