lifesciences-graph-builder

name	lifesciences-graph-builder
description	Orchestrates life sciences APIs to build knowledge graphs using the Fuzzy-to-Fact protocol, combining MCPs for nodes and curl for edges, then persisting to Graphiti. This skill should be used when the user asks to \"build knowledge graphs\", \"find biological connections\", \"explore drug repurposing\", \"validate drug targets\", or mentions traversing gene→protein→pathway→drug→disease paths, multi-API orchestration, or graph persistence workflows. Use when this capability is needed.
metadata	{"author":"donbr"}

Life Sciences Graph Builder

Orchestrate multi-API graph construction using the Fuzzy-to-Fact protocol.

Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                         GRAPH CONSTRUCTION KIT                          │
├─────────────────────────────────────────────────────────────────────────┤
│  TIER 1: MCP TOOLS (Verified Nodes)                                     │
│  ├── HGNC: search_genes, get_gene                                       │
│  ├── UniProt: search_proteins, get_protein                              │
│  ├── ChEMBL: search_compounds, get_compound                             │
│  ├── STRING: search_proteins, get_interactions                          │
│  ├── Open Targets: search_targets, get_associations                     │
│  └── WikiPathways: get_pathways_for_gene, get_pathway_components        │
├─────────────────────────────────────────────────────────────────────────┤
│  TIER 2: CURL COMMANDS (Relationship Edges)                             │
│  ├── ChEMBL /mechanism: Drug → Target                                   │
│  ├── ChEMBL /drug_indication: Drug → Disease                            │
│  ├── ChEMBL /activity: Drug → Target (with Ki/IC50)                     │
│  ├── Ensembl /homology: Gene → Orthologs                                │
│  ├── STRING /enrichment: Protein Set → GO/KEGG terms                    │
│  └── NCBI elink: Gene → PubMed                                          │
├─────────────────────────────────────────────────────────────────────────┤
│  TIER 3: GRAPHITI (Persistence)                                         │
│  └── add_memory: Persist validated subgraph as JSON episode             │
└─────────────────────────────────────────────────────────────────────────┘

Workflow: Fuzzy-to-Fact Protocol

Phase 1: Anchor Node (Naming)

Resolve fuzzy user input to canonical identifier.

# MCP: HGNC
result = hgnc.search_genes("p53")
gene = hgnc.get_gene("HGNC:11998")  # → cross_references: UniProt, Ensembl, Entrez

Phase 2: Enrich Node (Functional)

Decorate node with metadata and cross-references.

# MCP: UniProt
protein = uniprot.get_protein("UniProtKB:P04637")
# → function text reveals interactors: BAX, BCL2, FAS

Phase 3: Expand Edges (Interactions)

Build adjacency list from interaction databases.

# MCP: STRING
interactions = string.get_interactions("STRING:9606.ENSP00000269305")
# → MDM2 (0.999), SIRT1 (0.999), ATM (0.995)

# Curl: Open Targets (gene-disease)
curl -s -X POST "https://api.platform.opentargets.org/api/v4/graphql" \
  -H "Content-Type: application/json" \
  -d '{"query": "{ target(ensemblId: \"ENSG00000141510\") { associatedDiseases(page: {size: 5}) { rows { disease { name } score } } } }"}'

Phase 4: Target Traversal (Pharma)

Follow edges to actionable targets.

# MCP: HGNC (resolve downstream effector)
bcl2 = hgnc.search_genes("BCL2")  # → HGNC:990

# MCP: ChEMBL (find inhibitors)
venetoclax = chembl.search_compounds("Venetoclax")  # → CHEMBL:3137309

# Curl: ChEMBL mechanism (Drug → Target edge)
curl -s "https://www.ebi.ac.uk/chembl/api/data/mechanism?molecule_chembl_id=CHEMBL3137309&format=json" \
  | jq '.mechanisms[] | {action: .action_type, target: .target_chembl_id}'
# → INHIBITOR → CHEMBL4860 (BCL2)

Phase 5: Persist Graph

Store validated subgraph in Graphiti.

# MCP: Graphiti
graphiti.add_memory(
    name="TP53-BCL2-Venetoclax pathway",
    episode_body=json.dumps({
        "nodes": [
            {"id": "HGNC:11998", "type": "Gene", "symbol": "TP53"},
            {"id": "HGNC:990", "type": "Gene", "symbol": "BCL2"},
            {"id": "CHEMBL:3137309", "type": "Compound", "name": "Venetoclax"}
        ],
        "edges": [
            {"source": "HGNC:11998", "target": "HGNC:990", "type": "REGULATES"},
            {"source": "CHEMBL:3137309", "target": "HGNC:990", "type": "INHIBITOR"}
        ]
    }),
    source="json",
    group_id="drug-repurposing"
)

Quick Edge Discovery Commands

Edge Type	Curl Command
Drug → Target	curl -s "https://www.ebi.ac.uk/chembl/api/data/mechanism?molecule_chembl_id={ID}&format=json"
Target → Drugs	curl -s "https://www.ebi.ac.uk/chembl/api/data/mechanism?target_chembl_id={ID}&format=json"
Drug → Disease	curl -s "https://www.ebi.ac.uk/chembl/api/data/drug_indication?molecule_chembl_id={ID}&format=json"
Gene → Disease	Open Targets GraphQL (see Phase 3)
Gene → Orthologs	curl -s "https://rest.ensembl.org/homology/id/human/{ENSG}?type=orthologues&content-type=application/json"
Protein Set → GO	curl -s "https://string-db.org/api/json/enrichment?identifiers={IDs}&species=9606"
Gene → PubMed	curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gene&db=pubmed&id={ID}&retmode=json"

Example: Drug Repurposing Graph

Build a complete subgraph for drug repurposing analysis:

# Step 1: Anchor - Resolve gene
# MCP: hgnc.search_genes("TP53") → HGNC:11998

# Step 2: Get protein context
# MCP: uniprot.get_protein("UniProtKB:P04637")
# → function mentions BCL2

# Step 3: Find BCL2 inhibitors
curl -s "https://www.ebi.ac.uk/chembl/api/data/mechanism?target_chembl_id=CHEMBL4860&format=json" \
  | jq '.mechanisms[] | {drug: .molecule_chembl_id, action: .action_type}'

# Step 4: Get drug indications
curl -s "https://www.ebi.ac.uk/chembl/api/data/drug_indication?molecule_chembl_id=CHEMBL3137309&format=json" \
  | jq '.drug_indications[:3][] | {disease: .mesh_heading, phase: .max_phase_for_ind}'

# Step 5: Find clinical trials
curl -s "https://clinicaltrials.gov/api/v2/studies?query.intr=venetoclax&filter.overallStatus=RECRUITING&pageSize=3&format=json" \
  | jq '.studies[] | {nct: .protocolSection.identificationModule.nctId}'

# Step 6: Persist to Graphiti
# MCP: graphiti.add_memory(...)

Node Types (Canonical CURIEs)

Type	CURIE Pattern	Example
Gene	HGNC:\d+	HGNC:11998
Protein	UniProtKB:[A-Z0-9]+	UniProtKB:P04637
Compound	CHEMBL:\d+	CHEMBL:3137309
Target	CHEMBL:\d+	CHEMBL:4860
Disease	EFO_\d+ or MONDO_\d+	EFO_0000574
Pathway	WP:WP\d+	WP:WP1742
Trial	NCT:\d+	NCT:00461032

Edge Types

Edge	Source	Target	Properties
ENCODES	Gene	Protein	-
REGULATES	Gene	Gene	direction: activation/repression
INTERACTS	Protein	Protein	score, evidence_type
INHIBITOR	Compound	Target	Ki, IC50
AGONIST	Compound	Target	EC50
TREATS	Compound	Disease	max_phase
ASSOCIATED_WITH	Gene	Disease	score, evidence_sources
MEMBER_OF	Gene	Pathway	-

Query Best Practices

Gene Discovery (Human-Centric)

• Default to species=9606 (human) for gene/protein searches
• Use page_size=10 for exploration, page_size=50 for batch operations
• Use slim=True for batch operations to reduce token usage
• Only use organism=null for comparative genomics across species

Drug Discovery vs Repurposing

• Drug repurposing: Use max_phase≥2 (clinical validation, shorter approval path)
• General discovery: No phase filter (include preclinical tools, mechanism probes)
• Check mechanisms before bioactivity data

Clinical Landscape

• Default status=RECRUITING for active research
• Use phase filter only for specific analysis:
  • PHASE3+ for commercialization analysis
  • PHASE1/2 for early pipeline
  • No filter for full landscape

See Also

• lifesciences-genomics: Ensembl, NCBI, HGNC endpoints
• lifesciences-proteomics: UniProt, STRING, BioGRID endpoints
• lifesciences-pharmacology: ChEMBL, PubChem, IUPHAR endpoints
• lifesciences-clinical: Open Targets, ClinicalTrials.gov endpoints

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