gene-knowledge-integration

name	gene-knowledge-integration
description	Given a gene symbol (e.g. TPMT), query 3 public databases (ClinGen CAR, PharmGKB, Monarch) to obtain gene registry info, FDA drug labels, clinical annotations, and gene-phenotype associations. Save all results into a JSON file.
license	MIT license
metadata	{"skill-author":"PJLab"}

Gene Knowledge Integration

Usage

1. Tool Descriptions

This skill chains 3 public genomics/pharmacogenomics database APIs sequentially to build a comprehensive pharmacogenomics profile for a given gene.

Tool 1: ClinGen CAR — Gene Registry Info

Query ClinGen Allele Registry API to get gene registration information.
API: GET https://reg.genome.network/gene?HGNC.symbol={gene_symbol}
Args:
    gene_symbol (str): HGNC gene symbol (e.g. "TPMT")
Return:
    Gene record (dict): Contains @id (GN id), locus (genomic coordinates),
        externalRecords (HGNC id/name/symbol, NCBI gene id, MANE transcripts).

Tool 2: PharmGKB (ClinPGx) — Gene Info, FDA Labels & Clinical Annotations

Query PharmGKB ClinPGx API to get pharmacogenomics information.
API (gene):   GET https://api.clinpgx.org/v1/data/gene?symbol={gene_symbol}&view=base
API (labels): GET https://api.clinpgx.org/v1/data/label?source=fda&relatedGenes.symbol={gene_symbol}&view=base
API (clin):   GET https://api.clinpgx.org/v1/data/clinicalAnnotation?location.genes.symbol={gene_symbol}&view=base
Args:
    gene_symbol (str): HGNC gene symbol (e.g. "TPMT")
Return:
    gene: PharmGKB gene record with accession id, alternate names, cross-references.
    labels: FDA drug labels mentioning this gene (drug name, source, testing level).
    clinicalAnnotations: Clinical annotations linking genotype to phenotype
        (level of evidence, related chemicals, phenotype categories).

Tool 3: Monarch Initiative — Gene-Phenotype Associations

Query Monarch Initiative API to get gene-to-phenotype associations.
API: GET https://api-v3.monarchinitiative.org/v3/api/entity/{hgnc_id}/biolink:GeneToPhenotypicFeatureAssociation
Args:
    hgnc_id (str): HGNC identifier (e.g. "HGNC:12014" for TPMT)
Return:
    items (list): Each item contains subject (gene), object (phenotype HP term),
                  object_label (phenotype name), evidence_types, publications.

2. Gene Knowledge Integration

Query 3 databases (ClinGen CAR → PharmGKB → Monarch) for a given gene symbol, then save all results into a single JSON file {gene_symbol}_knowledge.json.

import requests
import json
from datetime import datetime

gene_symbol = "TPMT"
results = {"query_gene": gene_symbol, "timestamp": datetime.now().isoformat()}

# ── Step 1: ClinGen CAR — 基因注册信息 ──
# 调用 ClinGen Allele Registry API，获取基因的 GN id、基因组坐标、
# HGNC/NCBI 外部记录和 MANE 转录本信息。
car_url = f"https://reg.genome.network/gene?HGNC.symbol={gene_symbol}"
car_resp = requests.get(car_url, headers={"Accept": "application/json"}, timeout=30)
car = car_resp.json()
results["clingen_car"] = car
hgnc_id = car.get("externalRecords", {}).get("HGNC", {}).get("id", "")
print(f"[ClinGen CAR] 基因={gene_symbol}, GN_id={car.get('@id','')}, HGNC={hgnc_id}")

# ── Step 2a: PharmGKB — 基因信息 ──
# 调用 PharmGKB ClinPGx API，获取基因的药物基因组学基本信息。
pgx_gene_url = f"https://api.clinpgx.org/v1/data/gene?symbol={gene_symbol}&view=base"
pgx_gene_resp = requests.get(pgx_gene_url, timeout=30)
pgx_gene = pgx_gene_resp.json()
results["pharmgkb_gene"] = pgx_gene
print(f"[PharmGKB] 基因信息获取成功")

# ── Step 2b: PharmGKB — FDA 药物标签 ──
# 查询与该基因相关的 FDA 药物标签，了解哪些药物的说明书提到了该基因。
pgx_label_url = (
    f"https://api.clinpgx.org/v1/data/label"
    f"?source=fda&relatedGenes.symbol={gene_symbol}&view=base"
)
pgx_labels_resp = requests.get(pgx_label_url, timeout=30)
pgx_labels = pgx_labels_resp.json()
results[] = pgx_labels
()



pgx_clin_url = (
    
    
)
pgx_clin_resp = requests.get(pgx_clin_url, timeout=)
pgx_clin = pgx_clin_resp.json()
results[] = pgx_clin
()




 hgnc_id:
    monarch_url = (
        
        
    )
    monarch_resp = requests.get(monarch_url, timeout=)
    monarch = monarch_resp.json()
    items = monarch.get(, [])
    results[] = {
        : (items),
        : items
    }
    phenotypes = [i.get(, )  i  items[:]]
    ()
:
    results[] = {: }
    ()


output_file = 
 (output_file, , encoding=)  f:
    json.dump(results, f, indent=, ensure_ascii=)
()