Abstract
RNA sequencing (RNA-seq) has become an essential technology for assessing gene expression profiles in biomedical research. However, the coding complexity of RNA-seq data analysis remains a significant barrier for students and researchers without extensive bioinformatics expertise. We present the Educational RNA-Seq Analysis tool (ERSAtool), a comprehensive R/Shiny interface that provides an intuitive graphical visualization of the complete RNA-seq analysis workflow. The application is built on established Bioconductor packages and upholds high standards in analyses while significantly reducing the technical expertise required to conduct sophisticated transcriptomic analyses. ERSAtool supports various input formats, such as raw count matrices and STAR alignment outputs. It generates sample information metadata through direct integration with the international public repository, Gene Expression Omnibus (GEO). The application guides users through normalization, data visualization, differential expression analysis, and functional interpretation using Gene Ontology and Gene Set Enrichment Analyses. All results can be compiled into comprehensive, downloadable reports that enhance reproducibility and knowledge sharing. The design targets features that support educational use, making it especially helpful for teaching transcriptomics in undergraduate to graduate-level bioinformatics courses and enhancing access to advanced transcriptomic analysis, potentially accelerating discoveries across various biological fields. ERSAtool is available for free at https://github.com/SuzukiLabTAMU/ERSAtool.