General

tooluniverse-metagenomics-analysis - Claude MCP Skill

Analyze microbiome and metagenomics data using MGnify, GTDB, ENA, and literature tools. Search studies by biome/keyword, retrieve taxonomic profiles and functional annotations, classify genomes with GTDB taxonomy, and find related publications. Use for human gut microbiome, soil/ocean metagenomics, and environmental microbiology research.

SEO Guide: Enhance your AI agent with the tooluniverse-metagenomics-analysis tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to analyze microbiome and metagenomics data using mgnify, gtdb, ena, and literature tools. search studi... Download and configure this skill to unlock new capabilities for your AI workflow.

🌟11 stars • 205 forks
📥0 downloads

Documentation

SKILL.md
# Metagenomics & Microbiome Analysis

Integrated pipeline for exploring microbiome studies, classifying taxa, assessing genome quality, linking microbial composition to clinical phenotypes, and interpreting findings through pathway analysis and literature context.

**Guiding principles**:
1. **Study context first** -- understand biome, sequencing method, and metadata before diving into taxa
2. **Taxonomic consistency** -- GTDB taxonomy as reference standard; reconcile NCBI where needed
3. **Genome quality matters** -- CheckM completeness/contamination thresholds determine trustworthy MAGs
4. **Interpretation over enumeration** -- explain what taxa mean for the biological question
5. **English-first queries** -- use English terms in tool calls

## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.

---

## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

## Core Databases

| Database | Best For |
|----------|---------|
| **MGnify** | Processed metagenomics studies, taxonomic/functional results |
| **GTDB** | Standardized bacterial/archaeal taxonomy, species-level resolution |
| **GMrepo** | Gut species-to-human-health phenotype associations |
| **ENA** | Raw sequencing datasets and study metadata |
| **KEGG** | Pathway mapping for microbial functional annotations |
| **PubMed/EuropePMC** | Published microbiome-disease studies |
| **CTD** | Chemical-microbiome-disease relationships |

---

## Workflow

```
Phase 0: Parse query → organism, biome, phenotype, or accession
Phase 1: Study Discovery → MGnify_search_studies, ENAPortal_search_studies
Phase 2: Taxonomic Classification → GTDB_search_genomes, GTDB_get_species, GTDB_search_taxon
Phase 3: Genome Quality → MGnify_search_genomes, MGnify_get_genome (CheckM metrics)
Phase 4: Functional Annotation → MGnify GO terms + KEGG pathway mapping
Phase 5: Clinical Associations → GMrepo species-phenotype links
Phase 6: Literature → PubMed/EuropePMC + CTD gene-disease
Phase 7: Interpretation & Report Synthesis
```

---

## Key Phase Notes

**Phase 1**: ENA requires structured queries (e.g., `study_title="*IBD*"`), not free text. If ENA fails, fall back to MGnify.

**Phase 2**: GTDB uses its own naming (e.g., `s__Bacteroides_A fragilis` vs NCBI `Bacteroides fragilis`). Always note discrepancies. Use `GTDB_search_taxon(operation="search_taxon", query=name)`.

**Phase 3 - Quality tiers** (MIMAG):
- **High**: >= 90% complete, <= 5% contamination, rRNA + >= 18 tRNAs
- **Medium**: >= 50% complete, <= 10% contamination
- **Low**: below medium -- flag but don't exclude

**Phase 4 - Functional interpretation**: Don't just list GO terms. Connect to biology:

| Functional Category | Key KEGG Pathways | Significance |
|---|---|---|
| SCFA production | map00650, map00640 | Gut barrier, anti-inflammatory |
| LPS biosynthesis | map00540 | Pro-inflammatory, endotoxemia |
| Bile acid metabolism | map00120 | Fat absorption, FXR signaling |
| Tryptophan metabolism | map00380 | Serotonin, AhR, immune |
| Vitamin biosynthesis | map00730/740/760 | Host nutritional contribution |

Use `kegg_search_pathway(keyword=...)` (NOT `query`). Pathway IDs need organism prefix (`hsa`, `ko`, `eco`), NOT bare `map`.

**Phase 5**: GMrepo uses MeSH terms: "Crohn Disease" not "IBD", "Colitis, Ulcerative" not "UC", "Colorectal Neoplasms" not "colorectal cancer". Try NCBI taxon IDs if species name fails.

**Phase 6 - Evidence grading**:
- **Strong**: Meta-analysis or >5 studies, consistent direction
- **Moderate**: 2-5 studies consistent, or 1 large cohort
- **Preliminary**: Single study or conflicting
- **Mechanistic only**: In vitro/animal, no human epidemiology

**Phase 7 - Report**: Executive summary, study landscape, GTDB taxonomy, functional interpretation (not GO term lists), clinical relevance with evidence grades, mechanistic model, genome catalog with quality tiers, data gaps.

---

## Edge Cases & Fallbacks

- **Taxon not in GTDB**: Try partial search or fall back to MGnify (NCBI taxonomy)
- **No GMrepo data**: Normal for non-gut organisms; use literature
- **GMrepo 0 results**: Use formal MeSH terms or NCBI taxon IDs
- **No KEGG match**: Check MetaCyc or literature

## Limitations

- **GMrepo**: Gut-only
- **GTDB**: Bacteria/Archaea only
- **ENA**: Raw data only, strict query syntax
- **No sequence analysis**: Queries databases, not raw FASTQ/FASTA

Signals

Avg rating0.0
Reviews0
Favorites0

Information

Repository
mims-harvard/ToolUniverse
Author
mims-harvard
Last Sync
5/10/2026
Repo Updated
5/10/2026
Created
3/26/2026

Reviews (0)

No reviews yet. Be the first to review this skill!

Related Skills

cursorrules

CrewAI Development Rules

43932Has guide

fastmcp-client-cli

Query and invoke tools on MCP servers using fastmcp list and fastmcp call. Use when you need to discover what tools a server offers, call tools, or integrate MCP servers into workflows.

25095

open-source

Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browser_use, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle hooks, MCP server setup, or monitoring/observability with Laminar or OpenLIT. Also trigger for questions about browser-use installation, prompting strategies, or sensitive data handling. Do NOT use this for Cloud API/SDK usage or pricing — use the cloud skill instead. Do NOT use this for directly automating a browser via CLI commands — use the browser-use skill instead.

23311

cloud

Documentation reference for using Browser Use Cloud — the hosted API and SDK for browser automation. Use this skill whenever the user needs help with the Cloud REST API (v2 or v3), browser-use-sdk (Python or TypeScript), X-Browser-Use-API-Key authentication, cloud sessions, browser profiles, profile sync, CDP WebSocket connections, stealth browsers, residential proxies, CAPTCHA handling, webhooks, workspaces, skills marketplace, liveUrl streaming, pricing, or integration patterns (chat UI, subagent, adding browser tools to existing agents). Also trigger for questions about n8n/Make/Zapier integration, Playwright/ Puppeteer/Selenium on cloud infrastructure, or 1Password vault integration. Do NOT use this for the open-source Python library (Agent, Browser, Tools config) — use the open-source skill instead.

23311

Related Guides