monarch-database

Monarch Initiative 数据库

概述

Monarch Initiative（https://monarchinitiative.org/）是一个跨物种集成知识库，整合人类和模式生物中的基因、疾病与表型信息。它汇集了 40 多个来源的数据，包括 OMIM、ORPHANET、HPO（人类表型本体）、ClinVar、MGI（小鼠基因组信息学）、ZFIN（斑马鱼）、RGD（大鼠）、FlyBase 和 WormBase。

Monarch 支持：

• 跨物种映射表型，以识别候选疾病基因
• 查找与某种疾病或表型相关的所有基因
• 发现人类疾病的模式生物
• 浏览 HPO 层级结构，进行表型本体查询

主要资源：

• Monarch 门户：https://monarchinitiative.org/
• Monarch API v3：https://api-v3.monarchinitiative.org/v3/
• API 文档：https://api-v3.monarchinitiative.org/v3/docs
• HPO 浏览器：https://hpo.jax.org/

何时使用此技能

以下情况可使用 Monarch：

• 罕见病基因发现：患者的表型（HPO 术语）与哪些基因相关？
• 表型相似性：两种疾病的表型特征是否相似？
• 跨物种建模：是否有针对目标疾病的小鼠或斑马鱼模型？
• HPO 术语查询：检索 HPO 术语名称、定义和本体层级结构
• 疾病-表型映射：列出与特定疾病相关的所有 HPO 术语
• 基因-表型关联：某个基因中的变异会导致哪些表型？
• 直系同源基因-表型映射：利用动物模型表型推断人类基因功能

核心功能

1. Monarch API v3

基础 URL：https://api-v3.monarchinitiative.org/v3/

import requests

BASE_URL = "https://api-v3.monarchinitiative.org/v3"

def monarch_get(endpoint, params=None):
    """Make a GET request to the Monarch API."""
    url = f"{BASE_URL}/{endpoint}"
    response = requests.get(url, params=params, headers={"Accept": "application/json"})
    response.raise_for_status()
    return response.json()

2. 表型到基因关联（Pheno2Gene）

def get_genes_for_phenotypes(hpo_ids, limit=50, offset=0):
    """
    Find genes associated with a list of HPO phenotype terms.
    Core use case: rare disease differential diagnosis.

    Args:
        hpo_ids: List of HPO term IDs (e.g., ["HP:0001250", "HP:0004322"])
        limit: Maximum number of results
    """
    params = {
        "terms": hpo_ids,
        "limit": limit,
        "offset": offset
    }
    return monarch_get("semsim/termset-pairwise-similarity/analyze", params)

def phenotype_to_gene(hpo_ids):
    """
    Return genes whose phenotypes match the given HPO terms.
    Uses semantic similarity scoring.
    """
    # Use the /association endpoint for direct phenotype-gene links
    all_genes = []
    for hpo_id in hpo_ids:
        data = monarch_get("association/all", {
            "subject": hpo_id,
            "predicate": "biolink:has_phenotype",
            "category": "biolink:GeneToPhenotypicFeatureAssociation",
            "limit": 50
        })
        for assoc in data.get("items", []):
            all_genes.append({
                "phenotype_id": hpo_id,
                "gene_id": assoc.get("object", {}).get("id"),
                "gene_name": assoc.get("object", {}).get("name"),
                "evidence": assoc.get("evidence_type")
            })
    return all_genes

# Example: Find genes associated with seizures and short stature
hpo_terms = ["HP:0001250", "HP:0004322"]  # Seizures, Short stature
genes = phenotype_to_gene(hpo_terms)

3. 疾病到基因关联

def get_genes_for_disease(disease_id, limit=100):
    """
    Get all genes associated with a disease.
    Disease IDs: OMIM:146300, MONDO:0007739, ORPHANET:558, etc.
    """
    params = {
        "object": disease_id,
        "category": "biolink:DiseaseToDiseaseAssociation",
        "limit": limit
    }
    # Use the gene-disease association endpoint
    gene_params = {
        "subject": disease_id,
        "category": "biolink:GeneToPhenotypicFeatureAssociation",
        "limit": limit
    }

    data = monarch_get("association/all", {
        "object": disease_id,
        "predicate": "biolink:has_phenotype",
        "limit": limit
    })
    return data

def get_disease_genes(disease_id, limit=100):
    """Get genes causally linked to a disease."""
    data = monarch_get("association/all", {
        "subject_category": "biolink:Gene",
        "object": disease_id,
        "predicate": "biolink:causes",
        "limit": limit
    })
    return data.get("items", [])

# MONDO disease IDs (preferred over OMIM for cross-ontology queries)
# MONDO:0007739 - Huntington disease
# MONDO:0009061 - Cystic fibrosis
# OMIM:104300 - Alzheimer disease, susceptibility to, type 1

4. 基因到表型和疾病

def get_phenotypes_for_gene(gene_id, limit=100):
    """
    Get all phenotypes associated with a gene.
    Gene IDs: HGNC:7884, NCBIGene:4137, etc.
    """
    data = monarch_get("association/all", {
        "subject": gene_id,
        "predicate": "biolink:has_phenotype",
        "limit": limit
    })
    return data.get("items", [])

def get_diseases_for_gene(gene_id, limit=100):
    """Get diseases caused by variants in a gene."""
    data = monarch_get("association/all", {
        "subject": gene_id,
        "object_category": "biolink:Disease",
        "limit": limit
    })
    return data.get("items", [])

# Example: What diseases does BRCA1 cause?
brca1_diseases = get_diseases_for_gene("HGNC:1100")
for assoc in brca1_diseases:
    print(f"  {assoc.get('object', {}).get('name')} ({assoc.get('object', {}).get('id')})")

5. HPO 术语查询

def get_hpo_term(hpo_id):
    """Fetch information about an HPO term."""
    return monarch_get(f"entity/{hpo_id}")

def search_hpo_terms(query, limit=20):
    """Search for HPO terms by name."""
    params = {
        "q": query,
        "category": "biolink:PhenotypicFeature",
        "limit": limit
    }
    return monarch_get("search", params)

# Example: look up the HPO term for seizures
seizure_term = get_hpo_term("HP:0001250")
print(f"Name: {seizure_term.get('name')}")
print(f"Definition: {seizure_term.get('description')}")

# Search for related terms
epilepsy_terms = search_hpo_terms("epilepsy")
for term in epilepsy_terms.get("items", [])[:5]:
    print(f"  {term['id']}: {term['name']}")

6. 语义相似性（疾病比较）

def compare_disease_phenotypes(disease_id_1, disease_id_2):
    """
    Compare two diseases by semantic similarity of their phenotype profiles.
    Returns similarity score using HPO hierarchy.
    """
    params = {
        "subjects": [disease_id_1],
        "objects": [disease_id_2],
        "metric": "ancestor_information_content"
    }
    return monarch_get("semsim/compare", params)

# Example: Compare Dravet syndrome with CDKL5-deficiency disorder
similarity = compare_disease_phenotypes("MONDO:0100135", "MONDO:0014917")

7. 跨物种直系同源基因

def get_orthologs(gene_id, species=None):
    """
    Get orthologs of a human gene in model organisms.
    Useful for finding animal models of human diseases.
    """
    params = {"limit": 50}
    if species:
        params["subject_taxon"] = species

    data = monarch_get("association/all", {
        "subject": gene_id,
        "predicate": "biolink:orthologous_to",
        "limit": 50
    })
    return data.get("items", [])

# NCBI Taxonomy IDs for common model organisms:
# Mouse: 10090 (Mus musculus)
# Zebrafish: 7955 (Danio rerio)
# Fruit fly: 7227 (Drosophila melanogaster)
# C. elegans: 6239
# Rat: 10116 (Rattus norvegicus)

8. 完整工作流：罕见病基因优先级排序

import requests
import pandas as pd

def rare_disease_gene_finder(patient_hpo_terms, candidate_gene_ids=None, top_n=20):
    """
    Find genes that match a patient's HPO phenotype profile.

    Args:
        patient_hpo_terms: List of HPO IDs from clinical assessment
        candidate_gene_ids: Optional list to restrict search
        top_n: Number of top candidates to return
    """
    BASE_URL = "https://api-v3.monarchinitiative.org/v3"

    # 1. Find genes associated with each phenotype
    gene_phenotype_counts = {}

    for hpo_id in patient_hpo_terms:
        data = requests.get(
            f"{BASE_URL}/association/all",
            params={
                "object": hpo_id,
                "subject_category": "biolink:Gene",
                "limit": 100
            }
        ).json()

        for item in data.get("items", []):
            gene_id = item.get("subject", {}).get("id")
            gene_name = item.get("subject", {}).get("name")
            if gene_id:
                if gene_id not in gene_phenotype_counts:
                    gene_phenotype_counts[gene_id] = {"name": gene_name, "count": 0, "phenotypes": []}
                gene_phenotype_counts[gene_id]["count"] += 1
                gene_phenotype_counts[gene_id]["phenotypes"].append(hpo_id)

    # 2. Rank by number of matching phenotypes
    ranked = sorted(gene_phenotype_counts.items(),
                    key=lambda x: -x[1]["count"])[:top_n]

    results = []
    for gene_id, info in ranked:
        results.append({
            "gene_id": gene_id,
            "gene_name": info["name"],
            "matching_phenotypes": info["count"],
            "total_patient_phenotypes": len(patient_hpo_terms),
            "phenotype_overlap": info["count"] / len(patient_hpo_terms),
            "matching_hpo_terms": info["phenotypes"]
        })

    return pd.DataFrame(results)

# Example usage
patient_phenotypes = [
    "HP:0001250",  # Seizures
    "HP:0004322",  # Short stature
    "HP:0001252",  # Hypotonia
    "HP:0000252",  # Microcephaly
    "HP:0001263",  # Global developmental delay
]
candidates = rare_disease_gene_finder(patient_phenotypes)
print(candidates[["gene_name", "matching_phenotypes", "phenotype_overlap"]].to_string())

查询工作流

工作流 1：基于 HPO 的鉴别诊断

1. 从临床记录或遗传咨询中提取 HPO 术语
2. 在 Monarch 中运行表型到基因查询
3. 按匹配表型的数量为候选基因排序
4. 与 gnomAD（约束评分）和 ClinVar（变异证据）进行交叉比对
5. 优先考虑 pLI 值较高且有已知致病变异的基因

工作流 2：疾病模型发现

1. 确定目标基因或疾病
2. 在 Monarch 中查询跨物种直系同源基因
3. 在模式生物数据库中查找表型关联
4. 确定能够重现人类疾病特征的实验模型

工作流 3：新基因的表型注释

1. 对于功能未知的基因，查询所有已知的表型关联
2. 映射到 HPO 层级结构，以了解受影响的身体系统
3. 与 OMIM 和 ORPHANET 交叉比对，查找疾病关联

常见标识符前缀

前缀	命名空间	示例
HP:	人类表型本体	HP:0001250（癫痫发作）
MONDO:	Monarch 疾病本体	MONDO:0007739
OMIM:	OMIM 疾病	OMIM:104300
ORPHANET:	Orphanet 罕见病	ORPHANET:558
HGNC:	HGNC 基因符号	HGNC:7884
NCBIGene:	NCBI 基因 ID	NCBIGene:4137
ENSEMBL:	Ensembl 基因	ENSEMBL:ENSG...
MGI:	小鼠基因	MGI:1338833
ZFIN:	斑马鱼基因	ZFIN:ZDB-GENE...

最佳实践

• 疾病使用 MONDO ID — 它们统一了 OMIM/ORPHANET/MESH 标识符
• 表型使用 HPO ID — 这是临床表型描述的标准
• 处理分页：大型查询可能需要使用 offset 参数进行迭代
• 语义相似度优于精确匹配：HPO 祖先术语可以涵盖相关表型
• 使用 ClinVar 和 OMIM 进行交叉验证：Monarch 汇总了许多来源，质量不一
• 基因使用 HGNC ID：与基因符号相比，在不同数据库版本间更加稳定

更多资源

• Monarch 门户：https://monarchinitiative.org/
• API v3 文档：https://api-v3.monarchinitiative.org/v3/docs
• HPO 浏览器：https://hpo.jax.org/
• MONDO 本体：https://mondo.monarchinitiative.org/
• 引用：Shefchek KA 等（2020），Nucleic Acids Research。PMID: 31701156
• Phenomizer（基于 HPO 的诊断）：https://compbio.charite.de/phenomizer/