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Found 81 result(s)
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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.
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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.
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Bioinformatics.org serves the scientific and educational needs of bioinformatic practitioners and the general public. We develop and maintain computational resources to facilitate world-wide communications and collaborations between people of all educational and professional levels. We provide and promote open access to the materials and methods required for, and derived from, research, development and education.
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HADb provides a complete and an up-to-date list of human genes and proteins involved directly or indirectly in autophagy as described in literature.
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PHI-base is a web-accessible database that catalogues experimentally verified pathogenicity, virulence and effector genes from fungal, Oomycete and bacterial pathogens, which infect animal, plant, fungal and insect hosts. PHI-base is therfore an invaluable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention. In collaboration with the FRAC team, PHI-base also includes antifungal compounds and their target genes.
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The JenAge Ageing Factor Database AgeFactDB is aimed at the collection and integration of ageing phenotype and lifespan data. Ageing factors are genes, chemical compounds or other factors such as dietary restriction, for example.
In a first step ageing-related data are primarily taken from existing databases. In addition, new ageing-related information is included both by manual and automatic information extraction from the scientific literature.
Based on a homology analysis, AgeFactDB also includes genes that are homologous to known ageing-related genes. These homologs are considered as candidate or putative ageing-related genes.
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SilkDB is a database of the integrated genome resource for the silkworm, Bombyx mori. This database provides access to not only genomic data including functional annotation of genes, gene products and chromosomal mapping, but also extensive biological information such as microarray expression data, ESTs and corresponding references. SilkDB will be useful for the silkworm research community as well as comparative genomics
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The CyberCell database (CCDB) is a comprehensive collection of detailed enzymatic, biological, chemical, genetic, and molecular biological data about E. coli (strain K12, MG1655). It is intended to provide sufficient information and querying capacity for biologists and computer scientists to use computers or detailed mathematical models to simulate all or part of a bacterial cell at a nanoscopic (10-9 m), mesoscopic (10-8 m).The CyberCell database CCDB actually consists of 4 browsable databases: 1) the main CyberCell database (CCDB - containing gene and protein information), 2) the 3D structure database (CC3D – containing information for structural proteomics), 3) the RNA database (CCRD – containing tRNA and rRNA information), and 4) the metabolite database (CCMD – containing metabolite information). Each of these databases is accessible through hyperlinked buttons located at the top of the CCDB homepage. All CCDB sub-databases are fully web enabled, permitting a wide variety of interactive browsing, search and display operations. and microscopic (10-6 m) level.
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The Allele Frequency Net Database (AFND) is a public database which contains frequency information of several immune genes such as Human Leukocyte Antigens (HLA), Killer-cell Immunoglobulin-like Receptors (KIR), Major histocompatibility complex class I chain-related (MIC) genes, and a number of cytokine gene polymorphisms. The Allele Frequency Net Database (AFND) provides a central source, freely available to all, for the storage of allele frequencies from different polymorphic areas in the Human Genome. Users can contribute the results of their work into one common database and can perform database searches on information already available.
We have currently collected data in allele, haplotype and genotype format. However, the success of this website will depend on you to contribute your data.
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Project Achilles is a systematic effort aimed at identifying and cataloging genetic vulnerabilities across hundreds of genomically characterized cancer cell lines. The project uses genome-wide genetic perturbation reagents (shRNAs or Cas9/sgRNAs) to silence or knock-out individual genes and identify those genes that affect cell survival. Large-scale functional screening of cancer cell lines provides a complementary approach to those studies that aim to characterize the molecular alterations (e.g. mutations, copy number alterations) of primary tumors, such as The Cancer Genome Atlas (TCGA). The overall goal of the project is to identify cancer genetic dependencies and link them to molecular characteristics in order to prioritize targets for therapeutic development and identify the patient population that might benefit from such targets.
Project Achilles data is hosted on the Cancer Dependency Map Portal (DepMap) where it has been harmonized with our genomics and cellular models data. You can access the latest and all past datasets here: https://depmap.org/portal/download/all/
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GeneMANI helps you predict the function of your favourite genes and gene sets. GeneMania, a real-time multiple association network integration algorithm for predicting gene function.
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<<<!!!<<< This repository is no longer open to the public !!! >>>!!!>>>
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Phytozome is the Plant Comparative Genomics portal of the Department of Energy's Joint Genome Institute. Families of related genes representing the modern descendants of ancestral genes are constructed at key phylogenetic nodes. These families allow easy access to clade-specific orthology/paralogy relationships as well as insights into clade-specific novelties and expansions.
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BioGRID ORCS is an open repository of CRISPR screens compiled through comprehensive curation efforts. The current index is version 1.0.3 and searches more than 49 publications and 58,161 genes to return more than 895 CRISPR screens from 3 major model organism species and 629 cell lines. All screen data are freely provided through our search index and available via download in a wide variety of standardized formats.
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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.
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The Plant Metabolic Network (PMN) provides a broad network of plant metabolic pathway databases that contain curated information from the literature and computational analyses about the genes, enzymes, compounds, reactions, and pathways involved in primary and secondary metabolism in plants. The PMN currently houses one multi-species reference database called PlantCyc and 22 species/taxon-specific databases.
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Gramene is a platform for comparative genomic analysis of agriculturally important grasses, including maize, rice, sorghum, wheat and barley. Relationships between cereals are queried and displayed using controlled vocabularies (Gene, Plant, Trait, Environment, and Gramene Taxonomy) and web-based displays, including the Genes and Quantitative Trait Loci (QTL) modules.
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InnateDB is a publicly available database of the genes, proteins, experimentally-verified interactions and signaling pathways involved in the innate immune response of humans, mice and bovines to microbial infection. The database captures an improved coverage of the innate immunity interactome by integrating known interactions and pathways from major public databases together with manually-curated data into a centralised resource. The database can be mined as a knowledgebase or used with our integrated bioinformatics and visualization tools for the systems level analysis of the innate immune response.
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<<<!!!<<< This site is no longer maintained and is provided for reference only. Some functionality or links may not work. For all enquiries please contact the Ensembl Helpdesk http://www.ensembl.org/Help/Contact >>>!!!>>>
PhytoPath is a new bioinformatics resource that integrates genome-scale data from important plant pathogen species with literature-curated information about the phenotypes of host infection. Using the Ensembl Genomes browser, it provides access to complete genome assembly and gene models of priority crop and model-fungal, oomycete and bacterial phytopathogens. PhytoPath also links genes to disease progression using data from the curated PHI-base resource. PhytoPath portal is a joint project bringing together Ensembl Genomes with PHI-base, a community-curated resource describing the role of genes in pathogenic infection. PhytoPath provides access to genomic and phentoypic data from fungal and oomycete plant pathogens, and has enabled a considerable increase in the coverage of phytopathogen genomes in Ensembl Fungi and Ensembl Protists.
PhytoPath also provides enhanced searching of the PHI-base resource as well as the fungi and protists in Ensembl Genomes.
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<<<!!!<<< This repository is no longer available>>>!!!>>>. Although the web pages are no longer available, you will still be able to download the final UniGene builds as static content from the FTP site https://ftp.ncbi.nlm.nih.gov/repository/UniGene/. You will also be able to match UniGene cluster numbers to Gene records by searching Gene with UniGene cluster numbers. For best results, restrict to the “UniGene Cluster Number” field rather than all fields in Gene. For example, a search with Mm.2108[UniGene Cluster Number] finds the mouse transthyretin Gene record (Ttr). You can use the advanced search page https://www.ncbi.nlm.nih.gov/gene/advanced to help construct these searches. Keep in mind that the Gene record contains selected Reference Sequences and GenBank mRNA sequences rather than the larger set of expressed sequences in the UniGene cluster.
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The Expression Atlas provides information on gene expression patterns under different biological conditions such as a gene knock out, a plant treated with a compound, or in a particular organism part or cell. It includes both microarray and RNA-seq data. The data is re-analysed in-house to detect interesting expression patterns under the conditions of the original experiment. There are two components to the Expression Atlas, the Baseline Atlas and the Differential Atlas.
The Baseline Atlas displays information about which gene products are present (and at what abundance) in "normal" conditions (e.g. tissue, cell type). It aims to answer questions such as "which genes are specifically expressed in human kidney?". This component of the Expression Atlas consists of highly-curated and quality-checked RNA-seq experiments from ArrayExpress. It has data for many different animal and plant species. New experiments are added as they become available.
The Differential Atlas allows users to identify genes that are up- or down-regulated in a wide variety of different experimental conditions such as yeast mutants, cadmium treated plants, cystic fibrosis or the effect on gene expression of mind-body practice. Both microarray and RNA-seq experiments are included in the Differential Atlas. Experiments are selected from ArrayExpress and groups of samples are manually identified for comparison e.g. those with wild type genotype compared to those with a gene knock out. Each experiment is processed through our in-house differential expression statistical analysis pipeline to identify genes with a high probability of differential expression.
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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.
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>>>!!!<<< The repository is no longer available. >>>!!!<<<
Indian Genetic Disease Database (IGDD) is an initiative of CSIR Indian Institute of Chemical Biology. It is supported by Council of Scientific and Industrial Research (CSIR) and Department of Biotechnology (DBT) of India.
The Indian people represent one-sixth of the world population and consists of a ethnically, geographically, and genetically diverse population. In some communities the ratio of genetic disorder is relatively high due to consanguineous marriage practiced in the community. This database has been created to keep track of mutations in the causal genes for genetic diseases common in India and help the physicians, geneticists, and other professionals retrieve and use the information for the benefit of the public. The database includes scientific information about these genetic diseases and disabilities, but also statistical information about these diseases in today's society. Data is categorized by body part affected and then by title of the disease.
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The mission of the GO Consortium is to develop a comprehensive, computational model of biological systems, ranging from the molecular to the organism level, across the multiplicity of species in the tree of life.
The Gene Ontology (GO) knowledgebase is the world’s largest source of information on the functions of genes. This knowledge is both human-readable and machine-readable, and is a foundation for computational analysis of large-scale molecular biology and genetics experiments in biomedical research.
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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.
- Current projects
- EOSC FAIR-IMPACT
- re3data COREF
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the database entries of re3data.org.
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Except where otherwise noted, content on this site is licensed under a
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- Cite this service: re3data.org - Registry of Research Data Repositories. https://doi.org/10.17616/R3D last accessed: 2024-04-26
