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Found 20 result(s)
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.
GWAS Central (previously the Human Genome Variation database of Genotype-to-Phenotype information) is a database of summary level findings from genetic association studies, both large and small. We actively gather datasets from public domain projects, and encourage direct data submission from the community.
The NCBI database of Genotypes and Phenotypes archives and distributes the results of studies that have investigated the interaction of genotype and phenotype, including genome-wide association studies, medical sequencing, molecular diagnostic assays, and association between genotype and non-clinical traits. The database provides summaries of studies, the contents of measured variables, and original study document text. dbGaP provides two types of access for users, open and controlled. Through the controlled access, users may access individual-level data such as phenotypic data tables and genotypes.
The Maize Genetics and Genomics Database focuses on collecting data related to the crop plant and model organism Zea mays. The project's goals are to synthesize, display, and provide access to maize genomics and genetics data, prioritizing mutant and phenotype data and tools, structural and genetic map sets, and gene models. MaizeGDB also aims to make the Maize Newsletter available, and provide support services to the community of maize researchers. MaizeGDB is working with the Schnable lab, the Panzea project, The Genome Reference Consortium, and iPlant Collaborative to create a plan for archiving, dessiminating, visualizing, and analyzing diversity data. MMaizeGDB is short for Maize Genetics/Genomics Database. It is a USDA/ARS funded project to integrate the data found in MaizeDB and ZmDB into a single schema, develop an effective interface to access this data, and develop additional tools to make data analysis easier. Our goal in the long term is a true next-generation online maize database.aize genetics and genomics database.
The HomoloGene database provides a system for the automated detection of homologs among annotated genes of genomes across multiple species. These homologs are fully documented and organized by homology group. HomoloGene processing uses proteins from input organisms to compare and sequence homologs, mapping back to corresponding DNA sequences.
ALSoD is a freely available database that has been transformed from a single gene storage facility recording mutations in the SOD1 gene to a multigene ALS bioinformatics repository and analytical instrument combining genotype, phenotype, and geographical information with associated analysis tools. These include a comparison tool to evaluate genes side by side or jointly with user configurable features, a pathogenicity prediction tool using a combination of computational approaches to distinguish variants with nonfunctional characteristics from disease-associated mutations with more dangerous consequences, and a credibility tool to enable ALS researchers to objectively assess the evidence for gene causation in ALS. Furthermore, integration of external tools, systems for feedback, annotation by users, and two-way links to collaborators hosting complementary databases further enhance the functionality of ALSoD.
CorrDB has data of cattle, relating to meat production, milk production, growth, health, and others. This database is designed to collect all published livestock genetic/phenotypic trait correlation data, aimed at facilitating genetic network analysis or systems biology studies.
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The Cystic Fibrosis Mutation Database (CFTR1) was initiated by the Cystic Fibrosis Genetic Analysis Consortium in 1989 to increase and facilitate communications among CF researchers, and is maintained by the Cystic Fibrosis Centre at the Hospital for Sick Children in Toronto. The specific aim of the database is to provide up to date information about individual mutations in the CFTR gene. In a major upgrade in 2010, all known CFTR mutations and sequence variants have been converted to the standard nomenclature recommended by the Human Genome Variation Society.
The Drosophila Synthetic Population Resource (DSPR) consists of a new panel of over 1700 recombinant inbred lines (RILs) of Drosophila melanogaster, derived from two highly recombined synthetic populations, each created by intercrossing a different set of 8 inbred founder lines (with one founder line common to both populations). Complete genome sequence data for the founder lines are available, and in addition, there is a high resolution genetic map for each RIL. The DSPR has been developed as a community resource for high-resolution QTL mapping and is intended to be used widely by the Drosophila community.
The dbVar is a database of genomic structural variation containing data from multiple gene studies. Users can browse data containing the number of variant cells from each study, and filter studies by organism, study type, method and genomic variant. Organisms include human, mouse, cattle and several additional animals. ***NCBI will phase out support for non-human organism data in dbSNP and dbVar beginning on September 1, 2017 ***
<<<!!!<<< 08.08.2019: Plexdb is no longer online, URLold: http://www.plexdb.org/index.php >>>!!!>>> >>>>!!!! <<<< 13.12.2018: PLEXdb is now a static site after funding stopped from NSF. We have stopped registration of new users; but past users who have data can login when needed and interact with the site. You can download data using the authentication provided at the download page. >>>>!!!!<<<< PLEXdb is a unified gene expression resource for plants and plant pathogens. PLEXdb is a genotype to phenotype, hypothesis building information warehouse, leveraging highly parallel expression data with seamless portals to related genetic, physical, and pathway data.
Online Mendelian Inheritance in Animals (OMIA) is a catalogue/compendium of inherited disorders, other (single-locus) traits, and genes in 218 animal species (other than human and mouse and rats, which have their own resources) authored by Professor Frank Nicholas of the University of Sydney, Australia, with help from many people over the years. OMIA information is stored in a database that contains textual information and references, as well as links to relevant PubMed and Gene records at the NCBI, and to OMIM and Ensembl.
The HUGO Gene Nomenclature Committee (HGNC) assigned unique gene symbols and names to over 35,000 human loci, of which around 19,000 are protein coding. This curated online repository of HGNC-approved gene nomenclature and associated resources includes links to genomic, proteomic and phenotypic information, as well as dedicated gene family pages.
LINCS Data Portal provides access to LINCS data from various sources. The program has six Data and Signature Generation Centers: Drug Toxicity Signature Generation Center, HMS LINCS Center, LINCS Center for Transcriptomics, LINCS Proteomic Characterization Center for Signaling and Epigenetics, MEP LINCS Center, and NeuroLINCS Center.
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CTD is a robust, publicly available database that aims to advance understanding about how environmental exposures affect human health. It provides manually curated information about chemical–gene/protein interactions, chemical–disease and gene–disease relationships. These data are integrated with functional and pathway data to aid in development of hypotheses about the mechanisms underlying environmentally influenced diseases. We also have additional ongoing projects involving manual curation of exposome data and chemical–phenotype relationships to help identify pre–disease biomarkers resulting from environmental exposures. The initial release of CTD was on November 12, 2004. We’re grateful to our strong community support and encourage you to give us feedback so we can continue to evolve with your research needs.
The IMPC is a confederation of international mouse phenotyping projects working towards the agreed goals of the consortium: To undertake the phenotyping of 20,000 mouse mutants over a ten year period, providing the first functional annotation of a mammalian genome. Maintain and expand a world-wide consortium of institutions with capacity and expertise to produce germ line transmission of targeted knockout mutations in embryonic stem cells for 20,000 known and predicted mouse genes. Test each mutant mouse line through a broad based primary phenotyping pipeline in all the major adult organ systems and most areas of major human disease. Through this activity and employing data annotation tools, systematically aim to discover and ascribe biological function to each gene, driving new ideas and underpinning future research into biological systems; Maintain and expand collaborative “networks” with specialist phenotyping consortia or laboratories, providing standardized secondary level phenotyping that enriches the primary dataset, and end-user, project specific tertiary level phenotyping that adds value to the mammalian gene functional annotation and fosters hypothesis driven research; and Provide a centralized data centre and portal for free, unrestricted access to primary and secondary data by the scientific community, promoting sharing of data, genotype-phenotype annotation, standard operating protocols, and the development of open source data analysis tools. Members of the IMPC may include research centers, funding organizations and corporations.
As with most biomedical databases, the first step is to identify relevant data from the research community. The Monarch Initiative is focused primarily on phenotype-related resources. We bring in data associated with those phenotypes so that our users can begin to make connections among other biological entities of interest. We import data from a variety of data sources. With many resources integrated into a single database, we can join across the various data sources to produce integrated views. We have started with the big players including ClinVar and OMIM, but are equally interested in boutique databases. You can learn more about the sources of data that populate our system from our data sources page https://monarchinitiative.org/about/sources.