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National Metabolomics Data Repository
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StudiesAs of 11/30/22 a total of 2290 studies have been processed by the National Metabolomics Data Repository (NMDR). There are 1985 publicly available studies and the remainder (305) will be made available subject to their embargo dates.
Recently released studies on NMDR
ST001614 - NMR metabolomics analysis of ricin-induced and fasting hypoglycemia (part-I); Mus musculus; Montana State University
ST001667 - NMR metabolomics analysis of ricin-induced and fasting hypoglycemia (part-II); Mus musculus; Montana State University
ST002341 - Metabolomic analysis of colorectal cancer cells using mass spectrometry; Homo sapiens; Nanjing Medical University
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Metabolite Structure Database
Updates to the Metabolite Structure Database (February 2, 2022)
The updated Metabolite structure database of primary and secondary metabolites at the Metabolomics Workbench contains new substructure and text-based searches including by chemical class. Over 164,000 structures have been added including over 10,000 sterols.
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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.
