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Found 12 result(s)
caNanoLab is a data sharing portal designed to facilitate information sharing in the biomedical nanotechnology research community to expedite and validate the use of nanotechnology in biomedicine. caNanoLab provides support for the annotation of nanomaterials with characterizations resulting from physico-chemical and in vitro assays and the sharing of these characterizations and associated nanotechnology protocols in a secure fashion.
STRING is a database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations; they are derived from four sources: - Genomic Context - High-throughput Experiments - (Conserved) Coexpression - Previous Knowledge STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable.
The ENCODE Encyclopedia organizes the most salient analysis products into annotations, and provides tools to search and visualize them. The Encyclopedia has two levels of annotations: Integrative-level annotations integrate multiple types of experimental data and ground level annotations. Ground-level annotations are derived directly from the experimental data, typically produced by uniform processing pipelines.
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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.
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The Swedish Human Protein Atlas project has been set up to allow for a systematic exploration of the human proteome using Antibody-Based Proteomics. This is accomplished by combining high-throughput generation of affinity-purified antibodies with protein profiling in a multitude of tissues and cells assembled in tissue microarrays. Confocal microscopy analysis using human cell lines is performed for more detailed protein localization. The program hosts the Human Protein Atlas portal with expression profiles of human proteins in tissues and cells. The main objective of the resource centre is to produce specific antibodies to human target proteins using a high-throughput production method involving the cloning and protein expression of Protein Epitope Signature Tags (PrESTs). After purification, the antibodies are used to study expression profiles in cells and tissues and for functional analysis of the corresponding proteins in a wide range of platforms.
DEPOD - the human DEPhOsphorylation Database (version 1.1) is a manually curated database collecting human active phosphatases, their experimentally verified protein and non-protein substrates and dephosphorylation site information, and pathways in which they are involved. It also provides links to popular kinase databases and protein-protein interaction databases for these phosphatases and substrates. DEPOD aims to be a valuable resource for studying human phosphatases and their substrate specificities and molecular mechanisms; phosphatase-targeted drug discovery and development; connecting phosphatases with kinases through their common substrates; completing the human phosphorylation/dephosphorylation network.
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Stemformatics is a collaboration between the stem cell and bioinformatics community. We were motivated by the plethora of exciting cell models in the public and private domains, and the realisation that for many biologists these were mostly inaccessible. We wanted a fast way to find and visualise interesting genes in these exemplar stem cell datasets. We'd like you to explore. You'll find data from leading stem cell laboratories in a format that is easy to search, easy to visualise and easy to export.
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SISSA Open Data is the Sissa repository for the research data managment. It is an institutional repository that captures, stores, preserves, and redistributes the data of the SISSA scientific community in digital form. SISSA Open Data is managed by the SISSA Library as a service to the SISSA scientific community.
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.
The Database explores the interactions of chemicals and proteins. It integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug-target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins.
>>>!!!<<< caArray Retirement Announcement >>>!!!<<< The National Cancer Institute (NCI) Center for Biomedical Informatics and Information Technology (CBIIT) instance of the caArray database was retired on March 31st, 2015. All publicly-accessible caArray data and annotations will be archived and will remain available via FTP download https://wiki.nci.nih.gov/x/UYHeDQ and is also available at GEO http://www.ncbi.nlm.nih.gov/geo/ . >>>!!!<<< While NCI will not be able to provide technical support for the caArray software after the retirement, the source code is available on GitHub https://github.com/NCIP/caarray , and we encourage continued community development. Molecular Analysis of Brain Neoplasia (Rembrandt fine-00037) gene expression data has been loaded into ArrayExpress: http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3073 >>>!!!<<< caArray is an open-source, web and programmatically accessible microarray data management system that supports the annotation of microarray data using MAGE-TAB and web-based forms. Data and annotations may be kept private to the owner, shared with user-defined collaboration groups, or made public. The NCI instance of caArray hosts many cancer-related public datasets available for download.
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