RNA modular expression laboratory (RNAmel): an integrated R/Shiny platform for reproducible bulk RNA-seq analysis and interpretation
Year: 2026
Type: article
Abstract: Background Bulk RNA-sequencing is routine, but turning a gene-level count matrix into biological insight still requires combining specialised tools for quality control, differential expression, dimensionality reduction, enrichment, co-expression networks, and activity inference. These steps are typically performed either with bespoke scripts that are hard to reuse and audit, or with interactive applications that ease exploration but can obscure provenance and couple analysis logic to interface code, making the analytical core hard to test, script, or reuse. Results We present the RNA Modular Expression Laboratory (RNAmel), an open-source R/Shiny platform for reproducible downstream bulk RNA-seq analysis, from count matrix and sample metadata to biological interpretation. Its distinguishing feature is architectural: rather than a monolithic Shiny application, RNAmel is implemented as a tested R package whose analysis and figure-generation logic lives in a public function layer covered by more than 440 unit tests, with the Shiny interface acting as a thin wrapper over this API. The same tested functions therefore drive the interactive session, the exported R script, and the test suite. On a single loaded dataset, RNAmel spans differential expression (DESeq2), PCA and UMAP exploration, multi-contrast comparison, gene-set enrichment and over-representation analysis, WGCNA co-expression networks, transcription-factor and pathway activity inference (decoupleR), per-sample signatures (GSVA/ssGSEA), and optional, opt-in AI-assisted interpretation. Analyses are recoverable as portable sessions and exported as runnable R scripts, Methods text, and self-contained HTML reports, with a versioned Docker environment and an accompanying renv.lock file recording the exact package versions. We demonstrate RNAmel on two published human datasets, a glucocorticoid-treatment study and a 120-sample, eight-cancer-type TCGA subset, and report internal consistency and cross-method concordance checks (exact reproduction of exported analyses; agreement with independent DESeq2 and WGCNA reference implementations; concordance with limma-voom). Conclusions By placing a tested, scriptable function layer beneath an interactive interface, RNAmel combines exploratory accessibility with script-level transparency and reproducibility for downstream bulk RNA-seq analysis. It is freely available under the MIT licence at https://github.com/KmBioChemo/RNAmel .
Source: BMC Bioinformatics
Institution: Department of Biomedicine Basel
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W7170049916
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