/bulk-rnaseq

name: bulk-rnaseq
description: Prepares bulk RNA-seq FASTQ, Salmon, STAR or featureCounts output for gene-level differential expression. Covers nf-core/rnaseq and standalone quantification, biological replication, strandedness, reference provenance, validated count assembly and a PyDESeq2 handoff. Use for FASTQ-to-counts analysis, nf-core/rnaseq configuration, STAR/Salmon quantification, or building a counts matrix for DESeq2. For single-cell data use scanpy; for statistical fitting alone use pydeseq2.
license: MIT
compatibility: Requires Python 3.11+ with pandas and numpy; Salmon import also needs pytximport. Read processing needs Nextflow with containers or standalone bioinformatics tools. Network access is needed for installation and reference downloads.
metadata:
  version: "2.0"
  last-reviewed: "2026-09-30"
  skill-author: K-Dense Inc.

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.

| 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**. 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

Reviewed against nf-core/rnaseq 3.27.0, STAR 2.7.11b documentation, Salmon 2.8.0, pytximport 0.13.0 and PyDESeq2 0.5.4. The bundled Python bridge and a tiny Salmon index/quant run were executed; the full Nextflow/STAR/trimming recipes are illustrative, not an end-to-end validation. Salmon 2.x cannot read older C++ indexes: rebuild them. The bridge's length-scaled Salmon route is for full-length bulk RNA-seq; 3′ counting assays need original counts without transcript-length correction.

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.

Version 2.