CoTRA: an integrated R/Shiny framework for transparent bulk and single-cell RNA-seq analysis

Authors

Seemab, U., Vainionpaa, K., Tanoli, Z., Leinonen, H. O.

Abstract

Bulk and single-cell RNA sequencing (scRNA-seq) have become essential for investigating disease mechanisms and identifying diagnostic biomarkers. However, the growing volume of transcriptomic data remains difficult to reuse efficiently for many researchers. Downstream analysis often requires multiple statistical, visualization, and reporting tools, creating fragmented workflows that reduce transparency and reproducibility, particularly when analyzing scRNA-seq data. To address this gap, we developed CoTRA (Comprehensive Toolbox for RNA-seq Analysis), an open-source R/Shiny package for bulk and scRNA analysis. CoTRA integrates established methods into modular workflows, exposes parameters, and offers alternatives at selected stages. It supports bulk RNA-seq quality assessment, differential expression, annotation, enrichment, and reporting, as well as scRNA quality control, dimensionality reduction, clustering, marker identification, cell-type annotation, differential abundance, trajectory inference, pathway activity, and cell-cell communication. CoTRA runs on workstations or HPC environments without mandatory external data submission and was tested on Linux, Windows, and macOS. Compared with 14 other platforms for bulk RNA-seq/scRNA-seq, CoTRA supported 46 of 49 predefined functionality criteria. Tool validation using published rd10 retinal bulk RNA-seq identified 1,947 shared differentially expressed genes with concordant direction and strong log2 fold-change agreement. A retinal scRNA-seq case study demonstrated appropriate clustering, cell-type resolved analysis, and pathway activity scoring. CoTRA provides a graphical environment for bulk and single-cell RNA-seq analysis while retaining parameter transparency, methodological flexibility, and reproducible outputs. Strong concordance with the published bulk RNA-seq analysis supports the workflow consistency, while the single-cell case study demonstrates its applicability to advanced scRNA-seq analysis. The source code is freely available at https://github.com/UmairSeemab/CoTRA.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 29 Aug 2026.

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