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Updated: Jan 1, 2026
05:07
Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
Published on: November 7, 2025
273
BISR-RNAseq: an efficient and scalable RNAseq analysis workflow with interactive report generation
Venkat Sundar Gadepalli1,2,3, Hatice Gulcin Ozer1,2,3, Ayse Selen Yilmaz1,2,3
1Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
BMC Bioinformatics
|December 22, 2019
Summary
This study introduces a new RNA sequencing (RNAseq) workflow using open-source software for gene expression analysis. The pipeline integrates quality control and differential expression results into an R shiny application for easier interpretation.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- RNA sequencing (RNAseq) is a cost-effective method for gene expression profiling.
- High-performance computing environments enable complex biological data analysis.
Purpose of the Study:
- To introduce a novel RNA sequencing workflow.
- To demonstrate the feasibility of an open-source, high-performance computing-based RNAseq analysis pipeline.
Main Methods:
- Implementation of several open-source software tools.
- Application of the workflow to publicly available RNAseq datasets from GEO.
- Development of a configurable R shiny web application for results visualization.
Main Results:
- The workflow successfully processed raw RNAseq data, including alignment, quality control (QC), and gene-wise counts generation.
- Demonstrated feasibility of the pipeline on public RNAseq datasets.
- Integrated QC and differential expression results into a user-friendly R shiny application.
Conclusions:
- The developed workflow facilitates comprehensive analysis of raw RNAseq data.
- Seamless integration of analysis results into a configurable R shiny application enhances data interpretation.
- The open-source workflow provides a reproducible and accessible tool for gene expression profiling.
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