ROGUE: an R Shiny app for RNA sequencing analysis and biomarker discovery

Alvin Farrel*, Peng Li, Sharon Veenbergen, Khushbu Patel, John M. Maris, Warren J. Leonard*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Background: The growing power and ever decreasing cost of RNA sequencing (RNA-Seq) technologies have resulted in an explosion of RNA-Seq data production. Comparing gene expression values within RNA-Seq datasets is relatively easy for many interdisciplinary biomedical researchers; however, user-friendly software applications increase the ability of biologists to efficiently explore available datasets. Results: Here, we describe ROGUE (RNA-Seq Ontology Graphic User Environment,, a user-friendly R Shiny application that allows a biologist to perform differentially expressed gene analysis, gene ontology and pathway enrichment analysis, potential biomarker identification, and advanced statistical analyses. We use ROGUE to identify potential biomarkers and show unique enriched pathways between various immune cells. Conclusions: User-friendly tools for the analysis of next generation sequencing data, such as ROGUE, will allow biologists to efficiently explore their datasets, discover expression patterns, and advance their research by allowing them to develop and test hypotheses.

Original languageEnglish
Article number303
JournalBMC Bioinformatics
Issue number1
Publication statusPublished - Dec 2023

Bibliographical note

Funding Information:
This work was supported by the Division of Intramural Research, National Heart, Lung, and Blood Institute, NIH, and NIH grants R35 CA220500, P01 CA217959 and U54 CA232568 (JMM).

Publisher Copyright:
© 2023, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.


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