ArticleGenes to cells : devoted to molecular & cellular mechanisms2025
ERSAtool: A User-Friendly R/Shiny Comprehensive Transcriptomic Analysis Interface Suitable for Education.
Article in Genes to cells : devoted to molecular & cellular mechanisms, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- ERSAtool: A User-Friendly R/Shiny Comprehensive Transcriptomic Analysis Interface Suitable for Education.Genes to cells : devoted to molecular & cellular mechanisms · 2025Article
Corrections and comments
- Update of
Authors and funding
3 authors.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.