genomas-guide

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")