Run any Skill in Manus with one click

research-article-analysis

Stars9

Forks4

UpdatedJuly 1, 2026 at 00:49

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.

Installation

Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

Run Skill in Manus

Source

dbcls

dbcls/togomcp

View GitHub Repository View Creator Repositories

Download

Run Skill in Manus

Related occupationsSOC

Based on SOC occupation classification

Biological Scientists, All OtherLife, Physical, and Social Science Occupations·SOC 19-1029

File Explorer

2 files

SKILL.md

readonly

More from this repository

same repository

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the user says things like "document this database", "add Reactome to our MIE collection", "the ChEMBL metadata file is out of date", or "I need to describe this SPARQL endpoint for Claude" — as long as the context is TogoMCP or an RDF database being described in YAML.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

disease-analysis

dbcls/togomcp

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.

2026-06-309

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

research-article-analysis

description

Research Article Analysis (claim validation via TogoMCP SPARQL)

Validate a paper claim-by-claim by running structured SPARQL against five RDF databases — ChEBI, Rhea, Reactome, UniProt, GO — and citing the query results (exact formulas, equations, EC numbers, GO definitions), not the paper's prose or keyword-search text. The driver is the TogoMCP MCP tools (run_sparql, the search_* tools, get_MIE_file).

The full phased checklist, ID-tracking templates, scoring rubric, and anti-skipping safeguards live in references/research_article_analysis.md — read it for the deliverable structure and discipline. This SKILL.md is the operational path: the SPARQL that actually works (the printed templates have two bugs — see Gotchas), the verified tool calls, and the traps.

Prerequisites

The TogoMCP MCP server connected to your client (e.g. Claude Desktop, Claude Code). Its run_sparql, search_*, and get_MIE_file tools are what drive this skill.
Network access from that server to rdfportal.org.

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

Before relying on the templates, send the ChEBI structure query below through the run_sparql tool and confirm it returns a row — that proves the endpoint is reachable and the (chemrof-namespace) pattern still resolves. If it returns nothing, that database's query type is broken (endpoint down or schema drift) — re-read its MIE via get_MIE_file and fix the pattern below before relying on it.

Run the analysis (phases, with verified queries)

Follow the phase discipline in the reference doc: 2A select databases (5 rules), 2B get_MIE_file(database=...) for each, 2C keyword-search to get IDs, 2D run 2+ SPARQL per database using those IDs, 3A reconstruct the DB-backed logical flow (results → conclusion) + 3B triage the gaps, 4 score flow soundness, then deliver the four-part FINAL SUMMARY (main claim / conclusion / DB-backed flow / gap analysis). Drive 2D with the TogoMCP run_sparql tool. The query types below were all run live (bile-acid example) and returned data — copy these, not the reference doc's printed templates.

ChEBI — structure (⚠ uses chemrof:, NOT chebi:):

run_sparql(database="chebi", sparql_query="""
PREFIX obo: <http://purl.obolibrary.org/obo/>
PREFIX chemrof: <https://w3id.org/chemrof/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?label ?formula ?mass ?smiles ?inchikey
FROM <http://rdf.ebi.ac.uk/dataset/chebi> WHERE {
  obo:CHEBI_16359 rdfs:label ?label ;
    chemrof:generalized_empirical_formula ?formula ; chemrof:mass ?mass .
  OPTIONAL { obo:CHEBI_16359 chemrof:smiles_string ?smiles }
  OPTIONAL { obo:CHEBI_16359 chemrof:inchi_key_string ?inchikey }
}""")
→ cholic acid, C24H40O5, 408.579, SMILES, InChIKey BHQCQFFYRZLCQQ-OELDTZBJSA-N

ChEBI hierarchy uses rdfs:subClassOf + the obo/CHEBI_ filter (works as printed in the reference doc). Definition is obo:IAO_0000115; monoisotopic mass is chemrof:monoisotopic_mass; InChI string is chemrof:inchi_string.

Rhea — equation search & participants (work as printed):

run_sparql(database="rhea", sparql_query="""
PREFIX rhea: <http://rdf.rhea-db.org/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?reaction ?equation ?status ?ec WHERE {
  ?reaction rdfs:subClassOf rhea:Reaction ; rhea:equation ?equation ; rhea:status ?status .
  FILTER(CONTAINS(LCASE(?equation), "cholate"))
  OPTIONAL { ?reaction rhea:ec ?ec }
} LIMIT 10""")
→ RHEA:14541 "choloyl-CoA + H2O = cholate + CoA + H(+)", EC 3.1.2.27

Participants: VALUES ?reaction { <http://rdf.rhea-db.org/14541> } then rhea:side → rhea:contains → rhea:compound → rhea:chebi → links to ChEBI IDs.

Reactome — pathway structure & complex (work as printed): always FROM <http://rdf.ebi.ac.uk/dataset/reactome>; FILTER(CONTAINS(LCASE(?displayName), "bile acid")) for pathways, ?complex a bp:Complex + bp:component for complexes (e.g. "GPBAR1: Bile acids"). For UniProt xrefs inside Reactome use bp:db "UniProt"^^xsd:string.

UniProt — function & GO (⚠ split into TWO queries):

# Query A: name + EC. Do NOT add up:classifiedWith here — the join TIMES OUT.
run_sparql(database="uniprot", sparql_query="""
PREFIX up: <http://purl.uniprot.org/core/>
PREFIX uniprot: <http://purl.uniprot.org/uniprot/>
SELECT ?mnemonic ?fullName ?ecNumber WHERE {
  uniprot:Q8TDU6 up:reviewed 1 ; up:mnemonic ?mnemonic ; up:recommendedName ?name .
  ?name up:fullName ?fullName .
  OPTIONAL { uniprot:Q8TDU6 up:enzyme ?ecNumber }
} LIMIT 10""")
→ GPBAR_HUMAN, "G-protein coupled bile acid receptor 1"

# Query B: GO terms in a separate query (fast on its own).
run_sparql(database="uniprot", sparql_query="""
PREFIX up: <http://purl.uniprot.org/core/>
PREFIX uniprot: <http://purl.uniprot.org/uniprot/>
SELECT DISTINCT ?goTerm WHERE {
  uniprot:Q8TDU6 up:classifiedWith ?goTerm .
  FILTER(STRSTARTS(STR(?goTerm), "http://purl.obolibrary.org/obo/GO_"))
} LIMIT 50""")
→ GO_0005886, GO_0038182, GO_0097009, ...

GO — definitions & hierarchy (work as printed): always FROM <http://rdfportal.org/ontology/go>, DISTINCT, and the obo/GO_ filter. Definitions via obo:IAO_0000115; hierarchy via rdfs:subClassOf.

Cross-database evidence chain (the reference doc's required deliverable) is real and reproducible: ChEBI cholate (CHEBI_15378) appears as a rhea:chebi participant of RHEA:14541, whose EC links to a UniProt enzyme, which appears in the Reactome "GPBAR1: Bile acids" complex, annotated with GO terms.

Read MIE files first for any database you haven't queried this session: get_MIE_file(database="chebi"), etc. The MIE is the authoritative, freshest schema source.

Gotchas (the template bugs and silent-fail traps)

ChEBI: the reference doc's printed structure query returns ZERO rows. It uses chebi:formula / chebi:mass / chebi:smiles / chebi:inchikey (http://purl.obolibrary.org/obo/chebi/). RDF Portal's ChEBI stores those under the chemrof namespace instead: chemrof:generalized_empirical_formula, chemrof:mass, chemrof:smiles_string, chemrof:inchi_key_string (https://w3id.org/chemrof/). Use the corrected query above. The hierarchy query is fine because it only uses rdfs:subClassOf.
UniProt: combining up:recommendedName + up:classifiedWith in one query TIMES OUT (60s) on the SIB endpoint even with a single bound accession. Split: name/EC in one query, GO terms (classifiedWith) in another. The workflow's "Query Type 1" template combines them — don't.
run_sparql database= vs endpoint_name= are different axes (single DB key vs endpoint group); a wrong value fails deterministically — don't retry.
FROM graph is mandatory and differs per DB: ChEBI/Reactome → http://rdf.ebi.ac.uk/dataset/{chebi,reactome}; GO → http://rdfportal.org/ontology/go. Rhea and UniProt take no FROM. Wrong/missing graph → empty results, no error.
Reactome string xref needs ^^xsd:string (bp:db "UniProt"^^xsd:string); a plain literal silently matches nothing.
UniProt: always up:reviewed 1; never put bif:contains on a property path (split it). Rhea/Reactome/GO: always LIMIT on exploratory queries.
Keyword search ≠ validation. The whole point of the workflow: every ID from a search_* call must be fed back into a SPARQL query, and you cite the SPARQL result, not the search snippet.

Troubleshooting

Symptom	Fix
ChEBI structure query returns 0 rows	Confirm chemrof predicates with `SELECT ?p ?o { obo:CHEBI_16359 ?p ?o FILTER(isLiteral(?o)) }`; namespace may have changed again
UniProt query `ReadTimeout`	You joined `recommendedName` with `classifiedWith` — split into two queries (see above)
`run_sparql` returns empty unexpectedly	Missing/wrong `FROM` graph, dropped `^^xsd:string`, or `chebi:` instead of `chemrof:` — diff against the verified queries here
`go: definitions` returns 0	GO uses the `primary` endpoint and `FROM <http://rdfportal.org/ontology/go>` — not the ebi graph

Full method

references/research_article_analysis.md — the full phased checklist, ID-tracking templates, scoring rubric, and anti-skipping safeguards. This SKILL.md gives the corrected operational queries; that file gives the discipline and the deliverable structure.