genomas-guide

name	genomas-guide
description	Automate gene expression analysis with the GenoMAS multi-agent system
metadata	{"openclaw":{"emoji":"🧬","category":"domains","subcategory":"biomedical","keywords":["GenoMAS","gene expression","multi-agent","bioinformatics","RNA-seq","genomics automation"],"source":"wentor-research-plugins"}}

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).