Abstract: Transcriptome deep sequencing (RNA-seq) data analysis is often affected by reference bias introduced by the use of a single linear reference (SLR) genome. Graph-based pangenomes can mitigate this bias by integrating the SLR genome with complex genetic variations within a species; however, their application remains limited owing to a lack of dedicated analytical tools. Here, we present PanGraphRNA, an integrated bioinformatics platform for RNA-seq data analysis using a graph pangenome as reference. Built on the Galaxy web-based framework, PanGraphRNA provides functional modules for constructing, evaluating, and applying graph pangenomes across different population scales, thus enabling accessibility, traceability, and reproducibility throughout the analysis. Applied to both real and simulated RNA-seq data sets from Arabidopsis (Arabidopsis thaliana), PanGraphRNA outperformed the SLR approach, achieving higher read alignment accuracy and more precise gene expression quantification. PanGraphRNA enabled the identification of drought stress-induced genes and flowering time-related quantitative trait loci that were previously missed with the conventional SLR approach. Furthermore, we successfully applied PanGraphRNA to process RNA-seq data sets from rice (Oryza sativa) and maize (Zea mays). By providing standardized, containerized workflows, PanGraphRNA will facilitate transcriptomic research in key plant species, including Arabidopsis, rice, and maize.

Key words: galaxy, graph pangenome, population, reference bias, RNA sequencing