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bio-workflows-rnaseq-to-de

Runs an end-to-end RNA-seq workflow from raw FASTQ files to differential expression results via picocomp.

Updated Mar 30, 2026
Search Tags:#quality-control#differential-expression#rna-seq#deseq2#rnaseq#tximport
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-workflows-rnaseq-to-de-ya-way
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-workflows-rnaseq-to-de
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/bio-wf-rnaseq-to-de
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-workflows-rnaseq-to-de-ya-way

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates end-to-end RNA-seq analysis from raw FASTQ files to differential expression results, reducing manual setup and ensuring reproducibility.

Core Features & Use Cases

  • QC with fastp to ensure reads pass quality thresholds and adapter removal.
  • Quantification via Salmon (alignment-free) or STAR + featureCounts for alternative pipelines.
  • Import + DE analysis with tximport and DESeq2, with visualization (volcano, heatmaps) and export of significant genes.
  • Use Case: A project with six samples (3 control, 3 treated) can be processed from FASTQ through DE results with one reproducible workflow.

Quick Start

Run the end-to-end RNA-seq workflow on FASTQ data to produce differential expression results.

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 run an end-to-end RNA-seq workflow from FASTQ to differential expression?▼

You can run an end-to-end RNA-seq workflow that processes raw FASTQ files through QC, quantification, and DE analysis. It automates setup with validated tool versions and clear checkpoints to produce robust differential expression results.

What is the best way to automate RNA-seq differential expression analysis with DESeq2 and tximport?▼

Automating RNA-seq differential expression with DESeq2 and tximport is handled by a workflow that imports quantification data and runs DE analysis. It exports significant genes and generates visualizations like volcano plots and heatmaps.

Can I use Salmon or STAR for quantification in an RNA-seq differential expression pipeline?▼

Yes, you can use Salmon for alignment-free quantification or STAR with featureCounts in your RNA-seq pipeline. The workflow supports both pipelines to generate quantification data for downstream tximport and DESeq2 analysis.

Does the RNA-seq workflow include quality control for adapter removal and read thresholds?▼

Yes, the RNA-seq workflow includes quality control using fastp to ensure reads pass quality thresholds and adapter removal. This QC step validates the raw FASTQ data before proceeding to quantification and differential expression analysis.

How many samples do I need for DESeq2 differential expression analysis in this RNA-seq workflow?▼

The workflow processes DESeq2 differential expression analysis for multiple samples, such as a project with six samples comprising three control and three treated. It uses these groups to process FASTQ data through to DE results reproducibly.

Why does my RNA-seq workflow need tximport before running DESeq2?▼

Your RNA-seq workflow needs tximport to import and summarize transcript-level quantification estimates from Salmon or featureCounts into a format compatible with DESeq2. This step bridges quantification and differential expression analysis.

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