/bulk-rnaseq

name: bulk-rnaseq
description: 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.
license: MIT
metadata:
  version: "1.1"
  skill-author: K-Dense Inc.

Bulk RNA-seq

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

Two Upstream Paths — Pick One

The reads → counts stage can be run two ways. They produce equivalent gene counts; choose by context, then stay on that path.

| 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` |

When unsure, prefer **Path A**: `nf-core/rnaseq` already wires together FastQC → trimming → STAR/Salmon → quantification → tximport → MultiQC with sensible, reviewed defaults, which is the most defensible option. Path B exists for transparency and constrained setups.

Both paths converge on a **gene-level counts matrix**, after which the workflow is identical.

Setup

# This skill's glue (bridge + handoffs) — Python
uv pip install pytximport pandas

# Downstream skills install their own deps:
#   pydeseq2 skill           -> uv pip install pydeseq2
#   pathway-enrichment skill -> uv pip install gseapy gprofiler-official

# Path A (nf-core): only Nextflow + a container engine are needed — see the `nextflow` skill.

# Path B (standalone tools): install via bioconda. Pin versions for reproducibility.
conda create -n rnaseq -c bioconda -c conda-forge \
  fastqc fastp trim-galore "star=2.7.11b" "salmon=1.10.3" subread multiqc

Record the exact versions you use (pipeline revision, tool versions, reference genome + annotation release) — they belong in the methods section and make the analysis reproducible.

Quick Start

Path A — nf-core/rnaseq (recommended)

# 0. Validate the samplesheet first (catches the most common failures early)
python scripts/validate_samplesheet.py --samplesheet samplesheet.csv

# 1. Smoke-test the environment with tiny bundled data
nextflow run nf-core/rnaseq -r 3.26.0 -profile test,docker --outdir test_results

# 2. Real run: pin the revision, pick an aligner, pass a samplesheet + reference
nextflow run nf-core/rnaseq -r 3.26.0 \
  -profile docker \
  --input samplesheet.csv \
  --genome GRC