Bulk RNA-seq

Overview

This skill prepares bulk RNA-seq reads and quantification output for a reproducible gene-level comparison. It owns sample validation, reads-to-counts recipes, and the count/metadata handoff; downstream statistical fitting and enrichment remain in their specialist skills.

"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. Both produce gene-level counts, but STAR/featureCounts and Salmon/tximport do not generally give identical or interchangeable values: assignment rules, multimapping, and effective-length corrections differ. Choose one quantification route for the entire comparison.

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. Preserve whether counts are raw or length-scaled, the transcript-to-gene mapping release, and any offsets. Length-scaled counts must not receive a second transcript-length correction.

Setup

# This skill's glue (bridge + handoffs) — Python
uv pip install "pytximport==0.13.0" pandas numpy

# 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 conda-forge -c bioconda --strict-channel-priority \
  fastqc fastp trim-galore "star=2.7.11b" "salmon=2.8.0" subread multiqc

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