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reactome-database

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Reactome Database

Reactome数据库

Overview

概述

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.
Reactome是一个拥有2,825+条人类通路的免费、开源、人工整理的通路数据库。可通过REST API和Python客户端查询生物通路、进行过表达分析和表达分析、将基因映射到通路、探索分子互作，为系统生物学研究提供支持。

When to Use This Skill

何时使用该技能

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
在以下场景中可使用本技能：
  • 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生物通路
  • 查询特定通路信息、反应或分子互作
  • 将基因或蛋白质映射到生物通路和过程
  • 探索疾病相关通路及机制
  • 在Reactome通路浏览器中可视化分析结果
  • 开展跨物种的通路比较分析

Core Capabilities

核心功能

Reactome provides two main API services and a Python client library:
Reactome提供两种主要的API服务和一个Python客户端库：

1. Content Service - Data Retrieval

1. 内容服务 - 数据检索

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
查询和检索生物通路数据、分子互作信息以及实体信息。
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
  • 探索通路的亚细胞定位和分布
API基础URL： 
https://reactome.org/ContentService

2. Analysis Service - Pathway Analysis

2. 分析服务 - 通路分析

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
对基因列表和表达数据进行计算分析。
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意义的通路
  • 表达数据分析：分析基因表达数据集以找到相关通路
  • 物种比较：比较不同生物的通路数据
API基础URL： 
https://reactome.org/AnalysisService

3. reactome2py Python Package

3. reactome2py Python包

Python client library that wraps Reactome API calls for easier programmatic access.
Installation:
bash
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.
封装Reactome API调用的Python客户端库，便于以编程方式访问。
安装：
bash
uv pip install reactome2py
注意： reactome2py包（版本3.0.0，发布于2021年1月）目前仍可使用，但已不再积极维护。如需最新功能，建议直接调用REST API。

Querying Pathway Data

查询通路数据

Using Content Service REST API

使用内容服务REST API

The Content Service uses REST protocol and returns data in JSON or plain text formats.
Get database version:
python
import requests

response = requests.get("https://reactome.org/ContentService/data/database/version")
version = response.text
print(f"Reactome version: {version}")
Query a specific entity:
python
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:
python
import requests

event_id = "R-HSA-69278"
response = requests.get(
    f"https://reactome.org/ContentService/data/event/{event_id}/participatingPhysicalEntities"
)
molecules = response.json()
内容服务采用REST协议，返回JSON或纯文本格式的数据。
获取数据库版本：
python
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：
python
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()
获取通路中的参与分子：
python
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

使用reactome2py包

python
import reactome2py
from reactome2py import content
python
import reactome2py
from reactome2py import content

Query pathway information

Query pathway information

pathway_info = content.query_by_id("R-HSA-69278")
pathway_info = content.query_by_id("R-HSA-69278")

Get database version

Get database version

version = content.get_database_version()

**For detailed API endpoints and parameters**, refer to `references/api_reference.md` in this skill.
version = content.get_database_version()

**如需详细的API端点和参数**，请参考本技能中的`references/api_reference.md`文档。

Performing Pathway Analysis

进行通路分析

Overrepresentation Analysis

过表达分析

Submit a list of gene/protein identifiers to find enriched pathways.
Using REST API:
python
import requests
提交基因/蛋白质标识符列表以找到富集的通路。
使用REST API：
python
import requests

Prepare identifier list

Prepare identifier list

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

Submit analysis

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
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

Access pathways

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

**Retrieve analysis by token:**
```python
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：**
```python

Token is valid for 7 days

Token is valid for 7 days

response = requests.get(f"https://reactome.org/AnalysisService/token/{token}") results = response.json()
undefined
response = requests.get(f"https://reactome.org/AnalysisService/token/{token}") results = response.json()
undefined

Expression Data Analysis

表达数据分析

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:
python
import requests
分析带有定量值的基因表达数据集。
输入格式（以#开头的TSV格式表头）：
#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：
python
import requests

Read TSV file

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()
undefined
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()
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Species Projection

物种映射

Map identifiers to human pathways exclusively using the 
/projection/
endpoint:
python
response = requests.post(
    "https://reactome.org/AnalysisService/identifiers/projection/",
    headers={"Content-Type": "text/plain"},
    data=data
)
使用
/projection/
端点将标识符仅映射到人类通路：
python
response = requests.post(
    "https://reactome.org/AnalysisService/identifiers/projection/",
    headers={"Content-Type": "text/plain"},
    data=data
)

Visualizing Results

可视化结果

Analysis results can be visualized in the Reactome Pathway Browser by constructing URLs with the analysis token:
python
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}")
可通过构建包含分析令牌的URL，在Reactome通路浏览器中可视化分析结果：
python
token = result["summary"]["token"]
pathway_id = "R-HSA-69278"
url = f"https://reactome.org/PathwayBrowser/#{pathway_id}&DTAB=AN&ANALYSIS={token}"
print(f"查看结果：{url}")

Working with Analysis Tokens

分析令牌的使用

  • 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
  • 分析令牌的有效期为7天
  • 令牌可用于检索之前计算的结果，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

Data Formats and Identifiers

数据格式与标识符

Supported Identifier Types

支持的标识符类型

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.
Reactome接受多种标识符格式：
  • UniProt登录号（如：P04637）
  • 基因符号（如：TP53）
  • Ensembl ID（如：ENSG00000141510）
  • EntrezGene ID（如：7157）
  • 小分子的ChEBI ID
系统会自动检测标识符类型。

Input Format Requirements

输入格式要求

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
过表达分析：
  • 纯文本格式的标识符列表（每行一个）
  • 或TSV格式的单列数据
表达分析：
  • 以"#"开头的TSV格式表头行
  • 第1列：标识符
  • 第2列及以后：数值型表达值
  • 使用点号（.）作为小数分隔符

Output Format

输出格式

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)
所有API响应均返回JSON格式数据，包含：
  • pathways
    ：带有统计指标的富集通路数组
  • summary
    ：分析元数据和令牌
  • entities
    ：匹配和未匹配的标识符
  • 统计值：pValue、FDR（假发现率）

Helper Scripts

辅助脚本

This skill includes 
scripts/reactome_query.py
, a helper script for common Reactome operations:
bash
undefined
本技能包含
scripts/reactome_query.py
脚本，用于执行常见的Reactome操作：
bash
undefined

Query pathway information

Query pathway information

python scripts/reactome_query.py query R-HSA-69278
python scripts/reactome_query.py query R-HSA-69278

Perform overrepresentation analysis

Perform overrepresentation analysis

python scripts/reactome_query.py analyze gene_list.txt
python scripts/reactome_query.py analyze gene_list.txt

Get database version

Get database version

python scripts/reactome_query.py version
undefined
python scripts/reactome_query.py version
undefined

Additional Resources

额外资源

For comprehensive API endpoint documentation, see 
references/api_reference.md
in this skill.
如需完整的API端点文档，请参考本技能中的
references/api_reference.md
。

Current Database Statistics (Version 94, September 2025)

当前数据库统计信息（版本94，2025年9月）

  • 2,825 human pathways
  • 16,002 reactions
  • 11,630 proteins
  • 2,176 small molecules
  • 1,070 drugs
  • 41,373 literature references
  • 2,825条人类通路
  • 16,002个反应
  • 11,630个蛋白质
  • 2,176个小分子
  • 1,070种药物
  • 41,373篇文献参考