Resources
The following list represents a small fraction of available resources for all stages of analyzing metabolomics data. It is by no means exhaustive and is meant to be a starting point for our users who are not so familiar with the metabolomics field.
Online database containing >220,000 metabolite entries including water-soluble and lipid-soluble metabolites
Database for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information
Curation of experimental MS/MS data and untargeted analysis software. May require a subscription.
Spectral library curated by Thermo
Thermo Fisher Scientific
Useful blog posts and feature requests for Thermo’s untargeted metabolomics software
Access to tutorials and other helpful information for Thermo’s TraceFinder software
Free, open-access lipidomics resource
Web-based application that uses R to perform statistical analysis of metabolomics data. Can also be used to perform peak integration)
Web-based Shiny App that is useful for quickly plotting data for multiple metabolites
Lipid mining and ontology generation)
An open-source application for integrating gene expression and metabolomics data
Online isotope pattern calculator
Examples of open-source data processing
and analysis platforms
used to convert vendor-specific files to standard format mzML or mzXML files
Open-source Windows client application for analyzing LCMS data
Untargeted analysis software. Free for academics only, works on both MAC and Windows OS
Machine learning software for metabolite ID
Competitive fragmentation modeling for metabolite ID
Metabolomics Data Repositories
The new NIH requirements for data sharing can be satisfied by uploading your raw data files to a data repository. Below is a list of repositories that are suitable for sharing raw LCMS files that contain metabolomics data.
Hosted by UCSD and sponsored by the NIH
Hosted by EMBL-EBI
Community resource developed by NIH-funded Center for Computational Mass Spectrometry. MassIVE data sets can be assigned ProteomeXchange accessions to satisfy publication requirements