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ngs-scrna-seq

Route single-cell RNA-seq FASTQs to public count-generation workflows and defer post-count analysis.

5.3k|765|Updated Mar 4, 2026
Search Tags:#bioinformatics#single-cell#nf-core#scrna-seq#fastq#count matrix#cell ranger
One-click install
npx skills add https://github.com/openai/plugins --skill ngs-scrna-seq
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ngs-scrna-seq
Source: https://github.com/openai/plugins/tree/main/plugins/ngs-analysis/skills/ngs-scrna-seq
Command: npx skills add https://github.com/openai/plugins --skill ngs-scrna-seq

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Starting a single-cell or single-nucleus RNA-seq analysis from raw FASTQs requires choosing the right counting pipeline, chemistry, and reference, and it is easy to conflate upstream counting with downstream QC and clustering. This Skill separates FASTQ-to-count routing from post-count analysis so each stage uses the right workflow.

Core Features & Use Cases

  • FASTQ-to-Count Routing: Directs raw reads to public pipelines such as nf-core/scrnaseq, STARsolo, kallisto-bustools, or alevin-fry, reserving Cell Ranger for explicit vendor-standard requests.
  • Structured Three-Stage Plan: Organizes work into count generation, post-count QC/annotation, and downstream differential statistics as sequential roadmap rows.
  • Delegation to scrna-seq-qc: Routes matrix, .h5, .h5ad, .rds, or Cell Ranger outputs to the embedded scrna-seq-qc skill for QC, doublets, ambient RNA, clustering, and UMAPs.
  • Use Case: A researcher with 10x-style FASTQs from multiple donors asks for a count matrix; the Skill confirms chemistry and reference, runs an nf-core preflight, and hands the resulting matrix to the QC skill.

Quick Start

Ask the assistant to inspect your single-cell FASTQ inputs, resolve chemistry and reference needs, and choose a public FASTQ-to-count pipeline.

Frequently Asked Questions about ngs-scrna-seq

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

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

Use a public pipeline such as nf-core/scrnaseq, STARsolo, kallisto-bustools via kb-python, or alevin-fry. Confirm the chemistry, barcode/UMI layout, organism, and reference genome first, then run a preflight before executing the pipeline with a container or HPC profile.

What is the difference between STARsolo, kallisto-bustools, and alevin-fry for single-cell counting?▼

All three are public FASTQ-to-count alternatives supported by this workflow. STARsolo performs alignment-based counting, while kallisto-bustools and alevin-fry use pseudoalignment approaches. Cell Ranger is reserved for cases requiring vendor-standard 10x output under its EULA.

When should I use Cell Ranger instead of nf-core/scrnaseq?▼

Use Cell Ranger only when vendor-standard 10x output is explicitly required and the user has accepted the 10x EULA. It is an optional backend, not the default execution target, and public pipelines are preferred otherwise.

Can this workflow handle .h5ad or .rds files directly?▼

Yes, but matrix-level inputs like .h5, .h5ad, .rds, or Cell Ranger outputs are routed to the embedded scrna-seq-qc skill. That skill handles QC metrics, doublets, ambient RNA, annotation, clustering, and UMAPs while preserving raw counts.

Why should I not assume 10x chemistry from FASTQ filenames?▼

Filenames are unreliable indicators of chemistry or barcode/UMI layout, and guessing can produce incorrect whitelist and counting choices. The workflow requires explicitly confirming chemistry and barcode structure before selecting a counting backend.

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