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Metabolomics Workbench Database

代谢组学工作台数据库

Overview

概述

The Metabolomics Workbench is a comprehensive NIH Common Fund-sponsored platform hosted at UCSD that serves as the primary repository for metabolomics research data. It provides programmatic access to over 4,200 processed studies (3,790+ publicly available), standardized metabolite nomenclature through RefMet, and powerful search capabilities across multiple analytical platforms (GC-MS, LC-MS, NMR).
代谢组学工作台是由NIH共同基金资助、加州大学圣地亚哥分校（UCSD）托管的综合性平台，是代谢组学研究数据的主要存储库。它提供对超过4200项已处理研究（其中3790+项可公开获取）的程序化访问，通过RefMet提供标准化的代谢物命名规范，并支持跨多种分析平台（GC-MS、LC-MS、NMR）的强大搜索功能。

When to Use This Skill

何时使用该技能

This skill should be used when querying metabolite structures, accessing study data, standardizing nomenclature, performing mass spectrometry searches, or retrieving gene/protein-metabolite associations through the Metabolomics Workbench REST API.
当你需要通过代谢组学工作台REST API查询代谢物结构、访问研究数据、标准化命名规范、执行质谱搜索，或检索基因/蛋白质-代谢物关联信息时，可使用该技能。

Core Capabilities

核心功能

1. Querying Metabolite Structures and Data

1. 查询代谢物结构与数据

Access comprehensive metabolite information including structures, identifiers, and cross-references to external databases.
Key operations:
  • Retrieve compound data by various identifiers (PubChem CID, InChI Key, KEGG ID, HMDB ID, etc.)
  • Download molecular structures as MOL files or PNG images
  • Access standardized compound classifications
  • Cross-reference between different metabolite databases
Example queries:
python
import requests
获取全面的代谢物信息，包括结构、标识符以及与外部数据库的交叉引用。
关键操作：
  • 通过多种标识符（PubChem CID、InChI Key、KEGG ID、HMDB ID等）检索化合物数据
  • 以MOL文件或PNG图片格式下载分子结构
  • 获取标准化的化合物分类
  • 在不同代谢物数据库之间进行交叉引用
示例查询：
python
import requests

Get compound information by PubChem CID

Get compound information by PubChem CID

response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/pubchem_cid/5281365/all/json')
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/pubchem_cid/5281365/all/json')

Download molecular structure as PNG

Download molecular structure as PNG

response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/png')
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/png')

Get compound name by registry number

Get compound name by registry number

response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/name/json')
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response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/name/json')
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2. Accessing Study Metadata and Experimental Results

2. 访问研究元数据与实验结果

Query metabolomics studies by various criteria and retrieve complete experimental datasets.
Key operations:
  • Search studies by metabolite, institute, investigator, or title
  • Access study summaries, experimental factors, and analysis details
  • Retrieve complete experimental data in various formats
  • Download mwTab format files for complete study information
  • Query untargeted metabolomics data
Example queries:
python
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通过多种条件查询代谢组学研究，并获取完整的实验数据集。
关键操作：
  • 按代谢物、机构、研究者或标题搜索研究
  • 获取研究摘要、实验因素和分析细节
  • 以多种格式检索完整的实验数据
  • 下载mwTab格式文件以获取完整研究信息
  • 查询非靶向代谢组学数据
示例查询：
python
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List all available public studies

List all available public studies

response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST/available/json')
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST/available/json')

Get study summary

Get study summary

response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/summary/json')
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/summary/json')

Retrieve experimental data

Retrieve experimental data

response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/data/json')
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/data/json')

Find studies containing a specific metabolite

Find studies containing a specific metabolite

response = requests.get('https://www.metabolomicsworkbench.org/rest/study/refmet_name/Tyrosine/summary/json')
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response = requests.get('https://www.metabolomicsworkbench.org/rest/study/refmet_name/Tyrosine/summary/json')
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3. Standardizing Metabolite Nomenclature with RefMet

3. 使用RefMet标准化代谢物命名

Use the RefMet database to standardize metabolite names and access systematic classification across four structural resolution levels.
Key operations:
  • Match common metabolite names to standardized RefMet names
  • Query by chemical formula, exact mass, or InChI Key
  • Access hierarchical classification (super class, main class, sub class)
  • Retrieve all RefMet entries or filter by classification
Example queries:
python
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利用RefMet数据库标准化代谢物名称，并获取四个结构分辨率级别的系统分类。
关键操作：
  • 将常见代谢物名称匹配为标准化的RefMet名称
  • 按化学式、精确质量或InChI Key查询
  • 获取层级分类（超类、主类、子类）
  • 检索所有RefMet条目或按分类筛选
示例查询：
python
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Standardize a metabolite name

Standardize a metabolite name

response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/match/citrate/name/json')
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/match/citrate/name/json')

Query by molecular formula

Query by molecular formula

response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/formula/C12H24O2/all/json')
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/formula/C12H24O2/all/json')

Get all metabolites in a specific class

Get all metabolites in a specific class

response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/main_class/Fatty%20Acids/all/json')
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/main_class/Fatty%20Acids/all/json')

Retrieve complete RefMet database

Retrieve complete RefMet database

response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/all/json')
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response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/all/json')
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4. Performing Mass Spectrometry Searches

4. 执行质谱搜索

Search for compounds by mass-to-charge ratio (m/z) with specified ion adducts and tolerance levels.
Key operations:
  • Search precursor ion masses across multiple databases (Metabolomics Workbench, LIPIDS, RefMet)
  • Specify ion adduct types (M+H, M-H, M+Na, M+NH4, M+2H, etc.)
  • Calculate exact masses for known metabolites with specific adducts
  • Set mass tolerance for flexible matching
Example queries:
python
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按质荷比（m/z）搜索化合物，可指定离子加合物和容差级别。
关键操作：
  • 跨多个数据库（代谢组学工作台、LIPIDS、RefMet）搜索前体离子质量
  • 指定离子加合物类型（M+H、M-H、M+Na、M+NH4、M+2H等）
  • 计算已知代谢物与特定加合物的精确质量
  • 设置质量容差以实现灵活匹配
示例查询：
python
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Search by m/z value with M+H adduct

Search by m/z value with M+H adduct

response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/MB/635.52/M+H/0.5/json')
response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/MB/635.52/M+H/0.5/json')

Calculate exact mass for a metabolite with specific adduct

Calculate exact mass for a metabolite with specific adduct

response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/exactmass/PC(34:1)/M+H/json')
response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/exactmass/PC(34:1)/M+H/json')

Search across RefMet database

Search across RefMet database

response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/REFMET/200.15/M-H/0.3/json')
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response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/REFMET/200.15/M-H/0.3/json')
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5. Filtering Studies by Analytical and Biological Parameters

5. 按分析和生物学参数筛选研究

Use the MetStat context to find studies matching specific experimental conditions.
Key operations:
  • Filter by analytical method (LCMS, GCMS, NMR)
  • Specify ionization polarity (POSITIVE, NEGATIVE)
  • Filter by chromatography type (HILIC, RP, GC)
  • Target specific species, sample sources, or diseases
  • Combine multiple filters using semicolon-delimited format
Example queries:
python
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使用MetStat上下文查找符合特定实验条件的研究。
关键操作：
  • 按分析方法（LCMS、GCMS、NMR）筛选
  • 指定电离极性（POSITIVE、NEGATIVE）
  • 按色谱类型（HILIC、RP、GC）筛选
  • 针对特定物种、样本来源或疾病
  • 使用分号分隔格式组合多个筛选条件
示例查询：
python
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Find human blood studies on diabetes using LC-MS

Find human blood studies on diabetes using LC-MS

response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/LCMS;POSITIVE;HILIC;Human;Blood;Diabetes/json')
response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/LCMS;POSITIVE;HILIC;Human;Blood;Diabetes/json')

Find all human blood studies containing tyrosine

Find all human blood studies containing tyrosine

response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/;;;Human;Blood;;;Tyrosine/json')
response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/;;;Human;Blood;;;Tyrosine/json')

Filter by analytical method only

Filter by analytical method only

response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/GCMS;;;;;;/json')
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response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/GCMS;;;;;;/json')
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6. Accessing Gene and Protein Information

6. 访问基因与蛋白质信息

Retrieve gene and protein data associated with metabolic pathways and metabolite metabolism.
Key operations:
  • Query genes by symbol, name, or ID
  • Access protein sequences and annotations
  • Cross-reference between gene IDs, RefSeq IDs, and UniProt IDs
  • Retrieve gene-metabolite associations
Example queries:
python
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检索与代谢通路和代谢物代谢相关的基因和蛋白质数据。
关键操作：
  • 按符号、名称或ID查询基因
  • 获取蛋白质序列和注释
  • 在基因ID、RefSeq ID和UniProt ID之间进行交叉引用
  • 检索基因-代谢物关联信息
示例查询：
python
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Get gene information by symbol

Get gene information by symbol

response = requests.get('https://www.metabolomicsworkbench.org/rest/gene/gene_symbol/ACACA/all/json')
response = requests.get('https://www.metabolomicsworkbench.org/rest/gene/gene_symbol/ACACA/all/json')

Retrieve protein data by UniProt ID

Retrieve protein data by UniProt ID

response = requests.get('https://www.metabolomicsworkbench.org/rest/protein/uniprot_id/Q13085/all/json')
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response = requests.get('https://www.metabolomicsworkbench.org/rest/protein/uniprot_id/Q13085/all/json')
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Common Workflows

常见工作流

Workflow 1: Finding Studies for a Specific Metabolite

工作流1：查找特定代谢物相关研究

To find all studies containing measurements of a specific metabolite:
  1. First standardize the metabolite name using RefMet:
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/match/glucose/name/json')
  2. Use the standardized name to search for studies:
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/study/refmet_name/Glucose/summary/json')
  3. Retrieve experimental data from specific studies:
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/data/json')
要查找所有包含特定代谢物测量数据的研究：
  1. 首先使用RefMet标准化代谢物名称：
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/match/glucose/name/json')
  2. 使用标准化名称搜索研究：
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/study/refmet_name/Glucose/summary/json')
  3. 从特定研究中检索实验数据：
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/data/json')

Workflow 2: Identifying Compounds from MS Data

工作流2：从质谱数据中识别化合物

To identify potential compounds from mass spectrometry m/z values:
  1. Perform m/z search with appropriate adduct and tolerance:
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/MB/180.06/M+H/0.5/json')
  2. Review candidate compounds from results
  3. Retrieve detailed information for candidate compounds:
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/{regno}/all/json')
  4. Download structures for confirmation:
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/{regno}/png')
要从质谱m/z值中识别潜在化合物：
  1. 使用合适的加合物和容差执行m/z搜索：
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/MB/180.06/M+H/0.5/json')
  2. 查看结果中的候选化合物
  3. 检索候选化合物的详细信息：
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/{regno}/all/json')
  4. 下载结构以确认：
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/{regno}/png')

Workflow 3: Exploring Disease-Specific Metabolomics

工作流3：探索疾病特异性代谢组学

To find metabolomics studies for a specific disease and analytical platform:
  1. Use MetStat to filter studies:
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/LCMS;POSITIVE;;Human;;Cancer/json')
  2. Review study IDs from results
  3. Access detailed study information:
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST{ID}/summary/json')
  4. Retrieve complete experimental data:
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST{ID}/data/json')
要查找特定疾病和分析平台的代谢组学研究：
  1. 使用MetStat筛选研究：
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/LCMS;POSITIVE;;Human;;Cancer/json')
  2. 查看结果中的研究ID
  3. 获取详细的研究信息：
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST{ID}/summary/json')
  4. 检索完整的实验数据：
    python
    response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST{ID}/data/json')

Output Formats

输出格式

The API supports two primary output formats:
  • JSON (default): Machine-readable format, ideal for programmatic access
  • TXT: Human-readable tab-delimited text format
Specify format by appending 
/json
or 
/txt
to API URLs. When format is omitted, JSON is returned by default.
API支持两种主要输出格式：
  • JSON（默认）：机器可读格式，适合程序化访问
  • TXT：人类可读的制表符分隔文本格式
通过在API URL末尾添加
/json
或
/txt
指定格式。若省略格式，默认返回JSON。

Best Practices

最佳实践

  1. Use RefMet for standardization: Always standardize metabolite names through RefMet before searching studies to ensure consistent nomenclature
  2. Specify appropriate adducts: When performing m/z searches, use the correct ion adduct type for your analytical method (e.g., M+H for positive mode ESI)
  3. Set reasonable tolerances: Use appropriate mass tolerance values (typically 0.5 Da for low-resolution, 0.01 Da for high-resolution MS)
  4. Cache reference data: Consider caching frequently used reference data (RefMet database, compound information) to minimize API calls
  5. Handle pagination: For large result sets, be prepared to handle multiple data structures in responses
  6. Validate identifiers: Cross-reference metabolite identifiers across multiple databases when possible to ensure correct compound identification
  1. 使用RefMet进行标准化：在搜索研究前，始终通过RefMet标准化代谢物名称，以确保命名规范一致
  2. 指定合适的加合物：执行m/z搜索时，为你的分析方法使用正确的离子加合物类型（例如，正模式ESI使用M+H）
  3. 设置合理的容差：使用合适的质量容差值（低分辨率质谱通常为0.5 Da，高分辨率质谱为0.01 Da）
  4. 缓存参考数据：考虑缓存频繁使用的参考数据（RefMet数据库、化合物信息）以减少API调用次数
  5. 处理分页：对于大型结果集，准备好处理响应中的多个数据结构
  6. 验证标识符：尽可能跨多个数据库交叉引用代谢物标识符，以确保化合物识别正确

Resources

资源

references/

references/

Detailed API reference documentation is available in 
references/api_reference.md
, including:
  • Complete REST API endpoint specifications
  • All available contexts (compound, study, refmet, metstat, gene, protein, moverz)
  • Input/output parameter details
  • Ion adduct types for mass spectrometry
  • Additional query examples
Load this reference file when detailed API specifications are needed or when working with less common endpoints.
详细的API参考文档可在
references/api_reference.md
中获取，包括：
  • 完整的REST API端点规范
  • 所有可用上下文（compound、study、refmet、metstat、gene、protein、moverz）
  • 输入/输出参数详情
  • 质谱的离子加合物类型
  • 更多查询示例
当需要详细的API规范或处理不太常用的端点时，加载此参考文件。