How do I install the Genomas Guide skill?

Run npx skillmds@latest add brycewang-stanford/genomas-guide in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Genomas Guide skill do?

Automate gene expression analysis with the GenoMAS multi-agent system It is listed under AI & ML on SkillMD.

Is Genomas Guide safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Genomas Guide?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Genomas Guide free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Genomas Guide?

brycewang-stanford (@brycewang-stanford) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Genomas Guide by brycewang-stanford

Name: Genomas Guide
Rating: 4.2 (5 reviews)
Author: brycewang-stanford

File contents

GenoMAS Guide

Overview

GenoMAS (Genomics Multi-Agent System) is a minimalist multi-agent framework for automating scientific analysis workflows, particularly gene expression analysis. It orchestrates specialized agents for data retrieval, preprocessing, differential expression analysis, pathway enrichment, and visualization — turning a natural language research question into a complete bioinformatics pipeline.

Installation

pip install genomas
# Or from source
git clone https://github.com/futianfan/GenoMAS.git
cd GenoMAS && pip install -e .

Core Workflow

Natural Language to Pipeline

from genomas import GenoMAS

geno = GenoMAS(llm_provider="anthropic")

# Describe analysis in natural language
result = geno.analyze(
    "Compare gene expression between tumor and normal tissue "
    "in the TCGA breast cancer dataset. Identify differentially "
    "expressed genes and run pathway enrichment analysis."
)

# GenoMAS automatically:
# 1. Retrieves TCGA-BRCA data via GDC API
# 2. Normalizes and filters expression data
# 3. Runs DESeq2-style differential expression
# 4. Performs GO and KEGG pathway enrichment
# 5. Generates volcano plots and heatmaps

Agent Roles

Agent	Responsibility
Data Agent	Retrieves datasets from GEO, TCGA, ArrayExpress
Preprocessing Agent	Quality control, normalization, filtering
Analysis Agent	Differential expression, clustering, PCA
Enrichment Agent	GO, KEGG, MSigDB pathway analysis
Visualization Agent	Plots, heatmaps, volcano plots
Report Agent	Generates methods section and results summary

Step-by-Step Usage

from genomas import DataAgent, AnalysisAgent, EnrichmentAgent

# Step 1: Retrieve data
data_agent = DataAgent()
dataset = data_agent.fetch("GSE12345", platform="RNA-seq")

# Step 2: Differential expression
analysis = AnalysisAgent()
de_results = analysis.differential_expression(
    dataset,
    group_col="condition",
    case="tumor",
    control="normal",
    method="deseq2",
)

# Step 3: Filter significant genes
sig_genes = de_results[
    (de_results["padj"] < 0.05) &
    (abs(de_results["log2FoldChange"]) > 1)
]
print(f"Found {len(sig_genes)} differentially expressed genes")

# Step 4: Pathway enrichment
enrichment = EnrichmentAgent()
pathways = enrichment.run(
    gene_list=sig_genes["gene_symbol"].tolist(),
    databases=["GO_BP", "KEGG", "Reactome"],
)

# Step 5: Visualize
from genomas.viz import volcano_plot, pathway_barplot
volcano_plot(de_results, output="volcano.png")
pathway_barplot(pathways, top_n=20, output="pathways.png")

Supported Analyses

Analysis	Method
Differential expression	DESeq2, edgeR, limma-voom
Clustering	Hierarchical, k-means, UMAP
PCA	Principal component analysis
GO enrichment	Gene Ontology term enrichment
KEGG pathway	KEGG pathway mapping
GSEA	Gene Set Enrichment Analysis
Survival analysis	Kaplan-Meier, Cox regression

Data Sources

Source	Data type
GEO (NCBI)	Microarray, RNA-seq
TCGA	Cancer genomics
GTEx	Normal tissue expression
ArrayExpress	European expression data

References

GenoMAS GitHub
Love, M.I. et al. (2014). "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome Biology 15(12).

---name: genomas-guidedescription: Automate gene expression analysis with the GenoMAS multi-agent system---# GenoMAS Guide## OverviewGenoMAS (Genomics Multi-Agent System) is a minimalist multi-agent framework for automating scientific analysis workflows, particularly gene expression analysis. It orchestrates specialized agents for data retrieval, preprocessing, differential expression analysis, pathway enrichment, and visualization — turning a natural language research question into a complete bioinformatics pipeline.## Installation```bashpip install genomas# Or from sourcegit clone https://github.com/futianfan/GenoMAS.gitcd GenoMAS && pip install -e .```## Core Workflow### Natural Language to Pipeline```pythonfrom genomas import GenoMASgeno = GenoMAS(llm_provider="anthropic")# Describe analysis in natural languageresult = geno.analyze( "Compare gene expression between tumor and normal tissue " "in the TCGA breast cancer dataset. Identify differentially " "expressed genes and run pathway enrichment analysis.")# GenoMAS automatically:# 1. Retrieves TCGA-BRCA data via GDC API# 2. Normalizes and filters expression data# 3. Runs DESeq2-style differential expression# 4. Performs GO and KEGG pathway enrichment# 5. Generates volcano plots and heatmaps```### Agent Roles| Agent | Responsibility ||-------|---------------|| **Data Agent** | Retrieves datasets from GEO, TCGA, ArrayExpress || **Preprocessing Agent** | Quality control, normalization, filtering || **Analysis Agent** | Differential expression, clustering, PCA || **Enrichment Agent** | GO, KEGG, MSigDB pathway analysis || **Visualization Agent** | Plots, heatmaps, volcano plots || **Report Agent** | Generates methods section and results summary |### Step-by-Step Usage```pythonfrom genomas import DataAgent, AnalysisAgent, EnrichmentAgent# Step 1: Retrieve datadata_agent = DataAgent()dataset = data_agent.fetch("GSE12345", platform="RNA-seq")# Step 2: Differential expressionanalysis = AnalysisAgent()de_results = analysis.differential_expression( dataset, group_col="condition", case="tumor", control="normal", method="deseq2",)# Step 3: Filter significant genessig_genes = de_results[ (de_results["padj"] < 0.05) & (abs(de_results["log2FoldChange"]) > 1)]print(f"Found {len(sig_genes)} differentially expressed genes")# Step 4: Pathway enrichmentenrichment = EnrichmentAgent()pathways = enrichment.run( gene_list=sig_genes["gene_symbol"].tolist(), databases=["GO_BP", "KEGG", "Reactome"],)# Step 5: Visualizefrom genomas.viz import volcano_plot, pathway_barplotvolcano_plot(de_results, output="volcano.png")pathway_barplot(pathways, top_n=20, output="pathways.png")```## Supported Analyses| Analysis | Method ||----------|--------|| Differential expression | DESeq2, edgeR, limma-voom || Clustering | Hierarchical, k-means, UMAP || PCA | Principal component analysis || GO enrichment | Gene Ontology term enrichment || KEGG pathway | KEGG pathway mapping || GSEA | Gene Set Enrichment Analysis || Survival analysis | Kaplan-Meier, Cox regression |## Data Sources| Source | Data type ||--------|-----------|| GEO (NCBI) | Microarray, RNA-seq || TCGA | Cancer genomics || GTEx | Normal tissue expression || ArrayExpress | European expression data |## References- [GenoMAS GitHub](https://github.com/futianfan/GenoMAS)- Love, M.I. et al. (2014). "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." *Genome Biology* 15(12).

Frequently asked questions

Genomas Guide