Skip to main content
skill CX Codex GP ChatGPT official · OpenAI

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.

Install into Codex · Skills CLI (Vercel) All methods and platforms ↓
npx skills add openai/plugins --skill ngs-bulk-rnaseq-counts-qc -a codex
Open on GitHub

What 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.csv and path/to/fastqs using the references in reference/.
  • Run ngs_preflight.py for 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 codex

2Manually: put the files in place

.agents/skills/ngs-bulk-rnaseq-counts-qc/SKILL.md

For all projects use ~/.agents/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`

© OpenAI · MIT · openai/plugins

Similar skills

All skills for Science and research →

From the same set422

The whole set →
ngs-dna-umi-panel-variants Plan or run targeted DNA panel analysis with UMIs, consensus reads and low-frequency variant calling. Useful when molecular evidence and panel-specific QC matter. CX Codex GP ChatGPT 2installs ngs-dna-somatic-variants A skill that plans and runs somatic variant workflows for tumor-normal and tumor-only designs, WGS, WES and cancer panels. Useful for checking sample pairs before a pipeline starts. CX Codex GP ChatGPT 2installs ngs-chip-cutrun-peaks-qc Plans and runs QC for ChIP-seq, CUT&RUN, and CUT&Tag, covering controls, peak calls, bigWigs, replicates, and differential binding. CX Codex GP ChatGPT 2installs ngs-bulk-rnaseq A dispatcher for bulk RNA-seq work: it splits FASTQ-to-count processing from count-matrix statistics. Useful when you need expression counts and then differentially expressed genes. CX Codex GP ChatGPT 2installs ngs-bcl-to-fastq Validates an Illumina run folder and sample sheet, plans demultiplexing, and runs BCL-to-FASTQ conversion. Useful when the input is a sequencing run folder. CX Codex GP ChatGPT 2installs website-to-hyperframes This skill turns a website URL into a HyperFrames video project, covering capture, scripting, storyboarding, voice timing, and validation. CX Codex GP ChatGPT 2installs hyperframes A skill for building video in HTML: compositions, title cards, captions, narration and music-driven animation. Useful when the video should come out of code, not an editor. CX Codex GP ChatGPT 2installs hyperframes-cli This skill runs HyperFrames projects from setup to video output, with checks for composition layout, browser preview, transcription, and speech generation. CX Codex GP ChatGPT 2installs

Project sponsors

Companies that keep this analytics open
Gold sponsor

Impressions, clicks and code copies — open statistics. We keep the numbers public for the same reason NeBlask itself is public. Become a sponsor