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metabolomics-workbench-database

Query the NIH Metabolomics Workbench REST API for metabolites, studies, and RefMet nomenclature.

33.0k|3.2k|Updated Oct 19, 2025
Search Tags:#NMR#metabolomics#NIH#metabolites#REST API#biomarker discovery#mass spectrometry
One-click install
npx skills add https://github.com/K-Dense-AI/claude-scientific-skills --skill metabolomics-workbench-database
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: metabolomics-workbench-database
Source: https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-databases/metabolomics-workbench-database
Command: npx skills add https://github.com/K-Dense-AI/claude-scientific-skills --skill metabolomics-workbench-database

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Manually querying vast metabolomics data is slow and complex. This Skill automates access to the NIH Metabolomics Workbench, simplifying data retrieval for research and biomarker discovery. It eliminates the need for manual navigation through web interfaces, allowing researchers to focus on analysis rather than data acquisition.

Core Features & Use Cases

  • Metabolite & Study Querying: Access 4,200+ studies, retrieve metabolite structures, identifiers, and comprehensive experimental data.
  • Nomenclature Standardization: Utilize RefMet to standardize metabolite names and access hierarchical classifications, ensuring consistent data interpretation.
  • MS/NMR Data Search: Perform mass-to-charge ratio (m/z) searches with specified ion adducts and tolerance levels for efficient compound identification.
  • Use Case: Quickly find all human blood studies related to "Tyrosine" and download their experimental data for further analysis, saving hours of manual database navigation and data compilation.

Quick Start

List all available public studies

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

Find studies containing a specific metabolite (e.g., Tyrosine)

response = requests.get('https://www.metabolomicsworkbench.org/rest/study/refmet_name/Tyrosine/summary/json') print(response.json())

Frequently Asked Questions about metabolomics-workbench-database

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I programmatically query the NIH Metabolomics Workbench database for metabolite data?▼

Programmatic queries to the NIH Metabolomics Workbench REST API retrieve metabolite structures, study metadata, and analytical data using Python requests. Access endpoints by metabolite identifier (PubChem CID, InChI Key, KEGG ID, HMDB ID) or RefMet name, and filter results by species, tissue, disease, and analytical method (MS/NMR) to obtain JSON or mwtab formatted results.

Can I standardize metabolite nomenclature across my research datasets?▼

RefMet nomenclature standardization ensures consistent metabolite naming and hierarchical classification across studies. The Skill retrieves standardized metabolite identifiers and hierarchical relationships, eliminating naming ambiguity and enabling reliable cross-study comparisons and data integration.

How do I perform mass spectrometry m/z searches with ion adduct filtering?▼

Mass spectrometry searches query metabolites by mass-to-charge ratio with specified ion adducts and tolerance levels for precise compound identification. Results return matching metabolites and associated NMR/MS metadata, enabling rapid identification of unknown compounds in experimental spectra.

What's the fastest way to access data from thousands of metabolomics studies without manual web navigation?▼

Automated REST API access eliminates manual database navigation through web interfaces, allowing batch queries across 4,200+ studies to retrieve experimental data, metabolite structures, and study metadata in seconds, freeing researchers to focus on analysis rather than data acquisition.

Does this work with standard Python HTTP libraries like requests?▼

Yes, the Skill uses the requests library to connect to the REST API, requiring only standard Python HTTP functionality. No specialized client libraries are needed; simple GET requests return JSON responses for immediate parsing and analysis in Python workflows.

What input identifiers are supported for metabolite lookups?▼

Metabolite lookups accept multiple identifier types: PubChem CID, InChI Key, chemical formula, KEGG ID, HMDB ID, and RefMet name. This flexibility enables queries regardless of which identifier system your data uses, and results include cross-referenced identifiers for standardization.

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