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disease-analysis

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更新时间2026年6月30日 03:52

Run a multi-scale disease pathophysiology analysis — molecular defects through to clinical symptoms — using TogoMCP (RDF Portal SPARQL), TogoID ID conversion, OLS4, and PubMed. Use whenever the user asks to "analyze a disease", build a disease mechanism model, find disease-associated proteins/pathways/drugs, map a disease across biological scales (molecular → pathway → cellular → tissue → clinical → treatment), or convert disease/protein/drug IDs across databases for a disease. Also triggers on "disease analysis", "pathophysiology of X", "what proteins/pathways/drugs are involved in X". The full method lives in references/disease_analysis.md.

安装

用 Codex 或 Claude 帮你安装复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。

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dbcls

dbcls/togomcp

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相关职业SOC

基于 SOC 职业分类

医学科学家（非流行病学）生命、物理与社会科学类职业·SOC 19-1042

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2 个文件

SKILL.md

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mie-generator

dbcls/togomcp

Generate or update MIE (Metadata Interoperability Exchange) YAML files that describe RDF databases for TogoMCP. Use this skill whenever the user asks to create, write, regenerate, update, or improve an MIE file for any RDF database (UniProt, ChEMBL, Reactome, Rhea, PubChem, MeSH, PDB, or any new bio/chem database being added to TogoMCP), or mentions anything about the `togo_mcp/data/mie/` directory, MIE schema documentation, database onboarding for TogoMCP, or describing an RDF endpoint's schema/ShEx/SPARQL examples for LLM consumption. Trigger even if the user says things like "document this database", "add Reactome to our MIE collection", "the ChEMBL metadata file is out of date", or "I need to describe this SPARQL endpoint for Claude" — as long as the context is TogoMCP or an RDF database being described in YAML.

2026-07-179

qa-generator

dbcls/togomcp

Generate, refresh, or replace benchmark questions for the TogoMCP evaluation set (benchmark/questions/question_XXX.yaml). Use whenever the user asks to create, add, extend, refresh, re-validate, replace, redirect, or retire benchmark questions — e.g. "add a few benchmark questions", "extend the QA set", "we need more list-type questions", "write a new question about a topic or database", "grow the benchmark past 50", "refresh question_042", "the answer to Q030 looks stale", "the queries are out of date". Anything mentioning benchmark/questions, coverage_tracker.yaml, the QA creation guide, or question YAML files. New questions follow the v5.5.0 type-first protocol against live RDF databases (SPARQL, OLS4, PubMed); updates re-run queries against current state and refresh answers — both presented for approval before writing.

2026-07-179

research-article-analysis

dbcls/togomcp

Systematically validate a research article's claims against TogoMCP RDF databases — molecular formulas, reaction equations, pathways, protein function, GO definitions — instead of trusting the paper's text or keyword-search snippets. Use whenever the user asks to "analyze this paper/article", fact-check or validate a study's biology, verify claimed metabolites/reactions/pathways/ proteins/processes against databases, score the evidence for a paper's claims, or build a cross-database evidence chain (ChEBI → Rhea → UniProt → Reactome → GO). Triggers on "article analysis", "validate this study", "check this paper's claims". The full method lives in references/research_article_analysis.md.

2026-07-019

prism

dbcls/togomcp

PRISM finds entities (genes, proteins, compounds) at the INTERSECTION of two or more property sets on RDF knowledge graphs via TogoMCP — e.g. "genes common to disease X and druggable pathway Y", "targets both linked to phenotype P and modulated by existing drugs". Use whenever the user asks a set-intersection / overlap / "what is common to" / "is there a gene or target that is both ... and ..." question, a drug-repurposing or target-identification question, or anything combining a DISEASE/PHENOTYPE axis with a FUNCTION axis and/or a DRUGGABILITY axis. Trigger even on "shared genes between A and B", "druggable targets for disease X", or "intersection of ... and ...". PRISM makes each axis a reproducible predicate expanded over the ontology hierarchy, triangulated across evidence sources, and intersected by stable IDs with a provenance ledger. If you catch yourself listing candidate genes from memory, STOP and use it.

2026-06-269

intro-page-updater

dbcls/togomcp

Update the public TogoMCP intro/landing page at `togo_mcp/data/docs/togomcp-intro.html` whenever the catalog of databases, the catalog of MCP tools, or any other surface fact shown on the page changes. Trigger when the user adds/removes/renames a database (MIE file, `endpoints.csv` row), adds/removes/renames a tool (any `@mcp.tool` decorator in `togo_mcp/*.py`), updates the preprint citation, changes setup instructions for a host (Claude/ChatGPT/Gemini), revises the recommended companion MCP servers, or otherwise edits something the landing page advertises. Also trigger when the user says "update the intro page", "regenerate the landing page", "refresh togomcp-intro.html", "the intro is out of date", or similar — even if they don't name the file. Spec for what the page should contain is `togo_mcp/data/docs/make_intro.md`; the rendered page is `togo_mcp/data/docs/togomcp-intro.html`.

2026-06-249

name

disease-analysis

description

Disease Analysis (multi-scale, TogoMCP-driven)

Systematic analysis of any disease from molecular defect to clinical symptom, built from structured queries (not model recall). The driver is the TogoMCP MCP tools (run_sparql, togoid_convertId, togoid_countId) plus OLS4 and PubMed.

The full prompt template, output format, and per-disease-category customizations live in references/disease_analysis.md — read it for the deliverable structure. This SKILL.md is the operational path: verified queries, the exact tool calls, and the traps.

Prerequisites

The TogoMCP MCP server connected to your client (e.g. Claude Desktop, Claude Code). Its run_sparql, togoid_*, search_*, and get_MIE_file tools are what drive this skill.
The OLS4 and PubMed MCP tools (used in Phase 1 and Phase 6).
Network access from those servers to rdfportal.org and api.togoid.dbcls.jp.

Warm-up check (confirm connectivity before trusting the workflow)

Before a full analysis, send one Phase-2 query and one TogoID conversion through the MCP tools and confirm both return rows — that proves the SPARQL endpoint and the TogoID API are both reachable and the patterns still resolve:

run_sparql(database="uniprot", sparql_query="""
PREFIX up: <http://purl.uniprot.org/core/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?protein WHERE {
  ?protein a up:Protein ; up:reviewed 1 ;
           up:organism <http://purl.uniprot.org/taxonomy/9606> ;
           up:annotation ?annot .
  ?annot a up:Disease_Annotation ; rdfs:comment ?c .
  ?c bif:contains "'osteoarthritis'"
} LIMIT 5""")
togoid_convertId(ids="P45452", route="uniprot,pdb")     → structures for MMP13

If either returns nothing, that step is broken (endpoint down, schema drift, or full-text index changed) — diagnose it (re-read the database's MIE via get_MIE_file) before running the analysis.

Run the analysis (the six phases, with verified tool calls)

Drive these with the TogoMCP MCP tools. Each call below was run live and returned data.

Phase 1 — Disease ID & ontology cross-refs. Resolve the disease to MONDO via OLS4, then fan out:

togoid_convertId(ids="MONDO:0005178", route="mondo,mesh")   → [["0005178","D010003"]]
togoid_convertId(ids="MONDO:0005178", route="mondo,doid")
togoid_convertId(ids="MONDO:0005178", route="mondo,hp_phenotype")
togoid_convertId(ids="MONDO:0005178", route="mondo,omim_phenotype")

Phase 2 — Disease-associated proteins (UniProt SPARQL). Full-text search over disease annotations. bif:contains must sit on a plain variable, never a property path:

run_sparql(database="uniprot", sparql_query="""
PREFIX up: <http://purl.uniprot.org/core/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?protein ?mnemonic ?fullName ?diseaseComment WHERE {
  ?protein a up:Protein ; up:reviewed 1 ;
           up:organism <http://purl.uniprot.org/taxonomy/9606> ;
           up:mnemonic ?mnemonic ; up:recommendedName ?name ; up:annotation ?annot .
  ?name up:fullName ?fullName .
  ?annot a up:Disease_Annotation ; rdfs:comment ?diseaseComment .
  ?diseaseComment bif:contains "'osteoarthritis'"
} LIMIT 30""")
→ Q9BXN1 / ASPN_HUMAN (Asporin), D14 allele association

Phase 3 — Cross-link proteins (TogoID). For each UniProt accession:

togoid_convertId(ids="P45452,P16112,O75173,Q9UNA0", route="uniprot,ncbigene")  → 4 pairs
togoid_convertId(ids="P45452,P16112,O75173,Q9UNA0", route="uniprot,pdb")       → 65 structures
togoid_countId(ids="P45452,P16112,O75173", source="uniprot", target="pdb")     → {"source":3,"target":58}
# also: uniprot,hgnc · uniprot,chembl_target · uniprot,go · uniprot,reactome_pathway

Phase 4 — Pathway analysis (Reactome SPARQL). Always include the FROM graph; score with bif:contains. Then get participants — the protein xref requires "UniProt"^^xsd:string (see Gotchas):

run_sparql(database="reactome", sparql_query="""
PREFIX bp: <http://www.biopax.org/release/biopax-level3.owl#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
SELECT DISTINCT ?reactionName ?proteinName ?uniprotId
FROM <http://rdf.ebi.ac.uk/dataset/reactome> WHERE {
  ?reaction a bp:BiochemicalReaction ; bp:displayName ?reactionName ; bp:left ?entity .
  FILTER(CONTAINS(?reactionName, "MMP") || CONTAINS(?reactionName, "collagen"))
  ?entity bp:entityReference ?proteinRef .
  ?proteinRef bp:xref ?xref .
  ?xref a bp:UnificationXref ; bp:db "UniProt"^^xsd:string ; bp:id ?uniprotId .
  OPTIONAL { ?proteinRef bp:name ?proteinName }
  FILTER(STRLEN(?uniprotId) = 6)
} LIMIT 50""")
→ MMP9 (P52176), MMP2 ...

Phase 5 — Drugs & targets. search_chembl_molecule / search_chembl_target to find IDs, then:

togoid_convertId(ids="CHEMBL118,CHEMBL139", route="chembl_compound,pubchem_compound") → [["CHEMBL118","2662"],["CHEMBL139","3033"]]
togoid_convertId(ids="CHEMBL118,CHEMBL139", route="chembl_compound,drugbank")
# get_compound_attributes_from_pubchem(...) for properties

Phase 6 — Literature. PubMed:search_articles("<disease> <mechanism> <treatment>") then get_article_metadata / get_full_text_article.

Read MIE files first when querying a database you haven't this session: get_MIE_file(database="reactome"), get_MIE_file(database="uniprot"). The MIE is the authoritative schema/warnings source and is fresher than this doc.

Gotchas (the traps that silently return zero rows)

Reactome xref typing — the #1 silent-fail. bp:db "UniProt"^^xsd:string. A plain "UniProt" literal (no ^^xsd:string) matches nothing and returns an empty result with no error. Same applies to any VALUES/literal compared against Reactome string-typed data. Pre-flight every quoted literal against the database's MIE critical_warnings before running.
Reactome has two UniProt xref flavors: "UniProt" (~91k) and "UniProt Isoform" (~374). Filtering on only one can silently drop proteins (e.g. NPC1L1 / Q9UHC9 is only reachable via the isoform form). For exhaustive coverage match both.
bif:contains cannot sit on a property path. ?p up:recommendedName/up:fullName ?n . ?n bif:contains "..." → 400. Split into two triples and put bif:contains on the terminal variable.
run_sparql database= vs endpoint_name= are different axes — database is a single DB key (uniprot, reactome); endpoint_name is an endpoint group (sib, ebi). Don't mix; a wrong value fails deterministically — don't retry it.
Reactome needs FROM <http://rdf.ebi.ac.uk/dataset/reactome> or you get wrong/empty results.
Always up:reviewed 1 + taxonomy/9606 on UniProt disease queries, and a LIMIT everywhere, or you get TrEMBL noise / timeouts.
TogoID route ordering matters (uniprot,ncbigene ≠ ncbigene,uniprot). Use togoid_countId as a cheap pre-check before a bulk convertId.
Disease keyword matters more than the MONDO ID for Phase 2 — "osteoarthritis" returns 1 protein, broader keywords return more. Try synonyms if a disease returns nothing.

Troubleshooting

Symptom	Fix
Reactome query returns empty but UniProt works	EBI endpoint down or schema drift; retry, then check `get_MIE_file(database="reactome")` for changed predicates
`run_sparql` returns empty unexpectedly	You likely passed an endpoint group to `database=` or dropped `^^xsd:string` / `FROM` — compare against the verified queries above
TogoID convert returns `[]`	Route ordering wrong, or IDs in wrong format — check `togoid_getDataset(dataset="...")` and `togoid_getAllRelation()`
Query times out	Endpoints are slow under load; add/lower a `LIMIT` and retry

Full method

references/disease_analysis.md — the complete prompt template, the deliverable/output structure, and the per-disease-category customizations. This SKILL.md gives the operational queries and traps; that file gives the shape of the final analysis.