bulk-rnaseq – Data & Science skill for AI agents

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 2.0 makes STAR strandedness explicit and rejects ambiguous samples, gene sets,

transcript mappings and fractional featureCounts data that older bridge versions accepted.

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 --nfcore

# 1. Smoke-test the environment with tiny bundled data
nextflow run nf-core/rnaseq -r 3.27.0 -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 3.27.0 \
  -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)

mkdir -p qc/
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 (decoy-aware selective alignment). Determine strandedness — the wrong convention can silently discard most assigned reads. 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 skill. Load counts.csv + metadata.csv, set the design (e.g. ~batch + condition), check full rank and residual degrees

… (full content is in the download package)