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Found 29 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
MetaboLights is a database for Metabolomics experiments and derived information. The database is cross-species, cross-technique and covers metabolite structures and their reference spectra as well as their biological roles, locations and concentrations, and experimental data from metabolic experiments.
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The Human Metabolome Database (HMDB) is a freely available electronic database containing detailed information about small molecule metabolites found in the human body. It is intended to be used for applications in metabolomics, clinical chemistry, biomarker discovery and general education.
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NODE (The National Omics Data Encyclopedia) provides an integrated, compatible, comparable, and scalable multi-omics resource platform that supports flexible data management and effective data release. NODE uses a hierarchical data architecture to support storage of muti-omics data including sequencing data, MS based proteomics data, MS or NMR based metabolomics data, and fluorescence imaging data. Launched in early 2017, NODE has collected and published over 900 terabytes of omics data for researchers from China and all over the world in last three years, 22% of which contains multiple omics data. NODE provides functions around the whole life cycle of omics data, from data archive, data requests/responses to data sharing, data analysis, data review and publish.
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.
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The Small Molecule Pathway Database (SMPDB) contains small molecule pathways found in humans, which are presented visually. All SMPDB pathways include information on the relevant organs, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures. Accompanying data includes detailed descriptions and references, providing an overview of the pathway, condition or processes depicted in each diagram.
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.
SWATHAtlas is a repository of mass spectrometry data of the human proteome. The repository provides open access to libraries of SWATH-MS (Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectra) datasets. SWATH-MS is a method which combines both data-independent acquisition (DIA) and targeted data analysis techniques for the collection and storage of fragmentation spectra of peptides. Compared to techniques of selected reaction monitoring (SRM), SWATH-MS allows for a more extensive throughput of proteins in a sample to be targeted. The spectra collected in SWATHAtlas can be interpreted with the help of software such as OpenSWATH or Peakview.
SwissLipids is an expert curated resource that provides a framework for the integration of lipid and lipidomic data with biological knowledge and models.
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The Yeast Metabolome Database (YMDB) is a manually curated database of small molecule metabolites found in or produced by Saccharomyces cerevisiae (also known as Baker’s yeast and Brewer’s yeast). This database covers metabolites described in textbooks, scientific journals, metabolic reconstructions and other electronic databases.
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The Open Archive for Miscellaneous Data (OMIX) database is a data repository developed and maintained by the National Genomics Data Center (NGDC). The database specializes in descriptions of biological studies, including genomic, proteomic, and metabolomic, as well as data that do not fit in the structured archives at other databases in NGDC. It can accept various types of studies described via a simple format and enables researchers to upload supplementary information and link to it from the publication.
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The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), advances life & health sciences by providing open access to a suite of resources, with the aim to translate big data into big discoveries and support worldwide activities in both academia and industry.
CryptoDB is an integrated genomic and functional genomic database for the parasite Cryptosporidium and other related genera. CryptoDB integrates whole genome sequence and annotation along with experimental data and environmental isolate sequences provided by community researchers. The database includes supplemental bioinformatics analyses and a web interface for data-mining.
GNPS is a web-based mass spectrometry ecosystem that aims to be an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. GNPS aids in identification and discovery throughout the entire life cycle of data; from initial data acquisition/analysis to post publication.
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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.
KiMoSys, a web application for quantitative KInetic MOdels of biological SYStems. Kinetic models, with the aim to understand and subsequently design the metabolism of organism of interest are constructed iteratively and require accurate experimental data for both the generation and verification of hypotheses. Therefore, there is a growing requirement for exchanging experimental data and models between the systems biology community, and to automate as much as possible the kinetic model building, editing, simulation and analysis steps.
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>>>!!!<<< OMICtools is no longer online >>>!!!<<< We founded OMICtools in 2012 with the vision to drive progress in life science. We wanted to empower life science practitioners all over the world to achieve breakthroughs by getting data to talk. While we made tremendous progress over the past three years, developing a bioinformatics database of software and dynamic protocols, attracting more than 1.5M visitors a year, we lacked the financial support we needed to continue. We certainly gave it our all. We'd like to thank everyone who believed in us and supported us on this journey: all our users, our community, our friends, families and employees (who we consider as our extended family!). omicX will probably shut down its operations within the next few weeks. The team and I remain firmly committed to our vision, particularly at this very difficult time. It is now, more than ever before, that researchers need access to a resource that pools collective scientific intelligence. We have accumulated an awful lot of experience which we are keen to share. If your institution would be interested in taking over our website and database, to provide researchers with continued access to the platform, or you simply want to stay in touch with the omicX team, contact us at contact@omictools.com or at carine.toutain@fhbx.eu.
WikiPathways was established to facilitate the contribution and maintenance of pathway information by the biology community. WikiPathways is an open, collaborative platform dedicated to the curation of biological pathways. WikiPathways thus presents a new model for pathway databases that enhances and complements ongoing efforts, such as KEGG, Reactome and Pathway Commons. Building on the same MediaWiki software that powers Wikipedia, we added a custom graphical pathway editing tool and integrated databases covering major gene, protein, and small-molecule systems. The familiar web-based format of WikiPathways greatly reduces the barrier to participate in pathway curation. More importantly, the open, public approach of WikiPathways allows for broader participation by the entire community, ranging from students to senior experts in each field. This approach also shifts the bulk of peer review, editorial curation, and maintenance to the community.
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The DrugBank database is a unique bioinformatics and cheminformatics resource that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information. The latest release of DrugBank (version 5.1.1, released 2018-07-03) contains 11,881 drug entries including 2,526 approved small molecule drugs, 1,184 approved biotech (protein/peptide) drugs, 129 nutraceuticals and over 5,751 experimental drugs. Additionally, 5,132 non-redundant protein (i.e. drug target/enzyme/transporter/carrier) sequences are linked to these drug entries. Each DrugCard entry contains more than 200 data fields with half of the information being devoted to drug/chemical data and the other half devoted to drug target or protein data.