| 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":"1.0","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
uv pip install pytximport pandas
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)
python scripts/validate_samplesheet.py --samplesheet samplesheet.csv
nextflow run nf-core/rnaseq -r 3.26.0 -profile test,docker --outdir test_results
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
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
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
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)
python scripts/build_counts_matrix.py --from salmon \
--quant-dir quant/ --tx2gene tx2gene.tsv --output-dir counts/
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.
- 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.
- 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.
- 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).
- 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.
- 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.
- Differential expression →
pydeseq2 skill. Load counts.csv + metadata.csv, set the design (e.g. ~batch + condition), fit, and test with FDR control. Inspect the PCA and p-value histogram as QC.
- Enrichment →
pathway-enrichment skill. For GSEA, rank the full gene list by the DESeq2 stat; for ORA, pass the thresholded hit list (padj < 0.05, optionally |log2FC| > 1). Map gene IDs to symbols first.
- Figures →
scientific-visualization skill. Volcano, MA, sample-distance heatmap, PCA, and enrichment dotplots, plus the MultiQC report for the QC narrative.
The counts → DE bridge (the key glue)
This is the one stage with no upstream/downstream skill, so this skill owns it. scripts/build_counts_matrix.py converts quant output into exactly what pydeseq2 expects:
- Salmon (
--from salmon): aggregates per-sample quant.sf to gene level with pytximport using counts_from_abundance="length_scaled_tpm" (the right choice for gene-level DE), needs a tx2gene map.
- STAR (
--from star): reads each ReadsPerGene.out.tab, selecting the column for your --strandedness (unstranded/forward/reverse).
- featureCounts (
--from featurecounts): parses the combined featureCounts matrix.
It writes counts.csv (genes × samples, integers) and metadata_template.csv (one row per sample) for you to fill in. Salmon/RSEM counts are estimates (non-integer); they are rounded to integers because PyDESeq2 requires integer counts — see references/counts-and-handoff.md for why this is acceptable with length_scaled_tpm and how it differs from the offset-based DESeq2+tximport route. That reference also covers Ensembl→symbol mapping (needed before enrichment) and the exact orientation PyDESeq2 wants.
Common Pitfalls
These cause most wrong or irreproducible bulk RNA-seq results:
- Too few replicates. <3 biological replicates per group gives almost no power and unstable dispersion estimates. More replicates beat deeper sequencing.
- Confounded batch and condition. If every treated sample was processed on a different day/lane than controls, the effect is unrecoverable. Randomize, and model known batches (
~batch + condition). See references/design-and-qc.md.
- Wrong strandedness. Choosing the wrong STAR column or featureCounts
-s/Salmon library type silently discards ~half the reads. Use Salmon -l A or infer strandedness, and verify the assigned-reads fraction.
- Feeding TPM/FPKM to DESeq2. DESeq2 needs raw (or length-scaled) counts, never TPM/FPKM/normalized values. The bridge handles this.
- Non-integer counts. PyDESeq2 requires integers; round Salmon estimates (the bridge does this).
- Gene-ID mismatch into enrichment. DESeq2 output is often Ensembl IDs; Enrichr/MSigDB want symbols. Map IDs before
pathway-enrichment or "nothing is significant".
- Skipping post-quant QC. Always look at the PCA and sample-distance heatmap before trusting DE — they expose swapped labels, outliers, and hidden batches.
- Mixing aligners across samples. Quantify every sample with the same tool, version, reference, and parameters.
- Unpinned versions. "latest" pipelines/genomes make results unreproducible; pin
-r, tool versions, and the genome/annotation release.
Integration with Other Skills
- Upstream execution:
nextflow (runs nf-core/rnaseq, Path A; HPC/cloud/containers).
- Reference data / gene IDs:
gget (gget ref for genome+GTF, gget info/gget search for ID mapping), database-lookup (Ensembl/NCBI), biopython/pysam (FASTA/BAM handling).
- Differential expression:
pydeseq2 (the DE engine this skill hands counts to).
- Enrichment:
pathway-enrichment (ORA + GSEA; its scripts/run_enrichment.py reads a DESeq2 results CSV directly).
- Figures & reporting:
scientific-visualization, matplotlib, seaborn; scientific-writing for the methods/results narrative.
- Related but distinct:
scanpy (single-cell), statistical-analysis (multiple-testing depth).
Reference Files
Read the relevant file when you need depth — each is self-contained:
references/upstream-nfcore.md — Path A: samplesheet format, --aligner/--pseudo_aligner choice, key params, the salmon.merged.gene_counts*.tsv outputs, MultiQC, and what to hand to pydeseq2.references/upstream-manual.md — Path B: FastQC, fastp/Trim Galore, STAR genome index + alignment + --quantMode GeneCounts, Salmon decoy-aware index + quant, featureCounts, and how to determine strandedness.references/counts-and-handoff.md — turning quant output into PyDESeq2-ready counts.csv/metadata.csv (pytximport, STAR column selection, featureCounts), the integer/estimated-count nuance, Ensembl→symbol mapping, and the DE→enrichment rank/hit-list recipe.references/design-and-qc.md — experimental design (replication, batch, confounding, design formulas) and QC-metric interpretation (mapping rate, duplication, rRNA, complexity, PCA/outliers) — the defensible-pipeline backbone.
Resources
