ngs-bulk-rnaseq-counts-qc — Codex skill: RNA-seq counts and setup
Plans or runs bulk RNA-seq processing from FASTQ reads to count matrices, with MultiQC and QC checks. Use it before differential-expression analysis.
npx skills add openai/plugins --skill ngs-bulk-rnaseq-counts-qc -a codexWhat ngs-bulk-rnaseq-counts-qc does
This skill works from FASTQ or aligned reads, a sample sheet, organism and genome-build details, FASTA, GTF, and library strandedness. It can route standard runs through nf-core/rnaseq, use a local Snakemake/Salmon path, or follow STAR/Salmon, Salmon-only, and featureCounts workflows. The local runner is run_bulk_rnaseq_counts_qc.py; ngs_preflight.py prepares installation and readiness checks.
Outputs include Salmon quantification files, TPM and expected-count matrices, a tx2gene provenance table, MultiQC summaries, and a compact QC verdict. The skill also keeps sample metadata and writes run artifacts such as logs, versions, validation files, and resource readiness files under resources/. When strandedness is unknown, it must be inferred before final counting; otherwise the configured library type can be wrong.
What it helps with
- process bulk RNA-seq FASTQs into count matrices
- resolve library strandedness before quantification
- collect per-sample results in a MultiQC report
- derive gene-level matrices from Salmon output
- flag missing replicates, mislabeled groups, or batch failures
Example prompts for the agent
- Run bulk RNA-seq processing from
samplesheet.csvandpath/to/fastqsusing the references inreference/. - Run
ngs_preflight.pyfor the bulk RNA-seq pipeline and prepare its installation plan. - Create count matrices and QC outputs, carry the sample metadata forward, and check the library-type agreement.
How to install ngs-bulk-rnaseq-counts-qc
1Skills CLI (Vercel)
npx skills add openai/plugins --skill ngs-bulk-rnaseq-counts-qc -a codex2Manually: put the files in place
.agents/skills/ngs-bulk-rnaseq-counts-qc/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add openai/plugins --skill ngs-bulk-rnaseq-counts-qc -a claude-code2Manually: put the files in place
~/.claude/skills/ngs-bulk-rnaseq-counts-qc/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/ngs-bulk-rnaseq-counts-qc/
Manually: put the files in place
.cursor/skills/ngs-bulk-rnaseq-counts-qc/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install openai/plugins ngs-bulk-rnaseq-counts-qc2Manually: put the files in place
.github/skills/ngs-bulk-rnaseq-counts-qc/SKILL.mdPlatform CLI
gemini skills install https://github.com/openai/plugins --path plugins/ngs-analysis/skills/ngs-bulk-rnaseq-counts-qcManually: put the files in place
.devin/skills/ngs-bulk-rnaseq-counts-qc/SKILL.mdThe legacy .windsurf/skills/ path still works
Claude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: Codex skills: how to install, where they live, how to call them
Questions about ngs-bulk-rnaseq-counts-qc
How do I install ngs-bulk-rnaseq-counts-qc in Codex?
Run npx skills add openai/plugins --skill ngs-bulk-rnaseq-counts-qc -a codex. The skill is placed at .agents/skills/ngs-bulk-rnaseq-counts-qc/SKILL.md.
What does ngs-bulk-rnaseq-counts-qc do?
It plans or runs bulk RNA-seq read processing, quantification, count-matrix generation, MultiQC reporting, and QC checks. Its outputs cover Salmon results, gene-level matrices, metadata, and a QC verdict.
What inputs are needed before a run?
Prepare FASTQ or aligned reads, organism and genome build, FASTA, GTF, a sample sheet with conditions and replicates, and strandedness or permission to infer it. The genome FASTA, GTF, transcriptome, and aligner indexes should come from the same build and release.
Source text
SKILL.md · MIT
--- name: ngs-bulk-rnaseq-counts-qc description: Run or plan bulk RNA-seq FASTQ-to-count processing with sample-sheet, strandedness, genome annotation, alignment or pseudoalignment, MultiQC, and count-matrix QC checks. --- # 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: ```bash 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: ```bash 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`
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