AlignRNAseqFlow
End-to-end bulk RNA-seq analysis with interactive Shiny interface
The Challenge
Bulk RNA-seq remains the workhorse of transcriptomics research, but the analysis workflow from raw count matrices to publication-ready results involves dozens of decisions: which normalization method to apply, how to handle batch effects, which differential expression framework to use, what enrichment approach captures the biology best. Each choice affects downstream results, and getting any of them wrong can lead to missed discoveries or false conclusions.
Existing tools address individual steps — DESeq2 for DE, clusterProfiler for enrichment — but stitching them together into a coherent, reproducible workflow requires substantial bioinformatics expertise. Researchers need an integrated solution that handles the full pipeline while keeping every analytical decision transparent and configurable.
How AlignRNAseqFlow Helps
AlignRNAseqFlow is a complete end-to-end bulk RNA-seq analysis platform built as a two-package R ecosystem. The core engine (41 exported functions) handles all computation, while the interactive Shiny application provides a 6-tab guided workflow that takes you from raw counts to publication figures without writing a single line of code.
The platform supports dual differential expression engines — DESeq2 and limma-voom — letting you choose the best approach for your experimental design or compare results between methods. Batch correction is built in via sva (surrogate variable analysis) and ComBat, with visual diagnostics to verify correction effectiveness.
Pathway enrichment goes beyond simple gene lists with four complementary approaches: clusterProfiler for GO/KEGG over-representation, GAGE for pathway-level differential expression, GSVA for sample-level enrichment scoring, and pathview for KEGG pathway visualization. This multi-method strategy captures different aspects of the biology that any single approach would miss.
Both Human and Mouse organisms are natively supported with automatic gene annotation, and the entire workflow produces publication-ready figures at every step — from QC plots through volcano plots and pathway maps.
The AlignRNAseqFlow Interface
Six-tab guided workflow with real-time validation and interactive visualizations:
What You Receive
Dual DE Analysis
Differential expression results from both DESeq2 and limma-voom with full statistics — log2 fold changes, adjusted p-values, and gene annotations for every gene in your dataset.
Multi-Method Enrichment
GO, KEGG, and Reactome pathway analysis via clusterProfiler, GAGE, and GSVA. KEGG pathway maps with gene expression overlaid via pathview.
Publication-Ready Figures
High-resolution QC plots, PCA, volcano plots, MA plots, heatmaps, enrichment dotplots, and KEGG pathway maps — all formatted for journal submission.
Complete Export Package
All results, figures, and normalized expression matrices exported as a single downloadable archive. Fully reproducible with logged parameters.
Methodology & Validation
limma-voom
clusterProfiler
GAGE
GSVA
pathview
sva / ComBat
Bioconductor
AlignRNAseqFlow implements field-standard methods from the Bioconductor ecosystem. DESeq2 (Love et al., 2014) provides negative binomial modeling with shrinkage estimation, while limma-voom (Law et al., 2014) handles the mean-variance relationship for robust linear modeling. Both engines are available for every analysis.
Batch correction uses sva for surrogate variable estimation and ComBat for known batch removal, with visual diagnostics (PCA before/after) to verify correction effectiveness. Pathway analysis combines clusterProfiler (Wu et al., 2021), GAGE for generally applicable gene set enrichment, and GSVA for sample-level pathway scoring.
Validation: Tested on the MCF7 breast cancer dataset (30 samples, 79K → 32.9K genes after filtering). Identified 62 differentially expressed genes with expected biological signatures. The software passes 170 unit tests with 0 R CMD check errors across the two-package architecture.
Ideal For
- Researchers who need end-to-end RNA-seq analysis without command-line bioinformatics
- Labs comparing DESeq2 vs limma-voom results on the same dataset
- Multi-factor experimental designs with batch effects requiring correction
- Studies requiring multi-method pathway enrichment (GO, KEGG, Reactome)
- Treatment vs. control comparisons in human or mouse models
- Time-course and dose-response experiments
- Any standard bulk RNA-seq experiment with count matrices
Start Your Analysis
Ready to analyze your data with AlignRNAseqFlow? Submit your project and we'll scope a plan tailored to your experimental design.