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dr-cook:bioinformatics-assistant

Guide researchers through omics analysis pipelines for DEG, enrichment, and network pharmacology.

7|1|Updated Mar 9, 2026
Search Tags:#bioinformatics#omics#enrichment#rna-seq#deg#network-pharmacology
One-click install
npx skills add https://github.com/wen-chen/dr-cook --skill dr-cook-bioinformatics-assistant
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dr-cook:bioinformatics-assistant
Source: https://github.com/wen-chen/dr-cook/tree/main/modules/analysis/bioinformatics-assistant
Command: npx skills add https://github.com/wen-chen/dr-cook --skill dr-cook-bioinformatics-assistant

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Computational omics analysis often requires coordinating multiple steps across DEG, enrichment, and network pharmacology. This Skill provides structured, reproducible workflows with tool guidance, parameter defaults, and interpretation help.

Core Features & Use Cases

  • Guided differential expression analysis (DEG) with DEG track options (DESeq2, edgeR, limma) and downstream enrichment and hub-gene exploration.
  • Enrichment analysis support for GO and KEGG (ORA and GSEA) with ranked and unranked inputs.
  • Full network pharmacology pipeline tailored to TCM contexts, including target prediction, disease targets, intersection, PPI construction, and hub identification.
  • Reproducible workflows with R/Python templates, QC checklists, and actionable interpretation outputs.

Quick Start

Load your DEG results and follow the guided steps to perform differential expression analysis, enrichment, or network pharmacology in an end-to-end workflow.

Frequently Asked Questions about dr-cook:bioinformatics-assistant

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

FAQPage Schema
How do I run a complete RNA-seq differential expression analysis pipeline from raw counts to hub-gene exploration?▼

RNA-seq differential expression analysis pipelines are guided step-by-step through DEG track options like DESeq2, edgeR, and limma, including context-aware parameter selection, robust QC, and downstream hub-gene exploration.

What's the best way to perform GO and KEGG enrichment analysis on ranked and unranked gene lists?▼

Enrichment analysis for GO and KEGG supports both ORA for unranked inputs and GSEA for ranked inputs, providing structured workflows with established code templates and actionable interpretation outputs.

Can I use this workflow for microarray and proteomics data, or is it limited to RNA-seq?▼

The omics analysis workflows apply to RNA-seq, microarray, and proteomics contexts, offering structured guidance and reproducible R/Python templates across DEG, enrichment, and network pharmacology tracks.

How do I build a network pharmacology pipeline for Traditional Chinese Medicine (TCM) target prediction and PPI construction?▼

Network pharmacology pipelines tailored to TCM contexts are fully supported, guiding target prediction, disease target identification, intersection, PPI construction, and hub gene identification with reproducible templates.

Does the enrichment analysis workflow provide error diagnostics and reproducible code templates for R and Python?▼

Reproducible workflows are ensured via established R/Python code templates, library recommendations, QC checklists, and clear error diagnostics across all DEG, enrichment, and network pharmacology analysis steps.

How do I choose between DESeq2, edgeR, and limma for my differential expression analysis?▼

Differential expression analysis provides DEG track options including DESeq2, edgeR, and limma with context-aware parameter selection and robust QC to match your specific omics data context and analysis requirements.

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