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Found 93 result(s)
=== !!!!! Due to changes in technology and funding, the RAD website is no longer available !!!!! ===
OMIM is a comprehensive, authoritative compendium of human genes and genetic phenotypes that is freely available and updated daily. OMIM is authored and edited at the McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, under the direction of Dr. Ada Hamosh. Its official home is omim.org.
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.
The Gene database provides detailed information for known and predicted genes defined by nucleotide sequence or map position. Gene supplies gene-specific connections in the nexus of map, sequence, expression, structure, function, citation, and homology data. Unique identifiers are assigned to genes with defining sequences, genes with known map positions, and genes inferred from phenotypic information. These gene identifiers are used throughout NCBI's databases and tracked through updates of annotation. Gene includes genomes represented by NCBI Reference Sequences (or RefSeqs) and is integrated for indexing and query and retrieval from NCBI's Entrez and E-Utilities systems.
FungiDB belongs to the EuPathDB family of databases and is an integrated genomic and functional genomic database for the kingdom Fungi. FungiDB was first released in early 2011 as a collaborative project between EuPathDB and the group of Jason Stajich (University of California, Riverside). At the end of 2015, FungiDB was integrated into the EuPathDB bioinformatic resource center. FungiDB integrates whole genome sequence and annotation and also includes experimental and environmental isolate sequence data. The database includes comparative genomics, analysis of gene expression, and supplemental bioinformatics analyses and a web interface for data-mining.
<<<!!!<<< Effective May 2024, NCBI's Assembly resource will no longer be available. NCBI Assembly data can now be found on the NCBI Datasets genome pages. https://www.re3data.org/repository/r3d100014298 >>>!!!>>> A database providing information on the structure of assembled genomes, assembly names and other meta-data, statistical reports, and links to genomic sequence data.
<<<!!!<<< The RDP website is no longer available. A stand-alone version of the RDP Classifier is available on Sorceforge https://sourceforge.net/projects/rdp-classifier/. Instructions for installing a command-line version of RDP Tools can be found at Dr. J.Quensen's Website https://john-quensen.com/tutorials/tutorial-1/ and https://jfq3.gitbook.io/rdptools-docker/rdptools-docker/readme. >>>!!!>>>
NCBI Datasets is a continually evolving platform designed to provide easy and intuitive access to NCBI’s sequence data and metadata. NCBI Datasets is part of the NIH Comparative Genomics Resource (CGR). CGR facilitates reliable comparative genomics analyses for all eukaryotic organisms through an NCBI Toolkit and community collaboration.
The Allen Brain Atlas provides a unique online public resource integrating extensive gene expression data, connectivity data and neuroanatomical information with powerful search and viewing tools for the adult and developing brain in mouse, human and non-human primate
This resource allows users to search for and compare influenza virus genomes and gene sequences taken from GenBank. It also provides a virus sequence annotation tool and links to other influenza resources: NIAID project, JCVI Flu, Influenza research database, CDC Flu, Vaccine Selection and WHO Flu. NOTE: The redirects that are planned for completion by May 2024 will NOT impact the Influenza Virus Resource in any way. The Influenza Virus Resource will continue to be available, serving up data to support our Flu-research community.
The Entrez Protein Clusters database contains annotation information, publications, structures and analysis tools for related protein sequences encoded by complete genomes. The data available in the Protein Clusters Database is generated from prokaryotic genomic studies and is intended to assist researchers studying micro-organism evolution as well as other biological sciences. Available genomes include plants and viruses as well as organelles and microbial genomes.
<<<!!!<<< NCBI announced plans to retire the Clone DB web interface. Pursuant to this retirement, starting on May 27, 2019, all web pages associated with Clone DB and CloneFinder will redirect to this blog post https://ncbiinsights.ncbi.nlm.nih.gov/?s=clone+db. Links to Clone DB from the NCBI home page will also be going away. >>>!!!>>>
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.
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.
IntAct provides a freely available, open source database system and analysis tools for molecular interaction data. All interactions are derived from literature curation or direct user submissions and are freely available.
ClinVar is a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. ClinVar thus facilitates access to and communication about the relationships asserted between human variation and observed health status, and the history of that interpretation. ClinVar processes submissions reporting variants found in patient samples, assertions made regarding their clinical significance, information about the submitter, and other supporting data. The alleles described in submissions are mapped to reference sequences, and reported according to the HGVS standard. ClinVar then presents the data for interactive users as well as those wishing to use ClinVar in daily workflows and other local applications. ClinVar works in collaboration with interested organizations to meet the needs of the medical genetics community as efficiently and effectively as possible
AceView provides a curated, comprehensive and non-redundant sequence representation of all public mRNA sequences (mRNAs from GenBank or RefSeq, and single pass cDNA sequences from dbEST and Trace). These experimental cDNA sequences are first co-aligned on the genome then clustered into a minimal number of alternative transcript variants and grouped into genes. Using exhaustively and with high quality standards the available cDNA sequences evidences the beauty and complexity of mammals’ transcriptome, and the relative simplicity of the nematode and plant transcriptomes. Genes are classified according to their inferred coding potential; many presumably non-coding genes are discovered. Genes are named by Entrez Gene names when available, else by AceView gene names, stable from release to release. Alternative features (promoters, introns and exons, polyadenylation signals) and coding potential, including motifs, domains, and homologies are annotated in depth; tissues where expression has been observed are listed in order of representation; diseases, phenotypes, pathways, functions, localization or interactions are annotated by mining selected sources, in particular PubMed, GAD and Entrez Gene, and also by performing manual annotation, especially in the worm. In this way, both the anatomy and physiology of the experimentally cDNA supported human, mouse and nematode genes are thoroughly annotated.
The DIP database catalogs experimentally determined interactions between proteins. It combines information from a variety of sources to create a single, consistent set of protein-protein interactions. The data stored within the DIP database were curated, both, manually by expert curators and also automatically using computational approaches that utilize the the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data. Please, check the reference page to find articles describing the DIP database in greater detail. The Database of Ligand-Receptor Partners (DLRP) is a subset of DIP (Database of Interacting Proteins). The DLRP is a database of protein ligand and protein receptor pairs that are known to interact with each other. By interact we mean that the ligand and receptor are members of a ligand-receptor complex and, unless otherwise noted, transduce a signal. In some instances the ligand and/or receptor may form a heterocomplex with other ligands/receptors in order to be functional. We have entered the majority of interactions in DLRP as full DIP entries, with links to references and additional information
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.
Clinical Genomic Database (CGD) is a manually curated database of conditions with known genetic causes, focusing on medically significant genetic data with available interventions.
BiGG is a knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.
mentha archives evidence collected from different sources and presents these data in a complete and comprehensive way. Its data comes from manually curated protein-protein interaction databases that have adhered to the IMEx consortium. The aggregated data forms an interactome which includes many organisms. mentha is a resource that offers a series of tools to analyse selected proteins in the context of a network of interactions. Protein interaction databases archive protein-protein interaction (PPI) information from published articles. However, no database alone has sufficient literature coverage to offer a complete resource to investigate "the interactome". mentha's approach generates every week a consistent interactome (graph). Most importantly, the procedure assigns to each interaction a reliability score that takes into account all the supporting evidence. mentha offers eight interactomes (Homo sapiens, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Escherichia coli K12, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae) plus a global network that comprises every organism, including those not mentioned. The website and the graphical application are designed to make the data stored in mentha accessible and analysable to all users. Source databases are: MINT, IntAct, DIP, MatrixDB and BioGRID.