Metagenomics & Microbiome Analysis

SkillDev tools

Microbiome and metagenomics analysis using MGnify, GTDB taxonomy, ENA sequencing data, and EuropePMC literature. Covers taxonomic classification, genome quality assessment, biome-clinical phenotype linkage, and pathway interpretation. Use for amplicon/shotgun metagenomics study analysis.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Metagenomics & Microbiome Analysis skill

What this skill tells your AI

The instructions your AI receives, as published by mims-harvard/tooluniverse in skills/tooluniverse-metagenomics-analysis/SKILL.md and read by ahel’s review.

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

DatabaseBest For
MGnifyProcessed metagenomics studies, taxonomic/functional results
GTDBStandardized bacterial/archaeal taxonomy, species-level resolution
GMrepoGut species-to-human-health phenotype associations
ENARaw sequencing datasets and study metadata
KEGGPathway mapping for microbial functional annotations
PubMed/EuropePMCPublished microbiome-disease studies
CTDChemical-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 CategoryKey KEGG PathwaysSignificance
SCFA productionmap00650, map00640Gut barrier, anti-inflammatory
LPS biosynthesismap00540Pro-inflammatory, endotoxemia
Bile acid metabolismmap00120Fat absorption, FXR signaling
Tryptophan metabolismmap00380Serotonin, AhR, immune
Vitamin biosynthesismap00730/740/760Host 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

GitHub stars
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Forks
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Last commit
Sep 2026
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Catalog kind
skill
Gateway key
tooluniverse-metagenomics-analysis
Source
github.com/mims-harvard/tooluniverse