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Search studies by biome\u002Fkeyword, retrieve taxonomic profiles and functional annotations, classify genomes with GTDB taxonomy, and find related publications. Use for human gut microbiome, soil\u002Focean metagenomics, and environmental microbiology research.","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Fmicrobiome-research\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"research","microbiome research","search","data","Tools","taxonomy","analyze","annotations","find","use","human","microbiology","environmental","classify","gut","2026-11-10 10:12:30",false,"---\nname: microbiome-research\ndescription: Analyze microbiome and metagenomics data using MGnify, GTDB, ENA, and literature tools. Search studies by biome\u002Fkeyword, retrieve taxonomic profiles and functional annotations, classify genomes with GTDB taxonomy, and find related publications. Use for human gut microbiome, soil\u002Focean metagenomics, and environmental microbiology research.\n---\n\n# Microbiome Research with ToolUniverse\n\nComprehensive microbiome analysis using MGnify (EBI metagenomics), GTDB (genome taxonomy), ENA (sequencing data), OLS (ontology lookup for ENVO biomes), and EuropePMC (literature).\n\n## Core Tools\n\n| Tool | Purpose | Auth |\n|------|---------|------|\n| **MGnify_search_studies** | Find metagenomics studies by biome\u002Fkeyword | None |\n| **MGnify_search_studies_detail** | Study metadata, abstract, sample counts | None |\n| **MGnify_list_analyses** | List taxonomic\u002Ffunctional analysis outputs for a study | None |\n| **MGnify_get_taxonomy** | Taxonomic composition from an analysis | None |\n| **MGnify_get_go_terms** | GO functional annotations from an analysis | None |\n| **MGnify_get_interpro** | InterPro protein domain annotations | None |\n| **MGnify_list_biomes** | Browse MGnify biome hierarchy | None |\n| **MGnify_search_genomes** | Search metagenome-assembled genomes (MAGs) | None |\n| **MGnify_get_genome** | Genome quality metrics (completeness, contamination) | None |\n| **GTDB_search_genomes** | Search bacterial\u002Farchaeal genomes by taxonomy | None |\n| **GTDB_get_species** | Species cluster details from GTDB | None |\n| **GTDB_get_taxon_info** | Taxonomic rank info in GTDB hierarchy | None |\n| **GTDB_search_taxon** | Search taxa by partial name across all ranks | None |\n| **ENAPortal_search_studies** | Find sequencing studies in ENA. Query format: `description=\"keyword\"` | None |\n| **ENAPortal_search_samples** | Find samples with environmental metadata | None |\n| **ols_search_terms** | Search ENVO ontology for biome\u002Fenvironment terms | None |\n| **EuropePMC_search_articles** | Find microbiome publications | None |\n| **PubMed_search_articles** | Literature search (different coverage than EuropePMC) | None |\n\n**For drug-microbiome studies**, also use:\n- `PubChem_get_CID_by_compound_name` \u002F `PubChem_get_compound_properties_by_CID` — drug identity\n- `CTD_get_chemical_gene_interactions` — drug-gene interactions (e.g., metformin affects 1,175+ genes)\n- `kegg_search_pathway` \u002F `kegg_get_pathway_info` — microbial metabolic pathways (butanoate, propanoate)\n- `ReactomeAnalysis_pathway_enrichment` — host pathway enrichment for drug-affected genes\n- `drugbank_vocab_search` — drug mechanism and targets\n\n> **MGnify tip**: Use concise single-keyword searches (e.g., \"metformin\") — multi-word queries may timeout. The MGnify API can be slow for broad searches.\n\n## Quick Start\n\n```python\nfrom tooluniverse import ToolUniverse\n\ntu = ToolUniverse()\ntu.load_tools()\n\n# 1. Search for gut microbiome studies\nstudies = tu.run_one_function({\n    'name': 'MGnify_search_studies',\n    'arguments': {'search': 'gut microbiome', 'size': 5}\n})\n\n# 2. Get study details\ndetail = tu.run_one_function({\n    'name': 'MGnify_search_studies_detail',\n    'arguments': {'study_accession': 'MGYS00006860'}\n})\n\n# 3. List analyses for a study\nanalyses = tu.run_one_function({\n    'name': 'MGnify_list_analyses',\n    'arguments': {'study_accession': 'MGYS00006860', 'size': 5}\n})\n\n# 4. Get taxonomic profile from an analysis\ntaxonomy = tu.run_one_function({\n    'name': 'MGnify_get_taxonomy',\n    'arguments': {'analysis_accession': 'MGYA00612683'}\n})\n\n# 5. Get functional annotations\ngo_terms = tu.run_one_function({\n    'name': 'MGnify_get_go_terms',\n    'arguments': {'analysis_accession': 'MGYA00612683'}\n})\n```\n\n## Common Workflows\n\n### Workflow 1: Study Discovery by Environment\n\nFind studies for a specific biome using MGnify's biome hierarchy:\n\n```python\n# Browse biome hierarchy\nbiomes = tu.run_one_function({\n    'name': 'MGnify_list_biomes',\n    'arguments': {'lineage': 'root:Host-associated:Human', 'depth': 3}\n})\n\n# Search studies in a specific biome\nstudies = tu.run_one_function({\n    'name': 'MGnify_search_studies',\n    'arguments': {'biome': 'root:Host-associated:Human:Digestive system', 'size': 10}\n})\n\n# Look up ENVO ontology terms for environment metadata\nenvo = tu.run_one_function({\n    'name': 'ols_search_terms',\n    'arguments': {'query': 'human gut', 'ontology': 'envo', 'rows': 5}\n})\n```\n\n### Workflow 2: Taxonomic Profiling\n\nGet the microbial composition of a metagenomics sample:\n\n```python\n# Get analyses for a study\nanalyses = tu.run_one_function({\n    'name': 'MGnify_list_analyses',\n    'arguments': {'study_accession': 'MGYS00006860', 'size': 3}\n})\n\n# Get taxonomy for a specific analysis\ntaxonomy = tu.run_one_function({\n    'name': 'MGnify_get_taxonomy',\n    'arguments': {'analysis_accession': 'MGYA00612683'}\n})\n# Returns organisms with lineage, abundance counts, and taxonomy rank\n```\n\n### Workflow 3: Genome Quality Assessment\n\nEvaluate metagenome-assembled genomes (MAGs):\n\n```python\n# Search for genomes from a specific taxon\ngenomes = tu.run_one_function({\n    'name': 'MGnify_search_genomes',\n    'arguments': {'search': 'Faecalibacterium prausnitzii', 'size': 5}\n})\n\n# Get quality metrics for a genome\ngenome = tu.run_one_function({\n    'name': 'MGnify_get_genome',\n    'arguments': {'genome_accession': 'MGYG000000001'}\n})\n# Returns completeness, contamination, N50, genome length, taxonomy\n\n# Cross-reference with GTDB taxonomy\ngtdb = tu.run_one_function({\n    'name': 'GTDB_search_genomes',\n    'arguments': {'operation': 'search_genomes', 'query': 'Faecalibacterium', 'items_per_page': 5}\n})\n```\n\n### Workflow 4: Functional Annotation\n\nDiscover functional potential of a metagenome:\n\n```python\n# GO terms from an analysis\ngo_terms = tu.run_one_function({\n    'name': 'MGnify_get_go_terms',\n    'arguments': {'analysis_accession': 'MGYA00612683'}\n})\n\n# InterPro domains\ninterpro = tu.run_one_function({\n    'name': 'MGnify_get_interpro',\n    'arguments': {'analysis_accession': 'MGYA00612683'}\n})\n```\n\n### Workflow 5: Literature Integration\n\nCombine metagenomics data with published research:\n\n```python\n# Find relevant publications\npapers = tu.run_one_function({\n    'name': 'EuropePMC_search_articles',\n    'arguments': {'query': 'gut microbiome AND Faecalibacterium AND (IBD OR \"Crohn\")', 'limit': 10}\n})\n\n# Find sequencing data in ENA\nena_studies = tu.run_one_function({\n    'name': 'ENAPortal_search_studies',\n    'arguments': {'query': 'description=\"gut microbiome 16S\"', 'limit': 5}\n})\n```\n\n## MGnify Biome Hierarchy\n\nKey biome lineages (use `MGnify_list_biomes` to discover others):\n- Human gut: `root:Host-associated:Human:Digestive system`\n- Human oral\u002Fskin: `root:Host-associated:Human:Oral` \u002F `root:Host-associated:Human:Skin`\n- Soil: `root:Environmental:Terrestrial:Soil`\n- Ocean\u002FFreshwater: `root:Environmental:Aquatic:Marine` \u002F `root:Environmental:Aquatic:Freshwater`\n- Wastewater: `root:Engineered:Wastewater`\n\n## Key Identifiers\n\nMGnify: studies=`MGYS*`, analyses=`MGYA*`, genomes=`MGYG*`. ENA studies=`PRJEB*`. GTDB genomes=`GCA_*`. ENVO terms=`ENVO:*` (e.g. ENVO:00002041).\n\n## Reasoning Framework\n\n### Starting Point: Define the Question First\n\nMicrobiome analysis starts with: what is the question? LOOK UP DON'T GUESS — always check the study type and sequencing method before interpreting results.\n\n**Decision tree for data type:**\n- Community composition (who is there?) → 16S\u002FITS amplicon → alpha\u002Fbeta diversity, differential abundance\n- Functional potential (what can they do?) → Shotgun metagenomics → MGnify GO terms, InterPro, KEGG pathways\n- Active function (what are they doing now?) → Metatranscriptomics → specialized pipelines (not MGnify\u002FGTDB alone)\n\nBefore calling any tool, determine which data type the user has via `MGnify_search_studies_detail` — the pipeline type (amplicon vs shotgun) determines which analyses are valid. Do not apply 16S diversity metrics to metagenomic data or vice versa.\n\n### Dysbiosis Assessment Strategy\n\nDysbiosis (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.\n\n1. **Check alpha diversity**: Reduced Shannon index relative to controls suggests dysbiosis. Use `MGnify_get_taxonomy` to get community profiles, then assess richness and evenness.\n2. **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`.\n3. **Functional consequences**: Does taxonomic shift correlate with loss\u002Fgain of metabolic pathways? Check `MGnify_get_go_terms` and `MGnify_get_interpro` for the affected samples.\n4. **Confounders**: Antibiotics, diet, age, and geography all affect microbiome composition. A dysbiosis claim requires controlling for these factors or acknowledging them as limitations.\n\n### Taxonomic vs Functional Analysis: When to Use Each\n\n- **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`.\n- **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`.\n- **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.\n\n### Evidence Grading\n\n| Tier | Description | Example |\n|------|-------------|---------|\n| **T1** | Replicated finding across multiple cohorts with consistent effect | Reduced Faecalibacterium in IBD (>10 independent studies) |\n| **T2** | Single well-powered study (n > 100) with appropriate controls | Metformin-associated Akkermansia enrichment in a controlled trial |\n| **T3** | Pilot study or observational association, small sample size | Taxonomic shift in n=15 case-control, no validation cohort |\n| **T4** | Computational prediction or single-sample observation | Novel MAG with predicted function, no culture confirmation |\n\n### Interpretation Guidance\n\n**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.\n\n**Beta diversity (between-sample)**: Bray-Curtis (abundance-based) or UniFrac (phylogenetic). PERMANOVA p \u003C 0.05 with R-squared > 0.05 indicates condition-driven clustering. Low R-squared (\u003C0.02) 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.\n\n**Taxonomic composition**: Relative abundance at phylum level (Firmicutes\u002FBacteroidetes 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 \u003C0.1% may be noise or sequencing artifacts. GTDB taxonomy may reclassify NCBI names (e.g., Firmicutes split into multiple phyla).\n\n**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.\n\n### Synthesis Questions\n\nA complete microbiome report should answer:\n1. How does alpha diversity compare between conditions, and is the difference significant?\n2. Does beta diversity analysis show condition-driven clustering (PERMANOVA)?\n3. Which taxa are differentially abundant, and are they known commensals or pathobionts?\n4. What functional pathways are enriched, and which taxa likely drive them?\n5. How do findings compare to published studies for the same biome\u002Fcondition (literature context)?\n\n## Tips\n\n- MGnify study accessions start with `MGYS`, analyses with `MGYA`, genomes with `MGYG`\n- Use `MGnify_list_biomes` first to find the correct biome lineage string\n- `MGnify_get_taxonomy` returns phylum-level to species-level composition\n- GTDB provides standardized bacterial\u002Farchaeal taxonomy (differs from NCBI in some lineages)\n- For 16S amplicon studies, taxonomy is the primary output; for shotgun metagenomics, both taxonomy and functional annotations are available\n- The `size` parameter in MGnify tools controls results per page (max 100)\n","# Microbiome Research with ToolUniverse\n\nComprehensive microbiome analysis using MGnify (EBI metagenomics), GTDB (genome taxonomy), ENA (sequencing data), OLS (ontology lookup for ENVO biomes), and EuropePMC (literature).\n\n## Core Tools\n\n| Tool | Purpose | Auth |\n|------|---------|------|\n| **MGnify_search_studies** | Find metagenomics studies by biome\u002Fkeyword | None |\n| **MGnify_search_studies_detail** | Study metadata, abstract, sample counts | None |\n| **MGnify_list_analyses** | List taxonomic\u002Ffunctional analysis outputs for a study | None |\n| **MGnify_get_taxonomy** | Taxonomic composition from an analysis | None |\n| **MGnify_get_go_terms** | GO functional annotations from an analysis | None |\n| **MGnify_get_interpro** | InterPro protein domain annotations | None |\n| **MGnify_list_biomes** | Browse MGnify biome hierarchy | None |\n| **MGnify_search_genomes** | Search metagenome-assembled genomes (MAGs) | None |\n| **MGnify_get_genome** | Genome quality metrics (completeness, contamination) | None |\n| **GTDB_search_genomes** | Search bacterial\u002Farchaeal genomes by taxonomy | None |\n| **GTDB_get_species** | Species cluster details from GTDB | None |\n| **GTDB_get_taxon_info** | Taxonomic rank info in GTDB hierarchy | None |\n| **GTDB_search_taxon** | Search taxa by partial name across all ranks | None |\n| **ENAPortal_search_studies** | Find sequencing studies in ENA. Query format: `description=\"keyword\"` | None |\n| **ENAPortal_search_samples** | Find samples with environmental metadata | None |\n| **ols_search_terms** | Search ENVO ontology for biome\u002Fenvironment terms | None |\n| **EuropePMC_search_articles** | Find microbiome publications | None |\n| **PubMed_search_articles** | Literature search (different coverage than EuropePMC) | None |\n\n**For drug-microbiome studies**, also use:\n- `PubChem_get_CID_by_compound_name` \u002F `PubChem_get_compound_properties_by_CID` — drug identity\n- `CTD_get_chemical_gene_interactions` — drug-gene interactions (e.g., metformin affects 1,175+ genes)\n- `kegg_search_pathway` \u002F `kegg_get_pathway_info` — microbial metabolic pathways (butanoate, propanoate)\n- `ReactomeAnalysis_pathway_enrichment` — host pathway enrichment for drug-affected genes\n- `drugbank_vocab_search` — drug mechanism and targets\n\n> **MGnify tip**: Use concise single-keyword searches (e.g., \"metformin\") — multi-word queries may timeout. The MGnify API can be slow for broad searches.\n\n## Quick Start\n\n```python\nfrom tooluniverse import ToolUniverse\n\ntu = ToolUniverse()\ntu.load_tools()\n\n# 1. Search for gut microbiome studies\nstudies = tu.run_one_function({\n    'name': 'MGnify_search_studies',\n    'arguments': {'search': 'gut microbiome', 'size': 5}\n})\n\n# 2. Get study details\ndetail = tu.run_one_function({\n    'name': 'MGnify_search_studies_detail',\n    'arguments': {'study_accession': 'MGYS00006860'}\n})\n\n# 3. List analyses for a study\nanalyses = tu.run_one_function({\n    'name': 'MGnify_list_analyses',\n    'arguments': {'study_accession': 'MGYS00006860', 'size': 5}\n})\n\n# 4. Get taxonomic profile from an analysis\ntaxonomy = tu.run_one_function({\n    'name': 'MGnify_get_taxonomy',\n    'arguments': {'analysis_accession': 'MGYA00612683'}\n})\n\n# 5. Get functional annotations\ngo_terms = tu.run_one_function({\n    'name': 'MGnify_get_go_terms',\n    'arguments': {'analysis_accession': 'MGYA00612683'}\n})\n```\n\n## Common Workflows\n\n### Workflow 1: Study Discovery by Environment\n\nFind studies for a specific biome using MGnify's biome hierarchy:\n\n```python\n# Browse biome hierarchy\nbiomes = tu.run_one_function({\n    'name': 'MGnify_list_biomes',\n    'arguments': {'lineage': 'root:Host-associated:Human', 'depth': 3}\n})\n\n# Search studies in a specific biome\nstudies = tu.run_one_function({\n    'name': 'MGnify_search_studies',\n    'arguments': {'biome': 'root:Host-associated:Human:Digestive system', 'size': 10}\n})\n\n# Look up ENVO ontology terms for environment metadata\nenvo = tu.run_one_function({\n    'name': 'ols_search_terms',\n    'arguments': {'query': 'human gut', 'ontology': 'envo', 'rows': 5}\n})\n```\n\n### Workflow 2: Taxonomic Profiling\n\nGet the microbial composition of a metagenomics sample:\n\n```python\n# Get analyses for a study\nanalyses = tu.run_one_function({\n    'name': 'MGnify_list_analyses',\n    'arguments': {'study_accession': 'MGYS00006860', 'size': 3}\n})\n\n# Get taxonomy for a specific analysis\ntaxonomy = tu.run_one_function({\n    'name': 'MGnify_get_taxonomy',\n    'arguments': {'analysis_accession': 'MGYA00612683'}\n})\n# Returns organisms with lineage, abundance counts, and taxonomy rank\n```\n\n### Workflow 3: Genome Quality Assessment\n\nEvaluate metagenome-assembled genomes (MAGs):\n\n```python\n# Search for genomes from a specific taxon\ngenomes = tu.run_one_function({\n    'name': 'MGnify_search_genomes',\n    'arguments': {'search': 'Faecalibacterium prausnitzii', 'size': 5}\n})\n\n# Get quality metrics for a genome\ngenome = tu.run_one_function({\n    'name': 'MGnify_get_genome',\n    'arguments': {'genome_accession': 'MGYG000000001'}\n})\n# Returns completeness, contamination, N50, genome length, taxonomy\n\n# Cross-reference with GTDB taxonomy\ngtdb = tu.run_one_function({\n    'name': 'GTDB_search_genomes',\n    'arguments': {'operation': 'search_genomes', 'query': 'Faecalibacterium', 'items_per_page': 5}\n})\n```\n\n### Workflow 4: Functional Annotation\n\nDiscover functional potential of a metagenome:\n\n```python\n# GO terms from an analysis\ngo_terms = tu.run_one_function({\n    'name': 'MGnify_get_go_terms',\n    'arguments': {'analysis_accession': 'MGYA00612683'}\n})\n\n# InterPro domains\ninterpro = tu.run_one_function({\n    'name': 'MGnify_get_interpro',\n    'arguments': {'analysis_accession': 'MGYA00612683'}\n})\n```\n\n### Workflow 5: Literature Integration\n\nCombine metagenomics data with published research:\n\n```python\n# Find relevant publications\npapers = tu.run_one_function({\n    'name': 'EuropePMC_search_articles',\n    'arguments': {'query': 'gut microbiome AND Faecalibacterium AND (IBD OR \"Crohn\")', 'limit': 10}\n})\n\n# Find sequencing data in ENA\nena_studies = tu.run_one_function({\n    'name': 'ENAPortal_search_studies',\n    'arguments': {'query': 'description=\"gut microbiome 16S\"', 'limit': 5}\n})\n```\n\n## MGnify Biome Hierarchy\n\nKey biome lineages (use `MGnify_list_biomes` to discover others):\n- Human gut: `root:Host-associated:Human:Digestive system`\n- Human oral\u002Fskin: `root:Host-associated:Human:Oral` \u002F `root:Host-associated:Human:Skin`\n- Soil: `root:Environmental:Terrestrial:Soil`\n- Ocean\u002FFreshwater: `root:Environmental:Aquatic:Marine` \u002F `root:Environmental:Aquatic:Freshwater`\n- Wastewater: `root:Engineered:Wastewater`\n\n## Key Identifiers\n\nMGnify: studies=`MGYS*`, analyses=`MGYA*`, genomes=`MGYG*`. ENA studies=`PRJEB*`. GTDB genomes=`GCA_*`. ENVO terms=`ENVO:*` (e.g. ENVO:00002041).\n\n## Reasoning Framework\n\n### Starting Point: Define the Question First\n\nMicrobiome analysis starts with: what is the question? LOOK UP DON'T GUESS — always check the study type and sequencing method before interpreting results.\n\n**Decision tree for data type:**\n- Community composition (who is there?) → 16S\u002FITS amplicon → alpha\u002Fbeta diversity, differential abundance\n- Functional potential (what can they do?) → Shotgun metagenomics → MGnify GO terms, InterPro, KEGG pathways\n- Active function (what are they doing now?) → Metatranscriptomics → specialized pipelines (not MGnify\u002FGTDB alone)\n\nBefore calling any tool, determine which data type the user has via `MGnify_search_studies_detail` — the pipeline type (amplicon vs shotgun) determines which analyses are valid. Do not apply 16S diversity metrics to metagenomic data or vice versa.\n\n### Dysbiosis Assessment Strategy\n\nDysbiosis (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.\n\n1. **Check alpha diversity**: Reduced Shannon index relative to controls suggests dysbiosis. Use `MGnify_get_taxonomy` to get community profiles, then assess richness and evenness.\n2. **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`.\n3. **Functional consequences**: Does taxonomic shift correlate with loss\u002Fgain of metabolic pathways? Check `MGnify_get_go_terms` and `MGnify_get_interpro` for the affected samples.\n4. **Confounders**: Antibiotics, diet, age, and geography all affect microbiome composition. A dysbiosis claim requires controlling for these factors or acknowledging them as limitations.\n\n### Taxonomic vs Functional Analysis: When to Use Each\n\n- **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`.\n- **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`.\n- **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.\n\n### Evidence Grading\n\n| Tier | Description | Example |\n|------|-------------|---------|\n| **T1** | Replicated finding across multiple cohorts with consistent effect | Reduced Faecalibacterium in IBD (>10 independent studies) |\n| **T2** | Single well-powered study (n > 100) with appropriate controls | Metformin-associated Akkermansia enrichment in a controlled trial |\n| **T3** | Pilot study or observational association, small sample size | Taxonomic shift in n=15 case-control, no validation cohort |\n| **T4** | Computational prediction or single-sample observation | Novel MAG with predicted function, no culture confirmation |\n\n### Interpretation Guidance\n\n**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.\n\n**Beta diversity (between-sample)**: Bray-Curtis (abundance-based) or UniFrac (phylogenetic). PERMANOVA p \u003C 0.05 with R-squared > 0.05 indicates condition-driven clustering. Low R-squared (\u003C0.02) 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.\n\n**Taxonomic composition**: Relative abundance at phylum level (Firmicutes\u002FBacteroidetes 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 \u003C0.1% may be noise or sequencing artifacts. GTDB taxonomy may reclassify NCBI names (e.g., Firmicutes split into multiple phyla).\n\n**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.\n\n### Synthesis Questions\n\nA complete microbiome report should answer:\n1. How does alpha diversity compare between conditions, and is the difference significant?\n2. Does beta diversity analysis show condition-driven clustering (PERMANOVA)?\n3. Which taxa are differentially abundant, and are they known commensals or pathobionts?\n4. What functional pathways are enriched, and which taxa likely drive them?\n5. How do findings compare to published studies for the same biome\u002Fcondition (literature context)?\n\n## Tips\n\n- MGnify study accessions start with `MGYS`, analyses with `MGYA`, genomes with `MGYG`\n- Use `MGnify_list_biomes` first to find the correct biome lineage string\n- `MGnify_get_taxonomy` returns phylum-level to species-level composition\n- GTDB provides standardized bacterial\u002Farchaeal taxonomy (differs from NCBI in some lineages)\n- For 16S amplicon studies, taxonomy is the primary output; for shotgun metagenomics, both taxonomy and functional annotations are available\n- The `size` parameter in MGnify tools controls results per page (max 100)\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Flibrary\u002Fskills\u002Fmicrobiome-research","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill microbiome-research",{"tool":56,"command":57},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill microbiome-research",{"tool":59,"command":60},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill microbiome-research","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Fmicrobiome-research\u002FSKILL.md",true,[],[65,86,114,141,155,171],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":71,"publishedAt":39},"01M6DF7D7478R55P46REYH4QAC","clarify-requirements","Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. NOT for mid-task use (injection risk); NOT when the goal is already unambiguous; use to disambiguate vague prompts before any file edits.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fworkspace-hooks\u002Fskills\u002Fclarify-requirements\u002FSKILL.md",[],[72,73,74,75,76,77,78,79,80,33,81,82,83,84,85],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":95,"publishedAt":39},"01M6DF79CA4BHJS66J8EZDBPWJ","openmm-md","Run FREE, open-source, Windows-native GPU molecular dynamics via the openmm-md MCP (OpenMM + CUDA on the local NVIDIA GPU). Use for protein \u002F protein-ligand MD when you want a free stack or are on Windows without a Schrodinger license. Covers the full workflow (prepare, solvate, minimize, equilibrate, production, analyze, mmgbsa) AND an honest decision guide vs Schrodinger Desmond, including how to run Desmond legitimately on Linux or a cloud GPU with a real license. This is a deliberately LESSER free alternative and does NOT replace the schrodinger MCP tools. Do NOT use it to pick a free tool when a validated FEP+, WaterMap, IFD-MD, or Prime MM-GBSA result is actually required.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fmcp\u002Fopenmm-md-mcp\u002Fskill\u002Fopenmm-md\u002FSKILL.md",[92],{"categoryNo":93,"categoryName":94,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[96,97,98,99,100,30,101,102,103,104,105,106,107,108,33,109,110,111,112,113],"openmm md","how","decision","pick","linux","tool","stack","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local","do","MCP",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":126,"publishedAt":39},"01M6DF76C1MG85VEWP8VZCBNMN","visual-review","Take screenshots of web pages and UI using Playwright for visual review and iteration","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fvisual-review\u002FSKILL.md",[120,123],{"categoryNo":121,"categoryName":122,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":124,"categoryName":125,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[127,128,129,130,131,132,133,134,135,136,97,137,138,139,140],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":147,"publishedAt":39},"01M6DF73AQVCBZKEN0YXEX9FXP","status","Diagnostic status report — shows metaswarm installation state, project setup, and potential issues","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstatus\u002FSKILL.md",[],[143,148,149,150,151,152,153,154],"setup","report","project","installation","project setup","issues","state",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":162,"publishedAt":39},"01M6DF6X6C2XB0P6GX76P7TTYV","start","Use when starting work on any task, when the user mentions metaswarm, or when the user wants to begin tracked development work","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstart\u002FSKILL.md",[161],{"categoryNo":93,"categoryName":94,"taxonomyType":21,"taxonomyTypeDesc":22},[157,163,77,164,165,166,33,167,168,169,170],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":172,"name":148,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":176,"publishedAt":39},"01M6DF6QMJ9AJKFKHHKRT005PQ","Interactive project setup — detects your project, configures metaswarm, writes project-local files","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fsetup\u002FSKILL.md",[],[148,150,111,177,152,178],"files","interactive"]