disease-analysis

name

disease-analysis

description

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.

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.