The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workbench are linked to chemical structures in the metabolite structure database to facilitate comparative analysis across studies. The Metabolomics Workbench, part of the data coordinating effort of the National Institute of Health (NIH) Common Fund's Metabolomics Program, provides data from the Common Fund's Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world.
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http://dx.doi.org/10.1093/nar/gkv1042 | DOI Listing |
Sci Data
October 2024
FAFU-UCR Joint Center for Horticultural Biology and Metabolomics, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
As one of the two most ancient groups of extant vertebrates, lamprey has become an important model organism in various fields of biology. In this paper, we present a comprehensive tissue-wide spatial metabolomics dataset for lampreys, where 14 distinct tissues were analyzed using liquid chromatography-mass spectrometry (LC-MS) in both positive and negative ion modes. The dataset has been fully validated using internal standard and pooled quality control samples and is readily accessible at the UCSD Metabolomics Workbench.
View Article and Find Full Text PDFMetabolites
November 2023
Department of Oncology, Hospital Universitari Sant Joan de Reus, Institut d'Investigació Sanitària Pere Virgili (IISPV), CERCA, 43204 Reus, Spain.
Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps.
View Article and Find Full Text PDFJ Proteome Res
August 2024
Department of Chemistry, University of Kansas, 2030 Becker Drive, Lawrence, Kansas 66047, United States.
Gigascience
December 2022
University of California San Diego, Department of Bioengineering, 9500 Gilman Dr, La Jolla, CA 92093, United States.
Background: Biomedical research often involves contextual integration of multimodal and multiomic data in search of mechanisms for improved diagnosis, treatment, and monitoring. Researchers need to access information from diverse sources, comprising data in various and sometimes incongruent formats. The downstream processing of the data to decipher mechanisms by reconstructing networks and developing quantitative models warrants considerable effort.
View Article and Find Full Text PDFMetabolites
July 2023
Superfund Research Center, University of Kentucky, Lexington, KY 40536, USA.
In recent years, the FAIR guiding principles and the broader concept of open science has grown in importance in academic research, especially as funding entities have aggressively promoted public sharing of research products. Key to public research sharing is deposition of datasets into online data repositories, but it can be a chore to transform messy unstructured data into the forms required by these repositories. To help generate Metabolomics Workbench depositions, we have developed the MESSES (Metadata from Experimental SpreadSheets Extraction System) software package, implemented in the Python 3 programming language and supported on Linux, Windows, and Mac operating systems.
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