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bulk-rnaseq

Automate bulk RNA-seq analysis from quality control to pathway enrichment.

Updated May 17, 2026
Search Tags:#bioinformatics#pathway enrichment#differential expression#RNA-seq#bulk analysis
One-click install
npx skills add https://github.com/galeep/plugin-place --skill bulk-rnaseq-galeep
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bulk-rnaseq
Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-bioinformatics-genomics/skills/bulk-rnaseq
Command: npx skills add https://github.com/galeep/plugin-place --skill bulk-rnaseq-galeep

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires STAR, Salmon, featureCounts, pydeseq2, pathway-enrichment, scientific-visualization, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the entire bulk RNA-seq analysis workflow, from quality control and alignment to differential expression and pathway enrichment, providing a reproducible and quality-gated pipeline.

Core Features & Use Cases

  • End-to-End Workflow: Orchestrates all steps from raw reads to publication figures.
  • Quality-Gated: Ensures reproducibility, quality control, and statistical soundness.
  • Use Case: Ideal for researchers who need to analyze bulk RNA-seq data and generate publication-ready figures and tables.

Quick Start

Run the bulk-rnaseq skill with the following command: bulk-rnaseq run --samplesheet samplesheet.csv --genome GRCh38 --aligner star_salmon --outdir results

Frequently Asked Questions about bulk-rnaseq

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

FAQPage Schema
How do I automate bulk RNA-seq analysis from raw reads to differential expression?▼

You can automate bulk RNA-seq analysis by running the bulk-rnaseq workflow with a samplesheet, genome reference, and aligner choice. It orchestrates quality control, alignment, quantification, differential expression, and pathway enrichment into a reproducible pipeline.

What is the best way to perform pathway enrichment after differential expression analysis?▼

Pathway enrichment following differential expression is handled automatically within the bulk RNA-seq workflow. The pipeline integrates statistical analysis and pathway enrichment to generate publication-ready figures and tables from your quantification results.

Do I need STAR and Salmon installed to run bulk RNA-seq alignment and quantification?▼

Yes, STAR and Salmon are required dependencies for the bulk RNA-seq alignment and quantification steps. The workflow uses these bioinformatics tools along with featureCounts and pydeseq2 to process high-throughput RNA-seq experiments.

Can I use pydeseq2 for differential expression in a high-throughput RNA-seq workflow?▼

Yes, pydeseq2 is integrated into the bulk RNA-seq workflow to perform differential expression analysis. It works alongside STAR, Salmon, and featureCounts to provide a statistically sound, quality-gated pipeline for high-throughput experiments.

Does this bulk RNA-seq pipeline support publication-ready figures and reproducible quality control?▼

Yes, the bulk RNA-seq pipeline is quality-gated and designed to produce publication-ready figures and tables. It enforces reproducibility and quality control measures across all steps from raw read alignment to pathway enrichment.

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