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National Metabolomics Data Repository
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StudiesAs of 12/01/21 a total of 1927 studies have been processed by the National Metabolomics Data Repository (NMDR). There are 1638 publicly available studies and the remainder (289) will be made available subject to their embargo dates.
Recently released studies on NMDR
ST001958 - Data on changes in lipid profiles during differentiation and maturation of human subcutaneous white adipocytes analyzed using chromatographic and bioinformatics tools; Homo sapiens; Hamamatsu University School of Medicine
ST001780 - Comparative analysis of metabolomic profiles in cerebrospinal fluid before and after endurance exercise; Homo sapiens; UCSD School of Medicine
ST001974 - Anti-oxidative metabolism measurement in mammalian cells and tissues by quantitative LC/MS method (I); Mus musculus; Boston Children's Hospital, Harvard Medical School
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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
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Lipid Notation in RefMet and lipid m/z calculation tools
View table of over 170 revised lipid abbreviations covered by RefMet, including structure examples and m/z calculation tools for a variety of adducts.
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Convert your metabolite name to standardized nomenclature via RefMet
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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.
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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.
