• National Metabolomics Data Repository

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    As of 02/23/24 a total of 3021 studies have been processed by the National Metabolomics Data Repository (NMDR). There are 2689 publicly available studies and the remainder (332) will be made available subject to their embargo dates.

    Recently released studies on NMDR

    ST002836 - Bacterial tryptophan metabolites increased by prebiotic galactooligosaccharide reduce microglial reactivity and are associated with lower anxiety-like behavior (Intestine); Mus musculus; National Center for Advancing Translational Sciences

    ST002837 - Bacterial tryptophan metabolites increased by prebiotic galactooligosaccharide reduce microglial reactivity and are associated with lower anxiety-like behavior (Blood); Mus musculus; National Center for Advancing Translational Sciences

    ST003092 - Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data; Homo sapiens; Korea Advanced Institute of Science and Technology (KAIST)

New!MetStat: View most frequently encountered metabolites in NMDR (mapped to RefMet)

  • RefMet name harmonization combined with ion-adduct calculation

    RefMet ion-adduct calculation (Nov 20, 2023)

    The RefMet metabolite name harmonization resource leverages data from over 500,000 annotations obtained from MS and NMR studies in NMDR to provide a standardized reference nomenclature across 4 different levels of structural resolution. A new feature is the ability to map a list of metabolite names to RefMet and simultaneously perform an ion-adduct calculation to generate a list of m/z values.

    Higlights/News archive

  • Core structures in RefMet classification

    RefMet core structures (March 1, 2023)

    Browse and search core structures associated with the RefMet classification system. For example, what are "Flavones", "Flavanols", "Flavonols" and "Flavanones" and what's the difference? The RefMet classification hierarchy has recently been updated to place more emphasis on biosynthetic considerations for Alkaloid, Polyketide and Prenol lipid super classes.

    Higlights/News archive

  • Molecular structure similarity analysis

    Create molecular structure similarity networks (February 3, 2023)

    This Structure similarity network tool creates a network map from a list of metabolite names (up to 500) by selecting a fingerprint type (MACCSkeys, Chem.RDK, Topological, Morgan,MorganBitVector) and similarity method (Tanimoto, Dice) with a similarity coefficient cutoff. This feature is also implemented for each NMDR study containing named metabolites (in 'Perform statisctical analysis' section). This application uses the Python-based Rdkit.

    Higlights/News archive

  • Searching untargeted LC-MS data

    Searching untargeted LC-MS data on the Workbench (December 13, 2022)

    This portal searches over 4.5 million m/z,retention time features from over 890 NMDR studies and over 1500 LC-MS analyses. Search with a m/z value and tolerance window and optionally specify a retention time value and tolerance window to restrict the search. Limit search to studies by sample source and/or species, and also by chromatography type, MS instrument and polarity. Features that have been identified by submitters will appear in the "Name" column in the results table.

    Higlights/News archive

  • Correlated network graphs in NMDR

    Correlated network graphs using Debiased Sparse Partial Correlation (DSPC)

    The Metabolomics Workbench has released a new graphical tool for estimating and visualizing partial correlation networks in NMDR studies. It uses the Debiased Sparse Partial Correlation algorithm (DSPC) developed at U.Michigan. Nodes may be mapped to chemical classification or fold-change. Study example: See "Perform Network analysis on correlated metabolites" links here

    Higlights/News archive

  • Exemplary Studies

    A list of exemplary studies are listed here which adhere to the submission guidelines of Metabolomics Workbench. Specifically, publically available studies having all or most of the features below were identified as exemplary studies.

    • Well-written study summary
    • Detailed metadata for collection/treatment/chromatography/MS/NMR, etc.
    • Post-processing details
    • Presence of control samples
    • Raw data availability for samples and controls
    • One-to-one mapping of sample names to raw data file name
    • Internal standards (with measurements)
    • Clear and organized metabolite annotations

    These include different analysis (GC-MS, LC-MS, NMR) and species type. We recommend looking at these studies as a model example before submitting to Metabolomics Workbench.

  • NMDR studies and Jupyter Notebooks

    Analyze Workbench studies via Python-based Jupyter Notebooks. Launch notebooks on Binder or download notebooks from GitHub and run them locally.

NIH Common Fund Stage 2 Metabolomics Consortium Centers
Metabolomics Consortium Coordinating Center (M3C)
Richard Yost, U. of Florida
Metabolomics Workbench/NMDR
Shankar Subramaniam, UC San Diego
(this website)
Compound Identification Cores (CIDCs)
Arthur Edison, U. of Georgia
Alexey Nesvizhskii, U. of Michigan
Oliver Fiehn, UC Davis
Dean Paul Jones, Emory University
Thomas Metz, Pacific Northwest Nat. Lab.
Data and Tools Cores (DTCs)
John Weinstein, MD Anderson Cancer C.
Jamey Young, Vanderbilt University
Xiuxia Du, U. of North Carolina Charlotte
Shuzhao Li, Emory University
Alla Karnovsky, U. of Michigan
Katerina Kechris, U. of Colorado, Denver
Gary Patti, Washington U. at St. Louis

Please cite:Metabolomics WorkbenchYou will get more info on how to cite here