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ngs-bulk-rnaseq-counts-qc

Processes bulk RNA-seq FASTQ reads into count matrices with MultiQC quality checks.

5.3k|765|Updated Mar 4, 2026
Search Tags:#bioinformatics#rna-seq#quantification#ngs#salmon#count matrix#MultiQC
One-click install
npx skills add https://github.com/openai/plugins --skill ngs-bulk-rnaseq-counts-qc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ngs-bulk-rnaseq-counts-qc
Source: https://github.com/openai/plugins/tree/main/plugins/ngs-analysis/skills/ngs-bulk-rnaseq-counts-qc
Command: npx skills add https://github.com/openai/plugins --skill ngs-bulk-rnaseq-counts-qc

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Bulk RNA-seq analysis requires coordinating sample sheets, reference genomes, strandedness, alignment or pseudoalignment, and QC reporting, and mistakes in any step silently corrupt downstream differential expression results. This Skill plans and runs the full FASTQ-to-count workflow with validation and QC gates so count matrices are trustworthy before statistical analysis.

Core Features & Use Cases

  • End-to-End Quantification: Runs nf-core/rnaseq or a local Snakemake/Salmon path to produce Salmon quant.sf outputs, TPM and NumReads matrices, and gene-level expected counts with a tx2gene provenance table.
  • Strandedness and Reference Validation: Infers or confirms library strandedness, checks genome FASTA, GTF, and index consistency, and emits a resource readiness bundle with plan, manifest, and environment exports.
  • QC Reporting: Generates MultiQC summaries, browser-safe helper HTML pages, and a compact QC verdict JSON covering mapping rate, duplication, library-type agreement, and outlier samples.
  • Use Case: A researcher receives 24 paired-end FASTQ samples with unknown strandedness; the Skill infers strandedness, validates references, runs Salmon quantification, and flags two outlier samples before differential expression.

Quick Start

Ask the assistant to plan and run a bulk RNA-seq FASTQ-to-count workflow for your sample sheet, validating strandedness and references and producing MultiQC and count-matrix QC outputs.

Frequently Asked Questions about ngs-bulk-rnaseq-counts-qc

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate a count matrix from bulk RNA-seq FASTQ files?▼

Provide a sample sheet, FASTQ files, genome FASTA, GTF annotation, and transcriptome FASTA, then run the workflow with nf-core/rnaseq or the local Snakemake/Salmon runner. It produces Salmon quant.sf outputs plus gene-level expected-count and TPM matrices.

Should I use STAR alignment or Salmon pseudoalignment for RNA-seq quantification?▼

The Skill supports STAR/Salmon, Salmon-only, or featureCounts from BAMs depending on your protocol. Salmon-only pseudoalignment suits fast transcript-level quantification, while STAR alignment is preferred when alignment-level QC or downstream BAM-based analyses are needed.

How do I determine RNA-seq library strandedness before quantification?▼

If strandedness is unknown, the Skill infers it before final counting rather than guessing from library metadata. It also flags disagreements between the configured library type and Salmon's inferred format.

Can I run RNA-seq quantification locally without Docker or Nextflow?▼

Yes, the local_light profile uses a Snakemake and Salmon path for small runs when Docker, registry access, or Nextflow containers are unavailable. A preflight script emits an install plan for the required tools.

What QC metrics should I check before differential expression analysis?▼

Review per-sample read counts, mapping rate, duplication, insert size, gene-body bias, and assignment rate via MultiQC. The Skill also emits a QC verdict JSON flagging outlier samples and issues like missing replicates or mislabeled groups that block differential expression.

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