Bulk Rnaseq

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End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g. "analyze my RNA-seq", "FASTQ to DESeq2", "run nf-core/rnaseq", "STAR/Salmon quantification", "build a counts matrix for DESeq2", or "go from reads to differentially expressed genes and enriched pathways". Routes between an nf-core/rnaseq (Nextflow) path and a standalone STAR/Salmon path, and covers experimental design, strandedness, and QC gates. For single-cell RNA-seq use the scanpy skill instead.

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Overview

This skill orchestrates a complete, defensible bulk RNA-seq differential-expression study, from raw sequencing reads to enriched pathways and figures. It is a router, not a reimplementation: most stages already have dedicated skills in this repo, and this skill connects them in the right order, fills the one real gap (raw reads → a gene-level counts matrix), and enforces the design and QC decisions that determine whether the final result is trustworthy.

"Defensible" means three things, applied throughout:

Reproducible — pinned pipeline/tool versions, containers where possible, recorded parameters, fixed random seeds.

Quality-gated — QC is inspected and acted on before, during, and after quantification, not skipped.

Statistically sound — adequate replication, a design that matches the biology, counts handled correctly, and FDR-controlled testing.

The pipeline is: FastQC/trim → align/quant (STAR/Salmon) → counts → DE (pydeseq2) → enrichment (pathway-enrichment) → figures.

When to Use This Skill

Use this skill when the user wants to:

Go from FASTQ files (or a sequencing run) to differentially expressed genes and pathways.

Run or configure nf-core/rnaseq, or align/quantify with STAR, Salmon, or featureCounts.

Turn Salmon/STAR/featureCounts output into a counts matrix ready for DESeq2/PyDESeq2.

Design or sanity-check a bulk RNA-seq experiment (replicates, batch, strandedness) before committing compute.

Scope an end-to-end RNA-seq analysis and decide which tools and skills to chain.

This is bulk RNA-seq (samples = biological specimens). For single-cell/nuclei data use scanpy; for the DE statistics alone use pydeseq2; for enrichment alone use pathway-enrichment.

The Pipeline at a Glance

flowchart TD fastq["Raw FASTQ + samplesheet"] --> qc["FastQC + MultiQC"] qc --> trim["Trim: fastp / Trim Galore"] trim --> align["Align + quant: STAR and/or Salmon"] align --> counts["Gene-level counts matrix"] counts --> de["Differential expression"] de --> enrich["Pathway / GSEA enrichment"] de --> fig["Figures"] enrich --> fig nfcore["nf-core/rnaseq via nextflow skill"] -.->|"path A"| align manual["Standalone recipes (this skill)"] -.->|"path B"| align bridge["build_counts_matrix.py (this skill)"] -.-> counts pydeseq2skill["pydeseq2 skill"] -.-> de pwskill["pathway-enrichment skill"] -.-> enrich vizskill["scientific-visualization skill"] -.-> fig

Use Path A — `nf-core/rnaseq` when…	Use Path B — standalone tools when…
You want the field-standard, audited, citable pipeline with one command	You have a few samples and want to learn/inspect each step
Many samples, or you'll scale to HPC/cloud	No Nextflow/containers available, or a constrained environment
Reproducibility and a full MultiQC report matter most	You need a non-standard step the pipeline doesn't expose
→ Drive it through the `nextflow` skill	→ Follow `references/upstream-manual.md`

Bulk Rnaseq

Bulk RNA-seq

Overview

When to Use This Skill

The Pipeline at a Glance

Two Upstream Paths — Pick One

Setup

Quick Start

Path A — nf-core/rnaseq (recommended)

Path B — standalone STAR/Salmon (abbreviated)

Counts → DE → enrichment (both paths)

Stage-by-Stage Workflow

The counts → DE bridge (the key glue)

Common Pitfalls

Integration with Other Skills

Reference Files

Resources