metabolomics-workbench-database

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• SKILL.md
• api_reference.md · references/
• demo.cpython-313.pyc · scripts/__pycache__/
• demo.py · scripts/

SKILL.md (first lines)

# 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:**
```python
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:**
```python
# 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:**
```python
# 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:**
```python
# 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

From lamm-mit/scienceclaw, Apache-2.0.

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