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bio-workflows-rnaseq-to-de

Automate RNA-seq analysis from FASTQ to differential expression results.

Updated Aug 27, 2026
Search Tags:#workflow#bioinformatics#differential-expression#star#deseq2#rnaseq#salmon
One-click install
npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-workflows-rnaseq-to-de-stellaromics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-workflows-rnaseq-to-de
Source: https://github.com/stellaromics/fast-bioinfo/tree/main/.claude/agents/spatial-analysis/skills/bio-workflows-rnaseq-to-de
Command: npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-workflows-rnaseq-to-de-stellaromics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This workflow automates an end-to-end RNA-seq analysis pipeline starting from raw FASTQ data to differential expression results, enabling researchers to obtain DE genes and informative plots with minimal manual orchestration.

Core Features & Use Cases

  • QC and trimming with fastp
  • Quantification with Salmon or STAR+featureCounts
  • Import and normalization with tximport
  • Differential expression analysis with DESeq2
  • Visualization of results (volcano plots, MA plots, heatmaps)
  • Use Case: from raw reads to a prioritized list of DE genes for downstream interpretation

Quick Start

Run the full RNA-seq workflow on my FASTQ data to generate differential expression results and plots.

Frequently Asked Questions about bio-workflows-rnaseq-to-de

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

FAQPage Schema
How do I automate an RNA-seq differential expression workflow from FASTQ files?▼

This workflow automates RNA-seq analysis from raw FASTQ data to differential expression results by handling QC, quantification, normalization, DESeq2 analysis, and visualization to yield prioritized DE genes.

Can I use Salmon or STAR for transcript quantification in this RNA-seq pipeline?▼

Yes, this RNA-seq pipeline supports transcript quantification using either Salmon or STAR combined with featureCounts, providing flexibility depending on your alignment preferences.

How does the workflow handle RNA-seq quality control and read trimming?▼

The workflow handles RNA-seq quality control and read trimming using fastp, which prepares raw FASTQ data for downstream quantification and ensures data integrity through defined QC checkpoints.

What is the best way to generate volcano plots and heatmaps from DESeq2 results?▼

The best way to generate volcano plots and heatmaps from DESeq2 results is using this end-to-end workflow, which automatically produces these visualizations alongside MA plots after differential expression analysis.

Do I need tximport to normalize RNA-seq data before running DESeq2?▼

Yes, this workflow uses tximport to import and normalize RNA-seq quantification data from Salmon or STAR before running DESeq2, ensuring transcript-level counts are properly aggregated for differential expression analysis.

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