Metabolomics Workbench Database

Metabolomics Workbench Database

Overview

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).

When to Use This Skill

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.

Core Capabilities

1. Querying Metabolite Structures and Data

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:

pythonCopy code
import requests

# Get compound information by PubChem CID
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/pubchem_cid/5281365/all/json')

# Download molecular structure as PNG
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/png')

# Get compound name by registry number
response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/11/name/json')

2. Accessing Study Metadata and Experimental Results

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:

pythonCopy code
# List all available public studies
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST/available/json')

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

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

# Find studies containing a specific metabolite
response = requests.get('https://www.metabolomicsworkbench.org/rest/study/refmet_name/Tyrosine/summary/json')

3. Standardizing Metabolite Nomenclature with RefMet

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:

pythonCopy code
# Standardize a metabolite name
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/match/citrate/name/json')

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

# Get all metabolites in a specific class
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/main_class/Fatty%20Acids/all/json')

# Retrieve complete RefMet database
response = requests.get('https://www.metabolomicsworkbench.org/rest/refmet/all/json')

4. Performing Mass Spectrometry Searches

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:

pythonCopy code
# 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')

# 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')

# Search across RefMet database
response = requests.get('https://www.metabolomicsworkbench.org/rest/moverz/REFMET/200.15/M-H/0.3/json')

5. Filtering Studies by Analytical and Biological Parameters

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:

pythonCopy code
# 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')

# Find all human blood studies containing tyrosine
response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/;;;Human;Blood;;;Tyrosine/json')

# Filter by analytical method only
response = requests.get('https://www.metabolomicsworkbench.org/rest/metstat/GCMS;;;;;;/json')

6. Accessing Gene and Protein Information

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:

pythonCopy code
# Get gene information by symbol
response = requests.get('https://www.metabolomicsworkbench.org/rest/gene/gene_symbol/ACACA/all/json')

# Retrieve protein data by UniProt ID
response = requests.get('https://www.metabolomicsworkbench.org/rest/protein/uniprot_id/Q13085/all/json')

Common Workflows

Workflow 1: Finding Studies for a Specific Metabolite

To find all studies containing measurements of a specific metabolite:

1. First standardize the metabolite name using RefMet:

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

2. Use the standardized name to search for studies:

   pythonCopy code
   response = requests.get('https://www.metabolomicsworkbench.org/rest/study/refmet_name/Glucose/summary/json')
   

3. Retrieve experimental data from specific studies:

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

Workflow 2: Identifying Compounds from MS Data

To identify potential compounds from mass spectrometry m/z values:

1. Perform m/z search with appropriate adduct and tolerance:

   pythonCopy code
   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:

   pythonCopy code
   response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/{regno}/all/json')
   

4. Download structures for confirmation:

   pythonCopy code
   response = requests.get('https://www.metabolomicsworkbench.org/rest/compound/regno/{regno}/png')
   

Workflow 3: Exploring Disease-Specific Metabolomics

To find metabolomics studies for a specific disease and analytical platform:

1. Use MetStat to filter studies:

   pythonCopy code
   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:

   pythonCopy code
   response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST{ID}/summary/json')
   

4. Retrieve complete experimental data:

   pythonCopy code
   response = requests.get('https://www.metabolomicsworkbench.org/rest/study/study_id/ST{ID}/data/json')
   

Output Formats

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.

Best Practices

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

Resources

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.