bulk-rna-seq-differential-expression-with-omicverse

Name: bulk-rna-seq-differential-expression-with-omicverse
Availability: InStock
Author: omicverse

Analyze bulk RNA-seq count matrices for differential expression and pathway enrichment.

32|5|Updated Mar 12, 2026

Search Tags:#bioinformatics #differential-expression #id-mapping #deseq2 #volcano-plot #bulk-rna-seq #geneset-enrichment

Ready to run

npx skills add https://github.com/omicverse/omicclaw --skill bulk-rna-seq-differential-expression-with-omicverse-omicverse

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bulk-rna-seq-differential-expression-with-omicverse
Source: https://github.com/omicverse/omicclaw/tree/main/src/omicverse_skills/skills/bulk-deg-analysis
Command: npx skills add https://github.com/omicverse/omicclaw --skill bulk-rna-seq-differential-expression-with-omicverse-omicverse

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a reproducible, guided workflow to convert raw gene-level counts into interpreted differential expression results, removing the manual friction of ID mapping, normalization, statistical testing, visualization, and pathway enrichment for bulk RNA-seq cohorts.

Core Features & Use Cases

Gene ID mapping: Convert Ensembl or other gene identifiers to gene symbols with downloadable mapping pairs and custom GTF-derived mappings.
Normalization and testing: DESeq2-style size factor normalization and multiple statistical testing options including Welch t-test, edgeR-like testing, and limma-style modeling.
Visualization: Volcano plots, per-gene boxplots, and enriched pathway visualizations for rapid result interpretation.
Pathway enrichment: Downloadable pathway databases, geneset preparation for multiple organisms, and plotting utilities for single and combined ontologies.
Validation and export: Basic defensive checks for group membership, duplicate handling, filtering recommendations, and guidance to export results and figures for reporting.

Quick Start

Run a DESeq2-style differential expression analysis on my featureCounts matrix comparing treated and control sample groups and generate volcano and pathway enrichment plots.

Frequently Asked Questions about bulk-rna-seq-differential-expression-with-omicverse

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

FAQPage Schema

How do I perform bulk RNA-seq differential expression analysis from featureCounts matrices?▼

Bulk RNA-seq differential expression analysis processes featureCounts-style gene-level count matrices to identify and rank differentially expressed genes. The workflow includes ID mapping, normalization, statistical testing, visualization, and pathway enrichment.

Can I convert Ensembl gene identifiers to gene symbols during bulk RNA-seq differential analysis?▼

Yes, gene ID mapping converts Ensembl or other gene identifiers to gene symbols during the differential expression workflow. It supports downloadable mapping pairs and custom GTF-derived mappings for accurate identifier conversion.

What statistical methods are available for bulk RNA-seq differential expression testing?▼

Bulk RNA-seq differential expression testing supports Welch t-test, edgeR-like testing, and limma-style modeling. It applies DESeq2-style size factor normalization prior to statistical testing to ensure accurate gene ranking.

How do I generate volcano plots and pathway enrichment visualizations for bulk RNA-seq results?▼

Volcano plots, per-gene boxplots, and enriched pathway visualizations are generated automatically after differential expression testing. The workflow also prepares genesets for multiple organisms and plots single and combined ontologies.

Does this bulk RNA-seq workflow support treatment versus control cohort comparisons?▼

Yes, the workflow is designed for treatment versus control cohort comparisons on bulk RNA-seq data. It includes defensive checks for group membership, duplicate handling, and filtering recommendations to validate cohort assignments.

What are the limitations of using DESeq2-style normalization for bulk RNA-seq differential expression?▼

DESeq2-style size factor normalization is suited for gene-level count matrices from bulk RNA-seq experiments. It requires featureCounts-style inputs and performs best with validated treatment versus control group memberships and proper duplicate handling.