bulk-rnaseq

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 GRCh38 \
  --aligner star_salmon \
  --outdir results \
  -resume

nf-core/rnaseq runs tximport internally, so gene counts come out already merged — no bridge script needed. Use results/star_salmon/salmon.merged.gene_counts_length_scaled.tsv for DE. Samplesheet format, aligner choice, and outputs: references/upstream-nfcore.md. For engine/HPC/cloud/container detail, use the nextflow skill.

Path B — standalone STAR/Salmon (abbreviated)

fastqc -o qc/ reads/*.fastq.gz                      # 1. QC raw reads
fastp -i s1_R1.fq.gz -I s1_R2.fq.gz \
      -o s1_R1.trim.fq.gz -O s1_R2.trim.fq.gz \
      --thread 4 -j s1.fastp.json                   # 2. Trim adapters/low-quality
salmon quant -i salmon_index -l A \
      -1 s1_R1.trim.fq.gz -2 s1_R2.trim.fq.gz \
      --gcBias --seqBias -p 8 -o quant/s1            # 3. Quantify (per sample)

Full recipes (FastQC, fastp/Trim Galore, STAR index+align+--quantMode GeneCounts, Salmon decoy-aware index, featureCounts, strandedness): references/upstream-manual.md.

Counts → DE → enrichment (both paths)

# Path B only: assemble a gene x sample counts matrix + metadata template for PyDESeq2
python scripts/build_counts_matrix.py --from salmon \
  --quant-dir quant/ --tx2gene tx2gene.tsv --output-dir counts/

# Then hand off (see the dedicated skills):
#   pydeseq2:           counts.csv + metadata.csv -> DE table (log2FC, padj, stat)
#   pathway-enrichment: rank by `stat` (GSEA) or padj+|LFC| hit list (ORA)
#   scientific-visualization / matplotlib: volcano, MA, heatmap, PCA, enrichment dotplot

Stage-by-Stage Workflow

Work top to bottom. Each stage names the skill or file that owns the detail. Don't skip the design/QC stages — they are where bulk RNA-seq studies most often go wrong.

1. Design & sample sheet. Confirm ≥3 biological replicates per group, identify batch/confounders, and choose the comparison(s). Build the samplesheet and validate it with scripts/validate_samplesheet.py. Rationale and rules: references/design-and-qc.md.
2. Raw-read QC. FastQC per file; aggregate with MultiQC. Check per-base quality, adapter content, duplication, and over-representation. Thresholds: references/design-and-qc.md.
3. Trimming. Remove adapters and low-quality tails (via fastp or Trim Galore). Re-run FastQC to confirm. Recipes: references/upstream-manual.md (Path A does this for you).
4. Align / quantify. STAR (genome alignment + --quantMode GeneCounts) and/or Salmon (transcript quasi-mapping, decoy-aware). Determine strandedness — it is easy to get wrong and silently halves your counts. Detail: references/upstream-manual.md; pipeline params: references/upstream-nfcore.md.
5. Build the counts matrix. Turn quant output into a gene × sample integer matrix and a metadata template (scripts/build_counts_matrix.py). The estimated-count and gene-ID-mapping nuances live in references/counts-and-handoff.md.
6. **Differential expression → pydeseq2