Microbiome research using MGnify, GTDB, ENA, OLS (ENVO biomes), and EuropePMC. Covers study discovery, taxonomic profiling, host-microbe interaction analysis, and biome-by-condition queries. Use for microbiome study selection, organism-environment associations, and clinical-microbiome literature review. Distinct from analytical workflow (use tooluniverse-metagenomics-analysis for that).
Microbiome research using MGnify, GTDB, ENA, OLS (ENVO biomes), and EuropePMC. Covers study discovery, taxonomic profiling, host-microbe interaction analysis, and biome-by-condition queries. Use for microbiome study selection, organism-environment associations, and clinical-microbiome literature review. Distinct from analytical workflow (use tooluniverse-metagenomics-analysis for that).
Microbiome Research with ToolUniverse
Comprehensive microbiome analysis using MGnify (EBI metagenomics), GTDB (genome taxonomy), ENA (sequencing data), OLS (ontology lookup for ENVO biomes), and EuropePMC (literature).
Core Tools
Tool
Purpose
Auth
MGnify_search_studies
Find metagenomics studies by biome/keyword
None
MGnify_get_study_detail
Study metadata, abstract, sample counts
None
MGnify_list_analyses
List taxonomic/functional analysis outputs for a study
Get the microbial composition of a metagenomics sample:
# Get analyses for a study
analyses = tu.run_one_function({
'name': 'MGnify_list_analyses',
'arguments': {'study_accession': 'MGYS00006860', 'size': 3}
})
# Get taxonomy for a specific analysis
taxonomy = tu.run_one_function({
'name': 'MGnify_get_taxonomy',
'arguments': {'analysis_accession': 'MGYA00612683'}
})
# Returns organisms with lineage, abundance counts, and taxonomy rank
Workflow 3: Genome Quality Assessment
Evaluate metagenome-assembled genomes (MAGs):
# Search for genomes from a specific taxon
genomes = tu.run_one_function({
'name': 'MGnify_search_genomes',
'arguments': {'search': 'Faecalibacterium prausnitzii', 'size': 5}
})
# Get quality metrics for a genome
genome = tu.run_one_function({
'name': 'MGnify_get_genome',
'arguments': {'genome_accession': 'MGYG000000001'}
})
# Returns completeness, contamination, N50, genome length, taxonomy# Cross-reference with GTDB taxonomy
gtdb = tu.run_one_function({
'name': 'GTDB_search_genomes',
'arguments': {'operation': 'search_genomes', 'query': 'Faecalibacterium', 'items_per_page': 5}
})
Workflow 4: Functional Annotation
Discover functional potential of a metagenome:
# GO terms from an analysis
go_terms = tu.run_one_function({
'name': 'MGnify_get_go_terms',
'arguments': {'analysis_accession': 'MGYA00612683'}
})
# InterPro domains
interpro = tu.run_one_function({
'name': 'MGnify_get_interpro',
'arguments': {'analysis_accession': 'MGYA00612683'}
})
Workflow 5: Literature Integration
Combine metagenomics data with published research:
# Find relevant publications
papers = tu.run_one_function({
'name': 'EuropePMC_search_articles',
'arguments': {'query': 'gut microbiome AND Faecalibacterium AND (IBD OR "Crohn")', 'limit': 10}
})
# Find sequencing data in ENA
ena_studies = tu.run_one_function({
'name': 'ENAPortal_search_studies',
'arguments': {'query': 'description="gut microbiome 16S"', 'limit': 5}
})
MGnify Biome Hierarchy
Key biome lineages (use MGnify_list_biomes to discover others):
Human gut: root:Host-associated:Human:Digestive system
Human oral/skin: root:Host-associated:Human:Oral / root:Host-associated:Human:Skin
Microbiome analysis starts with: what is the question? LOOK UP DON'T GUESS — always check the study type and sequencing method before interpreting results.
Decision tree for data type:
Community composition (who is there?) → 16S/ITS amplicon → alpha/beta diversity, differential abundance
Functional potential (what can they do?) → Shotgun metagenomics → MGnify GO terms, InterPro, KEGG pathways
Active function (what are they doing now?) → Metatranscriptomics → specialized pipelines (not MGnify/GTDB alone)
Before calling any tool, determine which data type the user has via MGnify_get_study_detail — the pipeline type (amplicon vs shotgun) determines which analyses are valid. Do not apply 16S diversity metrics to metagenomic data or vice versa.
Dysbiosis Assessment Strategy
Dysbiosis (microbial imbalance) is context-dependent — there is no universal "healthy" microbiome. LOOK UP DON'T GUESS — compare to study-matched controls, not general population references.
Check alpha diversity: Reduced Shannon index relative to controls suggests dysbiosis. Use MGnify_get_taxonomy to get community profiles, then assess richness and evenness.
Identify keystone taxa shifts: Loss of known beneficial taxa (e.g., Faecalibacterium, Roseburia in gut) or bloom of pathobionts (e.g., Enterobacteriaceae). LOOK UP taxa roles with GTDB_get_species and literature via EuropePMC_search_articles.
Functional consequences: Does taxonomic shift correlate with loss/gain of metabolic pathways? Check MGnify_get_go_terms and MGnify_get_interpro for the affected samples.
Confounders: Antibiotics, diet, age, and geography all affect microbiome composition. A dysbiosis claim requires controlling for these factors or acknowledging them as limitations.
Taxonomic vs Functional Analysis: When to Use Each
Taxonomic analysis alone is sufficient when the question is "which organisms are present?" or "does community composition differ between groups?" Use MGnify_get_taxonomy + GTDB_search_genomes.
Functional analysis is needed when the question is "what metabolic capabilities differ?" or "why does a taxonomic shift matter?" Use MGnify_get_go_terms + MGnify_get_interpro + kegg_search_pathway.
Both together when linking organisms to functions (e.g., "which taxa drive butyrate production in healthy vs IBD gut?"). Cross-reference taxonomic profiles with functional annotations from the same MGnify analysis.
Evidence Grading
Tier
Description
Example
T1
Replicated finding across multiple cohorts with consistent effect
Reduced Faecalibacterium in IBD (>10 independent studies)
T2
Single well-powered study (n > 100) with appropriate controls
Metformin-associated Akkermansia enrichment in a controlled trial
T3
Pilot study or observational association, small sample size
Taxonomic shift in n=15 case-control, no validation cohort
T4
Computational prediction or single-sample observation
Novel MAG with predicted function, no culture confirmation
Interpretation Guidance
Alpha diversity (within-sample): Shannon index measures richness and evenness. Higher Shannon (>3.0 for gut) suggests a stable community. Reduced alpha diversity is associated with dysbiosis (IBD, antibiotics). Always compare to study-matched controls — diversity varies by body site, sequencing depth, and geography.
Beta diversity (between-sample): Bray-Curtis (abundance-based) or UniFrac (phylogenetic). PERMANOVA p < 0.05 with R-squared > 0.05 indicates condition-driven clustering. Low R-squared (<0.02) even with significant p suggests the effect is small relative to inter-individual variation. Choose weighted UniFrac when abundant taxa matter most; unweighted when rare taxa are important.
Taxonomic composition: Relative abundance at phylum level (Firmicutes/Bacteroidetes ratio) is a coarse indicator; genus- or species-level resolution is preferred. A taxon present at >1% relative abundance in multiple samples is reliably detected. Taxa at <0.1% may be noise or sequencing artifacts. GTDB taxonomy may reclassify NCBI names (e.g., Firmicutes split into multiple phyla).
Functional profiling: GO terms and InterPro domains from MGnify reflect the metabolic potential (not necessarily activity) of the community. Enrichment of specific pathways (e.g., butyrate production, LPS biosynthesis) should be interpreted alongside taxonomic data to identify which organisms contribute the functions.
Synthesis Questions
A complete microbiome report should answer:
How does alpha diversity compare between conditions, and is the difference significant?
Does beta diversity analysis show condition-driven clustering (PERMANOVA)?
Which taxa are differentially abundant, and are they known commensals or pathobionts?
What functional pathways are enriched, and which taxa likely drive them?
How do findings compare to published studies for the same biome/condition (literature context)?
Tips
MGnify study accessions start with MGYS, analyses with MGYA, genomes with MGYG
Use MGnify_list_biomes first to find the correct biome lineage string
MGnify_get_taxonomy returns phylum-level to species-level composition
GTDB provides standardized bacterial/archaeal taxonomy (differs from NCBI in some lineages)
For 16S amplicon studies, taxonomy is the primary output; for shotgun metagenomics, both taxonomy and functional annotations are available
The size parameter in MGnify tools controls results per page (max 100)
