Reactome 数据库：生物通路富集分析与基因表达数据研究工具

reactome-database by davila7/claude-code-templates

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安装命令

npx skills add https://github.com/davila7/claude-code-templates --skill reactome-database

数据分析科研工具生物信息学

🇨🇳中文介绍

Reactome 数据库

概述

Reactome 是一个免费的、开源的、经过人工审阅的路径数据库，包含 2,825 条以上的人类通路。可查询生物通路、执行过表达和表达分析、将基因映射到通路、通过 REST API 和 Python 客户端探索分子相互作用，用于系统生物学研究。

何时使用此技能

此技能应在以下情况使用：

对基因或蛋白质列表进行通路富集分析
分析基因表达数据以识别相关的生物通路
查询特定的通路信息、反应或分子相互作用
将基因或蛋白质映射到生物通路和过程
探索疾病相关通路和机制
在 Reactome 通路浏览器中可视化分析结果
进行跨物种的比较通路分析

核心功能

Reactome 提供两种主要的 API 服务和一套 Python 客户端库：

1. 内容服务 - 数据检索

查询和检索生物通路数据、分子相互作用和实体信息。

常见操作：

检索通路信息和层级结构
查询特定实体（蛋白质、反应、复合物）
获取通路中的参与分子
访问数据库版本和元数据
探索通路区室和位置

API 基础 URL： https://reactome.org/ContentService

2. 分析服务 - 通路分析

对基因列表和表达数据执行计算分析。

分析类型：

过表达分析：从基因/蛋白质列表中识别具有统计学显著性的通路
表达数据分析：分析基因表达数据集以寻找相关通路
物种比较：比较不同生物体的通路数据

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3. reactome2py Python 包

包装 Reactome API 调用的 Python 客户端库，便于程序化访问。

uv pip install reactome2py

注意： reactome2py 包（版本 3.0.0，发布于 2021 年 1 月）功能正常，但未积极维护。要获得最新的功能，请考虑使用直接的 REST API 调用。

使用内容服务 REST API

内容服务使用 REST 协议，并以 JSON 或纯文本格式返回数据。

获取数据库版本：

import requests

response = requests.get("https://reactome.org/ContentService/data/database/version")
version = response.text
print(f"Reactome version: {version}")

查询特定实体：

import requests

entity_id = "R-HSA-69278"  # 示例通路 ID
response = requests.get(f"https://reactome.org/ContentService/data/query/{entity_id}")
data = response.json()

获取通路中的参与分子：

import requests

event_id = "R-HSA-69278"
response = requests.get(
    f"https://reactome.org/ContentService/data/event/{event_id}/participatingPhysicalEntities"
)
molecules = response.json()

使用 reactome2py 包

import reactome2py
from reactome2py import content

# 查询通路信息
pathway_info = content.query_by_id("R-HSA-69278")

# 获取数据库版本
version = content.get_database_version()

有关详细的 API 端点和参数，请参阅此技能中的 references/api_reference.md。

提交一个基因/蛋白质标识符列表以查找富集的通路。

使用 REST API：

import requests

# 准备标识符列表
identifiers = ["TP53", "BRCA1", "EGFR", "MYC"]
data = "\n".join(identifiers)

# 提交分析
response = requests.post(
    "https://reactome.org/AnalysisService/identifiers/",
    headers={"Content-Type": "text/plain"},
    data=data
)

result = response.json()
token = result["summary"]["token"]  # 保存令牌以便稍后检索结果

# 访问通路
for pathway in result["pathways"]:
    print(f"{pathway['stId']}: {pathway['name']} (p-value: {pathway['entities']['pValue']})")

通过令牌检索分析结果：

# 令牌有效期为 7 天
response = requests.get(f"https://reactome.org/AnalysisService/token/{token}")
results = response.json()

使用定量值分析基因表达数据集。

输入格式（带以 # 开头的标题行的 TSV）：

#Gene	Sample1	Sample2	Sample3
TP53	2.5	3.1	2.8
BRCA1	1.2	1.5	1.3
EGFR	4.5	4.2	4.8

提交表达数据：

import requests

# 读取 TSV 文件
with open("expression_data.tsv", "r") as f:
    data = f.read()

response = requests.post(
    "https://reactome.org/AnalysisService/identifiers/",
    headers={"Content-Type": "text/plain"},
    data=data
)

result = response.json()

使用 /projection/ 端点将标识符专门映射到人类通路：

response = requests.post(
    "https://reactome.org/AnalysisService/identifiers/projection/",
    headers={"Content-Type": "text/plain"},
    data=data
)

分析结果可以通过构建包含分析令牌的 URL 在 Reactome 通路浏览器中可视化：

token = result["summary"]["token"]
pathway_id = "R-HSA-69278"
url = f"https://reactome.org/PathwayBrowser/#{pathway_id}&DTAB=AN&ANALYSIS={token}"
print(f"View results: {url}")

分析令牌有效期为 7 天
令牌允许检索先前计算的结果而无需重新提交
存储令牌以便在不同会话中访问结果
使用 GET /token/{TOKEN} 端点检索结果

数据格式和标识符

支持的标识符类型

Reactome 接受各种标识符格式：

UniProt 登录号（例如，P04637）
基因符号（例如，TP53）
Ensembl ID（例如，ENSG00000141510）
EntrezGene ID（例如，7157）
小分子的 ChEBI ID

系统会自动检测标识符类型。

对于过表达分析：

标识符的纯文本列表（每行一个）
或 TSV 格式的单列

对于表达分析：

TSV 格式，必须包含以 "#" 开头的标题行
第 1 列：标识符
第 2 列及以后：数值型表达值
使用句点 (.) 作为小数点分隔符

所有 API 响应都返回包含以下内容的 JSON：

pathways：包含统计指标的富集通路数组
summary：分析元数据和令牌
entities：匹配和未映射的标识符
统计值：pValue、FDR（错误发现率）

此技能包含 scripts/reactome_query.py，一个用于常见 Reactome 操作的辅助脚本：

# 查询通路信息
python scripts/reactome_query.py query R-HSA-69278

# 执行过表达分析
python scripts/reactome_query.py analyze gene_list.txt

# 获取数据库版本
python scripts/reactome_query.py version

有关全面的 API 端点文档，请参阅此技能中的 references/api_reference.md。

当前数据库统计信息（版本 94，2025 年 9 月）

2,825 条人类通路
16,002 个反应
11,630 种蛋白质
2,176 个小分子
1,070 种药物
41,373 篇文献引用

🇺🇸English

Reactome Database

Overview

Reactome is a free, open-source, curated pathway database with 2,825+ human pathways. Query biological pathways, perform overrepresentation and expression analysis, map genes to pathways, explore molecular interactions via REST API and Python client for systems biology research.

When to Use This Skill

This skill should be used when:

Performing pathway enrichment analysis on gene or protein lists
Analyzing gene expression data to identify relevant biological pathways
Querying specific pathway information, reactions, or molecular interactions
Mapping genes or proteins to biological pathways and processes
Exploring disease-related pathways and mechanisms
Visualizing analysis results in the Reactome Pathway Browser
Conducting comparative pathway analysis across species

Core Capabilities

Reactome provides two main API services and a Python client library:

1. Content Service - Data Retrieval

Query and retrieve biological pathway data, molecular interactions, and entity information.

Common operations:

Retrieve pathway information and hierarchies
Query specific entities (proteins, reactions, complexes)
Get participating molecules in pathways
Access database version and metadata
Explore pathway compartments and locations

API Base URL: https://reactome.org/ContentService

2. Analysis Service - Pathway Analysis

Perform computational analysis on gene lists and expression data.

Analysis types:

Overrepresentation Analysis : Identify statistically significant pathways from gene/protein lists
Expression Data Analysis : Analyze gene expression datasets to find relevant pathways
Species Comparison : Compare pathway data across different organisms

API Base URL: https://reactome.org/AnalysisService

3. reactome2py Python Package

Python client library that wraps Reactome API calls for easier programmatic access.

Installation:

uv pip install reactome2py

Note: The reactome2py package (version 3.0.0, released January 2021) is functional but not actively maintained. For the most up-to-date functionality, consider using direct REST API calls.

Querying Pathway Data

Using Content Service REST API

The Content Service uses REST protocol and returns data in JSON or plain text formats.

Get database version:

import requests

response = requests.get("https://reactome.org/ContentService/data/database/version")
version = response.text
print(f"Reactome version: {version}")

Query a specific entity:

import requests

entity_id = "R-HSA-69278"  # Example pathway ID
response = requests.get(f"https://reactome.org/ContentService/data/query/{entity_id}")
data = response.json()

Get participating molecules in a pathway:

import requests

event_id = "R-HSA-69278"
response = requests.get(
    f"https://reactome.org/ContentService/data/event/{event_id}/participatingPhysicalEntities"
)
molecules = response.json()

Using reactome2py Package

import reactome2py
from reactome2py import content

# Query pathway information
pathway_info = content.query_by_id("R-HSA-69278")

# Get database version
version = content.get_database_version()

For detailed API endpoints and parameters , refer to references/api_reference.md in this skill.

Performing Pathway Analysis

Overrepresentation Analysis

Submit a list of gene/protein identifiers to find enriched pathways.

Using REST API:

import requests

# Prepare identifier list
identifiers = ["TP53", "BRCA1", "EGFR", "MYC"]
data = "\n".join(identifiers)

# Submit analysis
response = requests.post(
    "https://reactome.org/AnalysisService/identifiers/",
    headers={"Content-Type": "text/plain"},
    data=data
)

result = response.json()
token = result["summary"]["token"]  # Save token to retrieve results later

# Access pathways
for pathway in result["pathways"]:
    print(f"{pathway['stId']}: {pathway['name']} (p-value: {pathway['entities']['pValue']})")

Retrieve analysis by token:

# Token is valid for 7 days
response = requests.get(f"https://reactome.org/AnalysisService/token/{token}")
results = response.json()

Expression Data Analysis

Analyze gene expression datasets with quantitative values.

Input format (TSV with header starting with #):

#Gene	Sample1	Sample2	Sample3
TP53	2.5	3.1	2.8
BRCA1	1.2	1.5	1.3
EGFR	4.5	4.2	4.8

Submit expression data:

import requests

# Read TSV file
with open("expression_data.tsv", "r") as f:
    data = f.read()

response = requests.post(
    "https://reactome.org/AnalysisService/identifiers/",
    headers={"Content-Type": "text/plain"},
    data=data
)

result = response.json()

Species Projection

Map identifiers to human pathways exclusively using the /projection/ endpoint:

response = requests.post(
    "https://reactome.org/AnalysisService/identifiers/projection/",
    headers={"Content-Type": "text/plain"},
    data=data
)

Visualizing Results

Analysis results can be visualized in the Reactome Pathway Browser by constructing URLs with the analysis token:

token = result["summary"]["token"]
pathway_id = "R-HSA-69278"
url = f"https://reactome.org/PathwayBrowser/#{pathway_id}&DTAB=AN&ANALYSIS={token}"
print(f"View results: {url}")

Working with Analysis Tokens

Analysis tokens are valid for 7 days
Tokens allow retrieval of previously computed results without re-submission
Store tokens to access results across sessions
Use GET /token/{TOKEN} endpoint to retrieve results

Data Formats and Identifiers

Supported Identifier Types

Reactome accepts various identifier formats:

UniProt accessions (e.g., P04637)
Gene symbols (e.g., TP53)
Ensembl IDs (e.g., ENSG00000141510)
EntrezGene IDs (e.g., 7157)
ChEBI IDs for small molecules

The system automatically detects identifier types.

Input Format Requirements

For overrepresentation analysis:

Plain text list of identifiers (one per line)
OR single column in TSV format

For expression analysis:

TSV format with mandatory header row starting with "#"
Column 1: identifiers
Columns 2+: numeric expression values
Use period (.) as decimal separator

Output Format

All API responses return JSON containing:

pathways: Array of enriched pathways with statistical metrics
summary: Analysis metadata and token
entities: Matched and unmapped identifiers
Statistical values: pValue, FDR (false discovery rate)

Helper Scripts

This skill includes scripts/reactome_query.py, a helper script for common Reactome operations:

# Query pathway information
python scripts/reactome_query.py query R-HSA-69278

# Perform overrepresentation analysis
python scripts/reactome_query.py analyze gene_list.txt

# Get database version
python scripts/reactome_query.py version

Additional Resources

API Documentation : https://reactome.org/dev
User Guide : https://reactome.org/userguide
Documentation Portal : https://reactome.org/documentation
Data Downloads : https://reactome.org/download-data
reactome2py Docs : https://reactome.github.io/reactome2py/

For comprehensive API endpoint documentation, see references/api_reference.md in this skill.

Current Database Statistics (Version 94, September 2025)

2,825 human pathways
16,002 reactions
11,630 proteins
2,176 small molecules
1,070 drugs
41,373 literature references

Weekly Installs

126

Repository

davila7/claude-…emplates

GitHub Stars

22.6K

First Seen

Jan 21, 2026

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