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Related Experiment Video

Updated: Mar 21, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

PanGraphRNA: An efficient and flexible bioinformatics platform for graph pangenome-based RNA-seq data analysis.

Yifan Bu^1,2,3, Zhixu Qiu³, Wen Sun^1,2

¹State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, College of Life Sciences, Northwest A&F University, Yangling, 712100, China.

Journal of Integrative Plant Biology

|March 20, 2026

Summary

This summary is machine-generated.

PanGraphRNA overcomes reference bias in RNA-seq analysis using graph pangenomes. This bioinformatics platform improves gene expression quantification and identifies novel genetic insights in plants like Arabidopsis.

Keywords:

RNA sequencing galaxy graph pangenome population reference bias

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Area of Science:

Bioinformatics
Genomics
Plant Science

Background:

Transcriptome deep sequencing (RNA-seq) analysis is challenged by reference bias from single linear reference (SLR) genomes.
Graph-based pangenomes offer a solution by incorporating genetic variations, but lack dedicated analytical tools.
Existing methods struggle to fully leverage complex genomic data for accurate transcriptomic analysis.

Purpose of the Study:

To introduce PanGraphRNA, an integrated bioinformatics platform for RNA-seq analysis utilizing graph pangenomes.
To provide accessible, traceable, and reproducible tools for constructing, evaluating, and applying graph pangenomes.
To enhance the accuracy and scope of transcriptomic studies in plants.

Main Methods:

Development of PanGraphRNA on the Galaxy web-based framework.
Implementation of functional modules for graph pangenome construction and application.
Testing with real and simulated RNA-seq data from Arabidopsis, rice, and maize.

Main Results:

PanGraphRNA demonstrated superior read alignment accuracy and gene expression quantification compared to the SLR approach.
The platform enabled the discovery of drought-induced genes and flowering time-related quantitative trait loci missed by conventional methods.
Successful application to RNA-seq datasets from Arabidopsis, rice, and maize confirmed its broad utility.

Conclusions:

PanGraphRNA effectively mitigates reference bias in RNA-seq analysis.
The platform enhances the precision and comprehensiveness of transcriptomic studies in key plant species.
Standardized, containerized workflows facilitate wider adoption of advanced pangenome-based transcriptomic research.