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Found 9 result(s)
With the creation of the Metabolomics Data Repository managed by Data Repository and Coordination Center (DRCC), the NIH acknowledges the importance of data sharing for metabolomics. Metabolomics represents the systematic study of low molecular weight molecules found in a biological sample, providing a "snapshot" of the current and actual state of the cell or organism at a specific point in time. Thus, the metabolome represents the functional activity of biological systems. As with other ‘omics’, metabolites are conserved across animals, plants and microbial species, facilitating the extrapolation of research findings in laboratory animals to humans. Common technologies for measuring the metabolome include mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR), which can measure hundreds to thousands of unique chemical entities. Data sharing in metabolomics will include primary raw data and the biological and analytical meta-data necessary to interpret these data. Through cooperation between investigators, metabolomics laboratories and data coordinating centers, these data sets should provide a rich resource for the research community to enhance preclinical, clinical and translational research.
MetabolomeXchange.org delivers the mechanisms needed for disseminating the data to the metabolomics community at large (both metabolomics researchers and databases). The main objective is to make it easier for metabolomics researchers to become aware of newly released, publicly available, metabolomics datasets that may be useful for their research. MetabolomeXchange contains datasets from different data providers: MetaboLights, Metabolomic Repository Bordeaux, Metabolomics Workbench, and Metabolonote
METLIN represents the largest MS/MS collection of data with the database generated at multiple collision energies and in positive and negative ionization modes. The data is generated on multiple instrument types including SCIEX, Agilent, Bruker and Waters QTOF mass spectrometers.
GeneLab is an interactive, open-access resource where scientists can upload, download, store, search, share, transfer, and analyze omics data from spaceflight and corresponding analogue experiments. Users can explore GeneLab datasets in the Data Repository, analyze data using the Analysis Platform, and create collaborative projects using the Collaborative Workspace. GeneLab promises to facilitate and improve information sharing, foster innovation, and increase the pace of scientific discovery from extremely rare and valuable space biology experiments. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight. GeneLab helps scientists understand how the fundamental building blocks of life itself – DNA, RNA, proteins, and metabolites – change from exposure to microgravity, radiation, and other aspects of the space environment. GeneLab does so by providing fully coordinated epigenomics, genomics, transcriptomics, proteomics, and metabolomics data alongside essential metadata describing each spaceflight and space-relevant experiment. By carefully curating and implementing best practices for data standards, users can combine individual GeneLab datasets to gain new, comprehensive insights about the effects of spaceflight on biology. In this way, GeneLab extends the scientific knowledge gained from each biological experiment conducted in space, allowing scientists from around the world to make novel discoveries and develop new hypotheses from these priceless data.
Database of mass spectra of known, unknown and provisionally identified substances. MassBank is the first public repository of mass spectral data for sharing them among scientific research community. MassBank data are useful for the chemical identification and structure elucidation of chemical compounds detected by mass spectrometry.
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GABI, acronym for "Genomanalyse im biologischen System Pflanze", is the name of a large collaborative network of different plant genomic research projects. Plant data from different ‘omics’ fronts representing more than 10 different model or crop species are integrated in GabiPD.
MassBank of North America (MoNA) is a metadata-centric, auto-curating repository designed for efficient storage and querying of mass spectral records. It intends to serve as a the framework for a centralized, collaborative database of metabolite mass spectra, metadata and associated compounds. MoNA currently contains over 200,000 mass spectral records from experimental and in-silico libraries as well as from user contributions.
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Datanator is an integrated database of genomic and biochemical data designed to help investigators find data about specific molecules and reactions in specific organisms and specific environments for meta-analyses and mechanistic models. Datanator currently includes metabolite concentrations, RNA modifications and half-lives, protein abundances and modifications, and reaction kinetics integrated from several databases and numerous publications. The Datanator website and REST API provide tools for extracting clouds of data about specific molecules and reactions in specific organisms and specific environments, as well as data about similar molecules and reactions in taxonomically similar organisms.
Tthe Lipidomics Gateway - a free, comprehensive website for researchers interested in lipid biology, provided by the LIPID MAPS (Lipid Metabolites and Pathways Strategy) Consortium. The LIPID MAPS Lipidomics Gateway provides a rich collection of information and resources to help you stay abreast of the latest developments in this rapidly expanding field. LIPID Metabolites And Pathways Strategy (LIPID MAPS®) is a multi-institutional effort created in 2003 to identify and quantitate, using a systems biology approach and sophisticated mass spectrometers, all of the major — and many minor — lipid species in mammalian cells, as well as to quantitate the changes in these species in response to perturbation. The ultimate goal of our research is to better understand lipid metabolism and the active role lipids play in diabetes, stroke, cancer, arthritis, Alzheimer's and other lipid-based diseases in order to facilitate development of more effective treatments. Since our inception, we have made great strides toward defining the "lipidome" (an inventory of the thousands of individual lipid molecular species) in the mouse macrophage. We have also worked to make lipid analysis easier and more accessible for the broader scientific community and to advance a robust research infrastructure for the international research community. We share new lipidomics findings and methods, hold annual meetings open to all interested investigators, and are exploring joint efforts to extend the use of these powerful new methods to new applications