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Related Experiment Video

Updated: Mar 23, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

MetaboLights: An Open-Access Database Repository for Metabolomics Data.

Namrata S Kale¹, Kenneth Haug¹, Pablo Conesa¹

¹European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, United Kingdom.

Current Protocols in Bioinformatics

|March 25, 2016

Summary

MetaboLights is a comprehensive, open-access database for metabolomics research, offering standardized data submission and retrieval. It facilitates data sharing and reproducibility in cross-platform and cross-species studies.

Keywords:

cross-platform cross-species metabolites metabolomics metadata open-access raw data

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Published on: March 14, 2013

Area of Science:

Bioinformatics
Metabolomics
Data Science

Background:

Metabolomics research generates large datasets requiring standardized storage and access.
Existing repositories may lack cross-platform and cross-species compatibility.
The Metabolomics Standards Initiative (MSI) promotes data standardization.

Purpose of the Study:

To introduce MetaboLights as a general-purpose, open-access repository for metabolomics data.
To provide a user-friendly platform for data submission and retrieval.
To ensure compliance with Metabolomics Standards Initiative (MSI) standards.

Main Methods:

Utilizes the open-source ISA framework for data organization.
Provides tools for uploading and assigning unique identifiers to study datasets.
Manually curates data and associates metabolites with studies and pathways.

Main Results:

MetaboLights offers MSI-compliant metadata and raw experimental data.
Study datasets are assigned unique identifiers (e.g., MTBLS1) for citation.
The Reference Layer includes structural, chemical, spectral, and pathway information.

Conclusions:

MetaboLights serves as a valuable resource for the metabolomics community.
The repository supports data sharing, reproducibility, and discovery.
It is recommended by journals for metabolomics data deposition.