<< All versions
Skill v1.0.1
currentAutomated scan100/100internscience/scp/variant-pharmacogenomics
1 files
──Details
PublishedJune 16, 2026 at 11:17 PM
Content Hashsha256:0dd07946b27c7533...
Git SHAcea539856403
Bump Typepatch
──Files
Files (1 file, 2.4 KB)
SKILL.md2.4 KBactive
SKILL.md · 74 lines · 2.4 KB
version: "1.0.1" name: variant-pharmacogenomics description: "Query PharmGKB (clinPGx) for pharmacogenomic clinical annotations — how a variant affects drug response, dosing, and adverse reactions." license: MIT license metadata: skill-author: PJLab
PharmGKB (clinPGx) — Pharmacogenomic Annotations
Usage
Tool Description
tex
Query PharmGKB clinical annotations API to find drug-gene-variant interactions.Database: PharmGKB / clinPGx (https://www.pharmgkb.org/)API: GET https://api.clinpgx.org/v1/data/clinicalAnnotation?location.fingerprint={rsid}&view=baseArgs:rs_id (str): dbSNP rsID (e.g. "rs7412")Return:Clinical annotations: related drugs (chemicals), evidence level (1A/1B/2A/2B/3/4),related diseases, gene (APOE etc.), phenotype categories (efficacy/toxicity/dosage/PK),related guidelines and FDA labels.Return Fields Explanation:- relatedChemicals: 关联的药物/化合物名称和 PharmGKB ID- levelOfEvidence.term: 证据级别 (1A=最强, 有CPIC/DPWG指南; 4=最弱, 个案报告)- location.genes: 变异所在基因- relatedDiseases: 关联疾病- types: 注释类型 (Efficacy=疗效, Toxicity=毒性, Dosage=剂量, PK=药代动力学)- relatedGuidelines: 关联的用药指南 (CPIC, DPWG 等)- relatedLabels: 关联的 FDA 药品标签
Query Example
python
import requestsrs_id = "rs7412"url = f"https://api.clinpgx.org/v1/data/clinicalAnnotation?location.fingerprint={rs_id}&view=base"resp = requests.get(url, timeout=30).json()if isinstance(resp, dict) and "data" in resp:annotations = resp["data"]elif isinstance(resp, list):annotations = respelse:annotations = []print(f"[PharmGKB] {rs_id} 药物基因组注释数: {len(annotations)}")for i, ann in enumerate(annotations):# 药物drugs = [c.get("name", "") for c in ann.get("relatedChemicals", [])]# 证据级别evidence = ann.get("levelOfEvidence", {}).get("term", "N/A")# 基因genes = [g.get("symbol", "") for g in ann.get("location", {}).get("genes", [])]# 疾病diseases = [d.get("name", "") for d in ann.get("relatedDiseases", [])]# 注释类型ann_types = ann.get("types", [])print(f"\n [{i+1}] 药物: {drugs}")print(f" 证据级别: {evidence}")print(f" 基因: {genes}")print(f" 类型: {ann_types}")if diseases:print(f" 疾病: {diseases}")