| name | ngs-bulk-rnaseq |
| description | Dispatch bulk RNA-seq requests to FASTQ-to-count QC or count-matrix differential-expression skills using nf-core/rnaseq, STAR, Salmon, featureCounts, MultiQC, and R/Bioconductor workflows. |
Prerequisite: the plugins/ngs-analysis/ plugin root (its scripts/*.py and references/*.json) is not vendored in this flat vault. Install the ngs-analysis plugin from openai/plugins at the repo root, or every python plugins/ngs-analysis/... command below fails with No such file or directory.
Bulk RNA-seq
Use this skill as the bulk RNA-seq dispatcher. Route FASTQ/BAM processing to count-generation QC, and route count-matrix statistical analysis to differential-expression guidance.
Essential Inputs
Confirm:
- organism and genome build
- FASTA and GTF, or supported nf-core genome key
- paired-end or single-end reads
- strandedness, or whether to infer strandedness
- sample sheet and metadata
- counts-only vs differential expression
- contrasts, covariates, and batch terms for differential expression
Dispatch
- FASTQ or aligned reads to raw counts, transcript estimates, or MultiQC summaries:
ngs-bulk-rnaseq-counts-qc
- Raw count matrix plus sample metadata to contrasts, plots, and DE result tables:
ngs-bulk-rnaseq-differential-expression
If the user asks for both, run count-generation planning first and start differential expression only after the raw count matrix, sample metadata, replicates, design formula, and contrasts are confirmed.
Public Default
Prefer nf-core/rnaseq for standardized processing when a stable container or HPC runtime is available. Use the local_light Snakemake/Salmon path when Docker, registry egress, or Nextflow process containers are unavailable and a compact local run is appropriate.
Plugin-Owned Local Paths
Use the counts/QC runner for local FASTQ-to-matrix execution:
python plugins/ngs-analysis/scripts/run_bulk_rnaseq_counts_qc.py \
--sample-sheet samplesheet.csv \
--fastq-root path/to/fastqs \
--transcriptome-fasta reference/transcriptome.fasta \
--genome-fasta reference/genome.fa \
--annotation-gtf reference/genes.gtf \
--execute
Use the differential-expression runner when the user already has a count or expression matrix:
python plugins/ngs-analysis/scripts/run_bulk_rnaseq_de.py \
--count-matrix count_matrix.tsv \
--sample-metadata sample_metadata.tsv \
--contrasts contrasts.tsv \
--execute
Preflight
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq --emit-install-plan
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq_counts_qc --emit-install-plan
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq_differential_expression --emit-install-plan
python plugins/ngs-analysis/scripts/ngs_preflight.py --profile local_light --emit-install-plan
Kickoff Pattern
Preflight run:
nextflow run nf-core/rnaseq \
-profile test,docker \
--outdir results/rnaseq_test
Real run skeleton:
nextflow run nf-core/rnaseq \
-profile docker \
--input samplesheet.csv \
--outdir results/rnaseq \
--genome GRCh38 \
--aligner star_salmon
If strandedness is unknown, run inference or use the pipeline's strandedness detection before committing to final counts.
Local execution run:
python plugins/ngs-analysis/scripts/run_bulk_rnaseq_counts_qc.py \
--sample-sheet samplesheet.csv \
--fastq-root path/to/fastqs \
--transcriptome-fasta reference/transcriptome.fasta
The local runners create a standard run envelope with run_manifest.json, config.json, validation/, logs/, versions/, artifact_index.json, and summary.md. Do not depend on development-only eval harness paths in a shared package.
Downstream
Only start DESeq2/edgeR/limma analysis after confirming biological replicates, design formula, and contrasts. Preserve the raw count matrix and sample metadata.
