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http://www.disgenet.org/ds/DisGeNET/files/GeneDiseaseAssociation.owl

Ontology that describes the different types of associations between a gene and a disease. It was developed to integrate information from different databases that contain gene-disease associations such as UniProt, CTD, Orphanet, the GWAS Catalog, GAD, MGD, RGD, and LHGDN.

Proper citation: Gene-Disease Association Type Ontology (RRID:SCR_006159) Copy   

•   Source: SciCrunch Registry

http://hereditaryhearingloss.org/

Overview of the genetics of hereditary hearing impairment for researchers and clinicians. The site lists data and references for all known gene localizations and identifications for nonsyndromic hearing impairment, and several for syndromic hearing loss. For syndromic hearing impairment, only a few of the most frequent forms are covered. An atlas of cochlea with genes listed can be accessed from this site.

Proper citation: Hereditary Hearing Loss Homepage (RRID:SCR_006469) Copy   

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http://bioinformatics.ubc.ca/ermineJ/

Data analysis software for gene sets in expression microarray data or other genome-wide data that results in rankings of genes. A typical goal is to determine whether particular biological pathways are doing something interesting in the data. The software is designed to be used by biologists with little or no informatics background. A command-line interface is available for users who wish to script the use of ermineJ. Major features include: * Implementation of multiple methods for gene set analysis: ** Over-representation analysis ** A resampling-based method that uses gene scores ** A rank-based method that uses gene scores ** A resampling-based method that uses correlation between gene expression profiles (a type of cluster-enrichment analysis). * Gene sets receive statistical scores (p-values), and multiple test correction is supported. * Support of the Gene Ontology terminology; users can choose which aspects to analyze. * User files use simple text formats. * Users can modify gene sets or create new ones. * The results can be visualized within the software. * It is simple to compare multiple analyses of the same data set with different settings. * User-definable hyperlinks are provided to external sites to allow more efficient browsing of the results. * For programmers, there is a command line interface as well as a simple application programming interface that can be used to plug ermineJ functionality into your own code Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: ErmineJ (RRID:SCR_006450) Copy   

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http://colt.ccbr.utoronto.ca/cancer/

The COLT-Cancer database is a collection of shRNA dropout signatures profiles, covering ~16000 human genes, and derived from more than 70 Pancreatic, Ovarian and Breast human cancer cell-lines using the microarray detection platform developed in the COLT (CCBR-OICR Lentiviral Technology) facility at the Moffat Lab. All shRNA dropout profiles are freely available through download or queries via this website.

Proper citation: COLT-Cancer (RRID:SCR_006485) Copy   

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http://www.mousebook.org/

Databases and portal to data and ordering mouse strains from MRC Harwell including mouse stocks in FESA (Frozen Embryo and Sperm Archive), mutants from the mutagenesis screen, the ENU DNA archive, standardized phenotyping procedures, imprinting genes and chromosome anomalies. The portal integrates curated information from the MRC Harwell stock resource, and other Harwell databases, with information from external data resources to provide added value information above and beyond what is available through other routes such as IMSR (International Mouse Stain Resource). MouseBook can be searched either using an intuitive Google-style free text search or using the Mammalian Phenotype Ontology (MP) tree structure. Text searches can be on gene, allele, strain identifier (e.g. MGI ID) or phenotype term and are assisted by automatic recognition of term types and autocompletion of gene and allele names covered by the database. Results are returned in a tabbed format providing categorized results identified from each of the catalogs in MouseBook. Individual results lines from each catalog include information on gene, allele, chromosomal location and phenotype and provide a simple click-through link to further information as well as ordering the strain. The infrastructure underlying MouseBook has been designed to be extensible, allowing additional data sources to be added enabling other sites to make their data directly available through MouseBook.

Proper citation: MouseBook (RRID:SCR_006358) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/ena/

Public archive providing a comprehensive record of the world''''s nucleotide sequencing information, covering raw sequencing data, sequence assembly information and functional annotation. All submitted data, once public, will be exchanged with the NCBI and DDBJ as part of the INSDC data exchange agreement. The European Nucleotide Archive (ENA) captures and presents information relating to experimental workflows that are based around nucleotide sequencing. A typical workflow includes the isolation and preparation of material for sequencing, a run of a sequencing machine in which sequencing data are produced and a subsequent bioinformatic analysis pipeline. ENA records this information in a data model that covers input information (sample, experimental setup, machine configuration), output machine data (sequence traces, reads and quality scores) and interpreted information (assembly, mapping, functional annotation). Data arrive at ENA from a variety of sources including submissions of raw data, assembled sequences and annotation from small-scale sequencing efforts, data provision from the major European sequencing centers and routine and comprehensive exchange with their partners in the International Nucleotide Sequence Database Collaboration (INSDC). Provision of nucleotide sequence data to ENA or its INSDC partners has become a central and mandatory step in the dissemination of research findings to the scientific community. ENA works with publishers of scientific literature and funding bodies to ensure compliance with these principles and to provide optimal submission systems and data access tools that work seamlessly with the published literature. ENA is made up of a number of distinct databases that includes the EMBL Nucleotide Sequence Database (Embl-Bank), the newly established Sequence Read Archive (SRA) and the Trace Archive. The main tool for downloading ENA data is the ENA Browser, which is available through REST URLs for easy programmatic use. All ENA data are available through the ENA Browser. Note: EMBL Nucleotide Sequence Database (EMBL-Bank) is entirely included within this resource.

Proper citation: European Nucleotide Archive (ENA) (RRID:SCR_006515) Copy   

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http://ctdbase.org/

A public database that enhances understanding of the effects of environmental chemicals on human health. Integrated GO data and a GO browser add functionality to CTD by allowing users to understand biological functions, processes and cellular locations that are the targets of chemical exposures. CTD includes curated data describing cross-species chemical–gene/protein interactions, chemical–disease and gene–disease associations to illuminate molecular mechanisms underlying variable susceptibility and environmentally influenced diseases. These data will also provide insights into complex chemical–gene and protein interaction networks.

Proper citation: Comparative Toxicogenomics Database (CTD) (RRID:SCR_006530) Copy   

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http://davinci.crg.es/deafness/

Database and data set of known mutations in connexins related to deafness with associated information including published work and classification scheme. Users may submit new mutations. A large number of subjects are affected by hearing impairment. In developed countries deafness has an important genetic origin and at least 60% of the cases are inherited. The pattern of inheritance can be dominant, recessive, X-linked and mitochondrial. Many genes are involved in the different types of deafness (syndromic and non-syndromic). Non-syndromic hereditary deafness is mainly (80%) due to recessive genes (or mutations). It is believed that more than one hundred genes could be involved in hearing impairment. Several of these genes have been identified recently by positional cloning or positional candidate gene approaches. Despite the fact that more than 20 loci have been described for non-syndromic autosomal recessive deafness (DFNB), a single locus, DFNB1, accounts for a high proportion of the cases, with variability depending on the population. The gene involved in this type of deafness is GJB2, which encodes the gap junction protein connexin 26(Cx26). NEW Recent data indicates that DFNB1 can also be due to a deletion of 342Kb involving GJB6, a gene that is very close to GJB2. This deletion has been reported to cause deafness both in the homozygous status and in heterozygosity with a GJB2 point mutation in trans (see big deletions affecting connexin genes...). Connexins are transmembrane proteins that form channels allowing rapid transport of ions or small molecules between cells. There are two types of connexins, alpha and beta, named GJA or GJB followed by a number. Connexins are expressed in many different tissues. Other connexin genes are also involved in deafness. These are GJB1 (Cx32), which is also responsible for X-linked Charcot-Marie-Tooth disease type I; GJB3 (Cx31), involved in both deafness or a skin disease, erythrokeratodermia variabilis, depending on the location of the mutation; GJB6 (Cx30), which has been related to a dominant type of deafness in an Italian family and NEW GJA1 (Cx43), which has recently been shown to be involved in recessive deafness.

Proper citation: Connexin-deafness (RRID:SCR_006531) Copy   

•   Source: SciCrunch Registry

http://vinovia.ncl.ac.uk/emagewebapp/pages/eadhb_home.jsf

Database of a set of standard 3D virtual models at different stages of development from Carnegie Stages (CS) 12-23 (approximately 26-56 days post conception) in which various anatomical regions have been defined with a set of anatomical terms at various stages of development (known as an ontology). Experimental data is captured and converted to digital format and then mapped to the appropriate 3D model. The ontology is used to define sites of gene expression using a set of standard descriptions and to link the expression data to an ''''anatomical tree''''. Human data from stages CS12 to CS23 can be submitted to the HUDSEN Gene Expression Database. The anatomy ontology currently being used is based on the Edinburgh Human Developmental Anatomy Database which encompasses all developing structures from CS1 to CS20 but is not detailed for developing brain structures. The ontology is being extended and refined (by Prof Luis Puelles, University of Murcia, Spain) and will be incorporated into the HUDSEN database as it is developed. Expression data is annotated using two methods to denote sites of expression in the embryo: spatial annotation and text annotation. Additionally, many aspects of the detection reagent and specimen are also annotated during this process (assignment of IDs, nucleotide sequences for probes etc). There are currently two main ways to search HUDSEN - using a gene/protein name or a named anatomical structure as the query term. The entire contents of the database can be browsed using the data browser. Results may be saved. The data in HUDSEN is generated from both from researchers within the HUDSEN project, and from the wider scientific community. The HUDSEN human gene expression spatial database is a collaboration between the Institute of Human Genetics in Newcastle, UK, and the MRC Human Genetics Unit in Edinburgh, UK, and was developed as part of the Electronic Atlas of the Developing Human Brain (EADHB) project (funded by the NIH Human Brain Project). The database is based on the Edinburgh Mouse Atlas gene expression database (EMAGE), and is designed to be an openly available resource to the research community holding gene expression patterns during early human development.

Proper citation: HUDSEN Human Gene Expression Spatial Database (RRID:SCR_006325) Copy   

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http://rgd.mcw.edu

Database for genetic, genomic, phenotype, and disease data generated from rat research. Centralized database that collects, manages, and distributes data generated from rat genetic and genomic research and makes these data available to scientific community. Curation of mapped positions for quantitative trait loci, known mutations and other phenotypic data is provided. Facilitates investigators research efforts by providing tools to search, mine, and analyze this data. Strain reports include description of strain origin, disease, phenotype, genetics, immunology, behavior with links to related genes, QTLs, sub-strains, and strain sources.

Proper citation: Rat Genome Database (RGD) (RRID:SCR_006444) Copy   

•   Source: SciCrunch Registry

http://flybase.org/

Database of Drosophila genetic and genomic information with information about stock collections and fly genetic tools. Gene Ontology (GO) terms are used to describe three attributes of wild-type gene products: their molecular function, the biological processes in which they play a role, and their subcellular location. Additionally, FlyBase accepts data submissions. FlyBase can be searched for genes, alleles, aberrations and other genetic objects, phenotypes, sequences, stocks, images and movies, controlled terms, and Drosophila researchers using the tools available from the "Tools" drop-down menu in the Navigation bar.

Proper citation: FlyBase (RRID:SCR_006549) Copy   

•   Source: SciCrunch Registry

http://dgidb.genome.wustl.edu/

A database of drug-gene relationships that provides drug-gene interactions and potential druggability data given list of genes. There are about 15 data sources that are being aggregated by DGIdb, with update date and these data sources are listed on this page: http://dgidb.genome.wustl.edu/sources, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: DGIdb (RRID:SCR_006608) Copy   

•   Source: SciCrunch Registry

http://www.progenygenetics.com/

Fully customizable, comprehensive genetic pedigree and clinical data management software including a multi-user relational database with an integrated pedigree drawing component to manage genetic and pedigree data in one database. Manage Pedigrees, Individuals, SNPs, STRs, Samples, Plates, Genotypes and exports to multiple analysis platforms. (entry from Genetic Analysis Software) * LIMS software, providing advanced sample tracking and management (including functionality to generate and record barcodes) and configurable workflows for your specific environment. * Full genotype management gives users the ability to track not only family-based studies, but Whole Genome Association studies containing 1000''s of samples with large arrays.

Proper citation: PROGENY (RRID:SCR_006647) Copy   

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http://159.149.160.51/cscan/

Data resource that includes a large collection of genome-wide ChIP-Seq experiments performed on transcription factors (TFs), histone modifications, RNA polymerases and others. Enriched peak regions from the ChIP-Seq experiments are crossed with the genomic coordinates of a set of input genes, to identify which of the experiments present a statistically significant number of peaks within the input genes' loci. The input can be a cluster of co-expressed genes, or any other set of genes sharing a common regulatory profile. Users can thus single out which TFs are likely to be common regulators of the genes, and their respective correlations. Also, by examining results on promoter activation, transcription, histone modifications, polymerase binding and so on, users can investigate the effect of the TFs (activation or repression of transcription) as well as of the cell or tissue specificity of the genes' regulation and expression.

Proper citation: Cscan (RRID:SCR_006756) Copy   

•   Source: SciCrunch Registry

http://cellfinder.de/

Database of mapped validated gene and protein expression, phenotype and images related to cell types. The data allow characterization and comparison of cell types and can be browsed by using the body browser and by searching for cells or genes. All cells are related to more complex systems such as tissues, organs and organisms and arranged according to their position in development. CellFinder provides long-term data storage for validated and curated primary research data and provides additional expert-validation through relevant information extracted from text. Operated under the Open Source/Access model, community and scientific networking applications will allow users to store and retrieve their data and to explore cells and their interactions on singular and complex resolution levels. The involvement of stem cell registries and banks will allow direct access to selected cells. The set up the stem cell data repository will involve three lines of action: * the acquisition of scientific data and contents * the standardized description of this data, its organization with the help of ontologies and technical implementation * the integration of existing sources/logistics and to ensure sustainable long-term operation

Proper citation: CellFinder (RRID:SCR_006598) Copy   

•   Source: SciCrunch Registry

http://www.pseudomonas.com/

Database of peer-reviewed, continually updated annotation for the Pseudomonas aeruginosa PAO1 reference strain genome expanded to include all Pseudomonas species to facilitate cross-strain and cross-species genome comparisons with high quality comparative genomics. The database contains robust assessment of orthologs, a novel ortholog clustering method, and incorporates five views of the data at the sequence and annotation levels (Gbrowse, Mauve and custom views) to facilitate genome comparisons. Other features include more accurate protein subcellular localization predictions and a user-friendly, Boolean searchable log file of updates for the reference strain PAO1. The current annotation is updated using recent research literature and peer-reviewed submissions by a worldwide community of PseudoCAP (Pseudomonas aeruginosa Community Annotation Project) participating researchers. If you are interested in participating, you are invited to get involved. Many annotations, DNA sequences, Orthologs, Intergenic DNA, and Protein sequences are available for download.

Proper citation: Pseudomonas Genome Database (RRID:SCR_006590) Copy   

•   Source: SciCrunch Registry

http://www.maizegdb.org

Collection of data related to crop plant and model organism Zea mays. Used 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 and to provide support services to the community of maize researchers. Data stored at MaizeGDB was inherited from the MaizeDB and ZmDB projects. Sequence data are from GenBank. Data are searchable by phenotype, traits, Pests, Gel Pattern, and Mutant Images.

Proper citation: MaizeGDB (RRID:SCR_006600) Copy   

•   Source: SciCrunch Registry

http://archives.niddk.nih.gov/patient/aask/aask.aspx

Clinical trial investigating whether a specific class of antihypertensive drugs (beta-adrenergic blockers, calcium channel blockers, or angiotensin converting enzyme inhibitors) and/or the level of blood pressure would influence progression of hypertensive kidney disease in African Americans. The initiative consisting of 21 clinical centers and a data-coordinating center is followed by a Continuation of AASK Cohort Study to investigate the environmental, socio-economic, genetic, physiologic, and other co-morbid factors that influence progression of kidney disease in a well-characterized cohort of African Americans with hypertensive kidney disease. Only patients who were previously in the randomized trial are eligible for the cohort study. A significant discovery was made in the treatment strategy for slowing kidney disease caused by hypertension. Angiotensin-converting enzyme (ACE) inhibitors, compared with calcium channel blockers, were found to slow kidney disease progression by 36 percent, and they drastically reduced the risk of kidney failure by 48 percent in patients who had at least one gram of protein in the urine, a sign of kidney failure. ACE inhibitors have been the preferred treatment for hypertension caused by diabetes since 1994; however, calcium channel blockers have been particularly effective in controlling blood pressure in African Americans. The AASK study now recommends ACE inhibitors to protect the kidneys from the damaging effects of hypertension. The Continuation of AASK Cohort Study will be followed at the clinical centers. The patients will be provided with the usual clinical care given to all such patients at the respective centers. Baseline demographic information, selected laboratory tests, and other studies are being obtained at the initiation of the Continuation Study. The patients will be seen quarterly at the centers, and some selected studies done at these visits. Samples will be obtained and stored for additional studies and analyses at a later date.

Proper citation: AASK Clinical Trial and Cohort Study (RRID:SCR_006985) Copy   

•   Source: SciCrunch Registry

http://cancercontrol.cancer.gov/tcrb/tturc/

A transdisciplinary approach to the full spectrum of basic and applied research on tobacco use to reduce the disease burden of tobacco use, including: * Etiology of tobacco use and addiction * Impact of advertising and marketing * Prevention of tobacco use * Treatment of tobacco use and addiction * Identification of biomarkers of tobacco exposure * Identification of genes related to addiction and susceptibility to harm from tobacco Goals * Increase the number of investigators from relevant disciplines who focus on the study of tobacco use as part of transdisciplinary teams. * Generate basic research evidence to improve understanding of the etiology and natural history of tobacco use. * Produce evidence-based tobacco use interventions that can translate to the community and specific understudied or underserved populations. * Increase the number of evidence-based interventions that are novel, including the development, testing and dissemination of innovative behavioral treatments and prevention strategies based upon findings from basic research. * Train transdisciplinary investigators capable of conducting cutting-edge tobacco use research. * Increase the number of peer-reviewed publications in the areas of tobacco use, nicotine addiction, and treatment.

Proper citation: Transdisciplinary Tobacco Use Research Centers (RRID:SCR_006858) Copy   

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http://phenom.ccbr.utoronto.ca/index.jsp

Database of morphological phenotypes caused by mutation of essential genes in Saccharomyces cerevisiae, it allows storing, retrieving, visualizing and data mining the quantitative single-cell measurements extracted from micrographs of the temperature-sensitive (ts) mutant cells. PhenoM allows users to rapidly search and retrieve raw images and their quantified morphological data for genes of interest. The database also provides several data-mining tools, including a PhenoBlast module for phenotypic comparison between mutant strains and a Gene Ontology module for functional enrichment analysis of gene sets showing similar morphological alterations. About one-fifth of the genes in the budding yeast are essential for haploid viability and cannot be functionally assessed using standard genetic approaches such as gene deletion. To facilitate genetic analysis of essential genes, we and others have assembled collections of yeast strains expressing temperature-sensitive (ts) alleles of essential genes. To explore the phenotypes caused by essential gene mutation we used a panel of genetically engineered fluorescent markers to explore the morphology of cells in the ts strain collection using high-throughput microscopy. The database contains quantitative measurements of 1,909,914 cells and 78,194 morphological images for 775 temperature-sensitive mutants spanning 491 different essential genes in permissive temperature (26* C) and restrictive temperature (32* C). The morphological images were generated by high-content screening (HCS) technology.

Proper citation: PhenoM - Phenomics of yeast Mutants (RRID:SCR_006970) Copy   

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