Routes bulk RNA-seq requests to workflows for generating expression counts and checking quality, or comparing gene expression between groups. Checks input data, sample information, and analysis requirements before directing the next step.
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.
Review the instructions the agent will receive before you install.
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.
Confirm:
ngs-bulk-rnaseq-counts-qcngs-bulk-rnaseq-differential-expressionIf 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.
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.
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
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
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.
Only start DESeq2/edgeR/limma analysis after confirming biological replicates, design formula, and contrasts. Preserve the raw count matrix and sample metadata.
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