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Found 240 result(s)
Born of the desire to systematize analyses from The Cancer Genome Atlas pilot and scale their execution to the dozens of remaining diseases to be studied, GDAC Firehose now sits atop terabytes of analysis-ready TCGA data and reliably executes thousands of pipelines per month. More information: https://broadinstitute.atlassian.net/wiki/spaces/GDAC/
>>>!!!<<< 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.
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CEEHRC represents a multi-stage funding commitment by the Canadian Institutes of Health Research (CIHR) and multiple Canadian and international partners. The overall aim is to position Canada at the forefront of international efforts to translate new discoveries in the field of epigenetics into improved human health. The two sites will focus on sequencing human reference epigenomes and developing new technologies and protocols; they will also serve as platforms for other CEEHRC funding initiatives, such as catalyst and team grants. The complementary reference epigenome mapping efforts of the two sites will focus on a range of common human diseases. The Vancouver group will focus on the role of epigenetics in the development of cancer, including lymphoma and cancers of the ovary, colon, breast, and thyroid. The Montreal team will focus on autoimmune / inflammatory, cardio-metabolic, and neuropsychiatric diseases, using studies of identical twins as well as animal models of human disease.
The CONP portal is a web interface for the Canadian Open Neuroscience Platform (CONP) to facilitate open science in the neuroscience community. CONP simplifies global researcher access and sharing of datasets and tools. The portal internalizes the cycle of a typical research project: starting with data acquisition, followed by processing using already existing/published tools, and ultimately publication of the obtained results including a link to the original dataset. From more information on CONP, please visit https://conp.ca
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!!! <<< the repository is offline >>> !!! The Canadian Poisonous Plants Information System presents data on plants that cause poisoning in livestock, pets, and humans. The plants include native, introduced, and cultivated outdoor plants as well as indoor plants that are found in Canada. Some food and herbal plants that may cause potential poisoning problems are also included.
The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The Data Coordinating Center (DCC) is the central provider of TCGA data. The DCC standardizes data formats and validates submitted data.
>>>>!!!!<<<< The Cancer Genomics Hub mission is now completed. The Cancer Genomics Hub was established in August 2011 to provide a repository to The Cancer Genome Atlas, the childhood cancer initiative Therapeutically Applicable Research to Generate Effective Treatments and the Cancer Genome Characterization Initiative. CGHub rapidly grew to be the largest database of cancer genomes in the world, storing more than 2.5 petabytes of data and serving downloads of nearly 3 petabytes per month. As the central repository for the foundational genome files, CGHub streamlined team science efforts as data became as easy to obtain as downloading from a hard drive. The convenient access to Big Data, and the collaborations that CGHub made possible, are now essential to cancer research. That work continues at the NCI's Genomic Data Commons. All files previously stored at CGHub can be found there. The Website for the Genomic Data Commons is here: https://gdc.nci.nih.gov/ >>>>!!!!<<<< The Cancer Genomics Hub (CGHub) is a secure repository for storing, cataloging, and accessing cancer genome sequences, alignments, and mutation information from the Cancer Genome Atlas (TCGA) consortium and related projects. Access to CGHub Data: All researchers using CGHub must meet the access and use criteria established by the National Institutes of Health (NIH) to ensure the privacy, security, and integrity of participant data. CGHub also hosts some publicly available data, in particular data from the Cancer Cell Line Encyclopedia. All metadata is publicly available and the catalog of metadata and associated BAMs can be explored using the CGHub Data Browser.
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>>>!!!<<< The NCI Cancer Models Database, caMOD, was retired on December 24, 2015. Information about many of the mouse models hosted in caMOD was obtained from the Jackson Laboratory Mouse Tumor Biology (MTB) Database and can be accessed through that resource http://tumor.informatics.jax.org/mtbwi/index.do . See caMOD Retirement Announcement https://wiki.nci.nih.gov/display/caMOD/caMOD+Retirement+Announcement >>>>!!<<< Query the Cancer Models database for models submitted by fellow researchers. Retrieve information about the making of models, their genetic description, histopathology, derived cell lines, associated images, carcinogenic agents, and therapeutic trials. Links to associated publications and other resources are provided.
The CancerData site is an effort of the Medical Informatics and Knowledge Engineering team (MIKE for short) of Maastro Clinic, Maastricht, The Netherlands. Our activities in the field of medical image analysis and data modelling are visible in a number of projects we are running. CancerData is offering several datasets. They are grouped in collections and can be public or private. You can search for public datasets in the NBIA (National Biomedical Imaging Archive) image archives without logging in.
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>>>!!!<<<As stated 2017-05-23 Cancer GEnome Mine is no longer available >>>!!!<<< Cancer GEnome Mine is a public database for storing clinical information about tumor samples and microarray data, with emphasis on array comparative genomic hybridization (aCGH) and data mining of gene copy number changes.
This is CSDB version 1 merged from Bacterial (BCSDB) and Plant&Fungal (PFCSDB) databases. This database aims at provision of structural, bibliographic, taxonomic, NMR spectroscopic and other information on glycan and glycoconjugate structures of prokaryotic, plant and fungal origin. It has been merged from the Bacterial and Plant&Fungal Carbohydrate Structure Databases (BCSDB+PFCSDB). The key points of this service are: High coverage. The coverage for bacteria (up to 2016) and archaea (up to 2016) is above 80%. Similar coverage for plants and fungi is expected in the future. The database is close to complete up to 1998 for plants, and up to 2006 for fungi. Data quality. High data quality is achieved by manual curation using original publications which is assisted by multiple automatic procedures for error control. Errors present in publications are reported and corrected, when possible. Data from other databases are verified on import. Detailed annotations. Structural data are supplied with extended bibliography, assigned NMR spectra, taxon identification including strains and serogroups, and other information if available in the original publication. Services. CSDB serves as a platform for a number of computational services tuned for glycobiology, such as NMR simulation, automated structure elucidation, taxon clustering, 3D molecular modeling, statistical processing of data etc. Integration. CSDB is cross-linked to other glycoinformatics projects and NCBI databases. The data are exportable in various formats, including most widespread encoding schemes and records using GlycoRDF ontology. Free web access. Users can access the database for free via its web interface (see Help). The main source of data is retrospective literature analysis. About 20% of data were imported from CCSD (Carbbank, University of Georgia, Athens; structures published before 1996) with subsequent manual curation and approval. The current coverage is displayed in red on the top of the left menu. The time lag between the publication of new data and their deposition into CSDB is ca. 1 year. In the scope of bacterial carbohydrates, CSDB covers nearly all structures of this origin published up to 2016. Prokaryotic, plant and fungal means that a glycan was found in the organism(s) belonging to these taxonomic domains or was obtained by modification of those found in them. Carbohydrate means a structure composed of any residues linked by glycosidic, ester, amidic, ketal, phospho- or sulpho-diester bonds in which at least one residue is a sugar or its derivative.
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<<<!!!<<< This repository is no longer available. >>>!!!>>> Message since 2018-06: This virtual host is being reconstructed.
The CATH database is a hierarchical domain classification of protein structures in the Protein Data Bank. Protein structures are classified using a combination of automated and manual procedures. There are four major levels in the CATH hierarchy; Class, Architecture, Topology and Homologous superfamily.
The Cellosaurus is a knowledge resource on cell lines. It attempts to describe all cell lines used in biomedical research. Its scope includes: Immortalized cell lines, Naturally immortal cell lines (example: stem cell lines), Finite life cell lines when those are distributed and used widely, Vertebrate cell line with an emphasis on human, mouse and rat cell lines, Invertebrate (insects and ticks) cell lines. Its scope does not include: Primary cell lines (with the exception of the finite life cell lines described above), Plant cell lines. Cellosaurus was initiated to be used as a cell line controlled vocabulary in the context of the neXtProt knowledgebase, but it quickly become apparent that there was a need for a cell line knowledge resource that would serve the needs of individual researchers, cell line distributors and bioinformatic resources. This leads to an increase of the scope and depth of the content of the Cellosaurus. The Cellosaurus is a participant of the Resource Identification Initiative and contributes actively to the work of the International Cell Line Authentication Committee (ICLAC). It is a Global Core Biodata Resource, an ELIXIR Core Data Resource and an IRDiRC Recognized Resource.
The Cellular Phenotype database stores data derived from high-throughput phenotypic studies and it is being developed as part of the Systems Microscopy Network of Excellence project. The aim of the Cellular Phenotype database is to provide easy access to phenotypic data and facilitate the integration of independent phenotypic studies. Through its interface, users can search for a gene of interest, or a collection of genes, and retrieve the loss-of-function phenotypes observed, in human cells, by suppressing the expression of the selected gene(s), through RNA interference (RNAi), across independent phenotypic studies. Similarly, users can search for a phenotype of interest and retrieve the RNAi reagents that have caused such phenotype and the associated target genes. Information about specific RNAi reagents can also be obtained when searching for a reagent ID.
CDC.gov is the Centers for Disease Control and Prevention primary online communication channel. CDC.gov provides users with credible, reliable health information on Data and Statistics, Diseases and Conditions, Emergencies and Disasters, Environmental Health, Healthy Living, Injury, Violence and Safety,Life Stages and Populations, Travelers' Health, Workplace Safety and Health
Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of 'small molecular entities'. The term 'molecular entity' encompasses any constitutionally or isotopically distinct atom, molecule, ion, ion pair, radical, radical ion, complex, conformer, etc., identifiable as a separately distinguishable entity. The molecular entities in question are either products of nature or synthetic products used to intervene in the processes of living organisms (either deliberately, as for drugs, or unintentionally', as for chemicals in the environment). The qualifier 'small' implies the exclusion of entities directly encoded by the genome, and thus as a rule nucleic acids, proteins and peptides derived from proteins by cleavage are not included.
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The Chickpea Transcriptome Database (CTDB) has been developed with the view to provide most comprehensive information about the chickpea transcriptome, the most relevant part of the genome. The database contains various information and tools for transcriptome sequence, functional annotation, conserved domain(s), transcription factor families, molecular markers (microsatellites and single nucleotide polymorphisms), Comprehensive gene expression and comparative genomics with other legumes. The database is a freely available resource, which provides user scientists/breeders a portal to search, browse and query the data to facilitate functional and applied genomics research in chickpea and other legumes. The current release of database provides transcriptome sequence from cultivated (Cicer arietinum desi (ICC4958) and kabuli (ICCV2)) and wild (Cicer reticulatum, PI489777) chickpea genotypes.
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Thousands of circular RNAs (circRNAs) have recently been shown to be expressed in eukaryotic cells [Salzman et al. 2012, Jeck et al. 2013, Memczak et al. 2013, Salzman et al. 2013]. Here you can explore public circRNA datasets and download the custom python scripts needed to discover circRNAs in your own (ribominus) RNA-seq data.
Collection of various motion capture recordings (walking, dancing, sports, and others) performed by over 140 subjects. The database contains free motions which you can download and use. There is a zip file of all asf/amc's on the FAQs page.
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>>>!!! <<< 2021-09-01: repository is offline >>>!!!<<< Background: Many studies have been conducted to detect quantitative trait loci (QTL) in dairy cattle. However, these studies are diverse in terms of their differing resource populations, marker maps, phenotypes, etc, and one of the challenges is to be able to synthesise this diverse information. This web page has been constructed to provide an accessible database of studies, providing a summary of each study, facilitating an easier comparison across studies. However, it also highlights the need for uniform reporting of results of studies, to facilitate more direct comparisons being made. Description: Studies recorded in this database include complete and partial genome scans, single chromosome scans, as well as fine mapping studies, and contain all known reports that were published in peer-reviewed journals and readily available conference proceedings, initially up to April 2005. However, this data base is being added to, as indicated by the last web update. Note that some duplication of results will occur, in that there may be a number of reports on the same resource population, but utilising different marker densities or different statistical methodologies. The traits recorded in this map are milk yield, milk composition (protein yield, protein %, fat yield, fat %), and somatic cell score (SCS).