Imported from openai/plugins (plugins/ngs-analysis/skills/ngs-bulk-rnaseq-counts-qc/SKILL.md). Install upstream with npx skills add openai/plugins --skill ngs-bulk-rnaseq-counts-qc. Copyright stays with the author.

Bulk RNA-seq Counts QC

Use this skill for bulk RNA-seq read processing, quantification, and count-matrix generation. If the user already has a count matrix and wants contrasts or statistics, use ngs-bulk-rnaseq-differential-expression.

Essential Inputs

Confirm:

• FASTQ or aligned-read inputs and paired-end/single-end status
• organism, genome build, FASTA, GTF, and gene ID convention
• strandedness or permission to infer strandedness
• sample sheet with biological condition, replicate, batch, and library metadata
• desired quantification: gene counts, transcript estimates, or both
• alignment strategy: STAR/Salmon, Salmon-only, featureCounts from BAMs, or existing lab protocol

Route

Prefer nf-core/rnaseq for standard processing when a stable container or HPC runtime is available. Use the local_light Snakemake/Salmon path for small local/devbox feasibility runs when Docker, registry egress, or Nextflow process containers are the blocker.

The plugin-owned local runner is:

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

Omit --execute for validation plus Snakemake workflow validation only. Use --no-dry-run only when the user wants input validation and run-envelope preparation without workflow graph validation.

The runner emits a run-local resources/ readiness bundle with resource_plan.json, resource_manifest.tsv, resource_env.sh, and resource_readiness.md. Resource checks are advisory by default for custom or reduced references; add --genome-build, --bundle-root <bundle>=<path>, and --require-resource-plan when a registered genome bundle must be complete before the run is considered ready.

Preflight command:

python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq_counts_qc --emit-install-plan
python plugins/ngs-analysis/scripts/ngs_preflight.py --profile local_light --emit-install-plan

Decision Points

• If strandedness is unknown, infer it before final counting; do not lock in a design based on library guesses.
• If strandedness is provided, carry it into the quantification command and flag any disagreement between the configured library type and Salmon's inferred format.
• Keep genome FASTA, GTF, transcriptome, and aligner indexes from the same build/release.
• Inspect per-sample reads, mapping rate, rRNA/mitochondrial fraction when available, duplication, insert size, gene-body bias, and assignment rate.
• Preserve raw counts separately from normalized expression.
• Carry sample metadata forward exactly; downstream DE depends on this table.

Outputs

Produce:

• sample sheet and command/profile
• reference manifest with genome and GTF release
• MultiQC or equivalent processing summary
• Salmon quant.sf outputs, TPM/NumReads/effective-length matrices, and carried-forward sample metadata
• Gene-level expected-count and TPM matrices derived from transcript-level Salmon outputs, plus a tx2gene provenance table
• Compact QC verdict JSON covering mapping rate, duplication, library-type agreement, and outlier samples
• Browser-safe MultiQC helper HTML pages and a localhost launch hint for reliable in-app review
• Run-local reference readiness artifacts under resources/, including the resource plan, manifest, environment exports, and Markdown readiness summary
• issues that block differential expression, such as missing replicates, mislabeled groups, or severe batch/library failures
• standard run envelope: run_manifest.json, config.json, validation/, logs/, versions/, artifact_index.json, and summary.md