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

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.