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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   

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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   

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https://ccb-compute.cs.uni-saarland.de/mirtargetlink2/

Web application provides users with visualization interface to explore and analyze interaction networks between miRNAs and target genes.

Proper citation: miRTargetLink (RRID:SCR_026858) Copy   

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https://www.pharmaron.com/

Contract Research, Development, and Manufacturing Organization (CRDMO) supporting the life sciences industry. Founded in 2004, it provides comprehensive services for small molecules, biologics, and cell and gene therapies (CGT) from discovery to commercialization. Offers integrated R&D, laboratory services, and manufacturing.

Proper citation: Pharmaron (RRID:SCR_028035) Copy   

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http://www.adgenetics.org/

Consortium to conduct genome-wide association studies (GWAS) to identify genes associated with an increased risk of developing late-onset Alzheimer''''s disease (LOAD). The goal of the ADGC is to identify genetic variants associated with risk for AD. It plans to do this through the following collaborative goals: # Identify genes responsible for AD susceptibility # Identify AD sub-phenotype genes rate-of-progression plaque / tangle load / distribution biomarker variability # Generate a genetic data resource for the AD research community Data generated by ADGC is available at the following website: https://www.niagads.org/content/alzheimers-disease-genetics-consortium-adgc-collection

Proper citation: Alzheimers Disease Genetics Consortium (RRID:SCR_004004) Copy   

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http://www.wikigenes.org/e/art/e/258.html

Consortium to discover and map the genes that contribute to Alzheimer's disease and completely understand the role inheritance plays. To achieve this goal, they will work to identify all the genes that contribute to the risk of developing this disease. Investigators will have access to combined genetic data from a large number of Alzheimer's disease subjects and compare it to genetic data from an equally large number of elderly people who do not have Alzheimer's. In the initial phase of the work, more than 20,000 people with Alzheimer's and about 20,000 healthy elderly subjects will be compared. As the study progresses, 10,000 additional people with Alzheimer's and the same number of healthy elderly subjects will be added to the study. The subjects for these studies come from different Alzheimer research project locations across Europe, the UK, the US, and Canada. Data is available from their 2014 publication in Translational Psychiatry at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3944635/ (http://www.ncbi.nlm.nih.gov/pubmed/24495969) Currently, there is no public access to the raw individual level genetic data because of privacy considerations. Researchers working with US cohorts deposit data in the database of genotypes and phenotypes (dbGaP), where it is available to all researchers who can show that they are able to guarantee the security of the data. After scanning the DNA of over 74,000 patients and controls from 15 countries, the IGAP consortium reported 11 new regions of the genome involved in late-onset Alzheimer's disease. IGAP published its results in Nature Genetics on October 27, http://www.ncbi.nlm.nih.gov/pubmed/24162737

Proper citation: International Genomics of Alzheimers Project (RRID:SCR_004029) Copy   

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http://www.blueprint-epigenome.eu/

Consortium to further the understanding of how genes are activated or repressed in both healthy and diseased human cells with a focus on distinct types of haematopoietic cells from healthy individuals and on their malignant leukemic counterparts. They will generate at least 100 reference epigenomes and study them to advance and exploit knowledge of the underlying biological processes and mechanisms in health and disease. Reference epigenomes will be generated by state-of-the-art technologies from highly purified cells for a comprehensive set of epigenetic marks in accordance with quality standards set by International Human Epigenome Consortium (IHEC). Access to the data is provided as well as the protocols used to collect the different blood cell types, to perform the different types of epigenomic analyses, etc.). This resource-generating activity will be complemented by hypothesis-driven research into blood-based diseases, including common leukemias and autoimmune disease (Type 1 Diabetes), by discovery and validation of epigenetic markers for diagnostic use and by epigenetic target identification. Since epigenetic changes are reversible, they can be targets for the development of novel and more individualized medical treatments. The involvement of companies will energize epigenomic research in the private sector by the development of smart technologies for better diagnostic tests and by identifying new targets for compounds. Thus the results of the project may lead to targeted diagnostics, new treatments and preventive measures for specific diseases in individual patients, an approach known as "personalized medicine". The Blueprint Data Access Committee will consider applications for access to data sets stored in the European Genome-phenome Archive (EGA) when authorized to do so by the Blueprint consortium and the holders of the original consent documents. Access is conditional upon availability of samples and/or data and signed agreement by the researcher(s) and the responsible employing Institution to abide by policies related to publication, data disposal, ethical approval and confidentiality. At EBI, the ftp site with the data can be found. You can either opt to link to the track hubs yourself or you can add the track hub to a genome browser - UCSC or ENSEMBL. Also Meta Data files and README are available. The data can also be accessed via the BIOMART system.

Proper citation: Blueprint Epigenome (RRID:SCR_003844) Copy   

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http://eaglep.case.edu/iamdgc_web/

Consortium aiming to identify the remaining genetic risk variants for Age-related Macular Degeneration (AMD). To increase the statistical power needed to identify genes that have small, yet significant contributions to AMD, the consortium is conducting a meta-analysis on 15 Genome Wide Association Studies (GWAS) pooled from consortium members representing over 8,000 patients with advanced AMD (dry type, neovascular, or both) and 50,000 controls. In addition to verifying known genes, the consortium identified 19 new gene variants. The genes identified in these studies function in the immune system, cholesterol transport and metabolism, and formation and maintenance of connective tissue. This study provides a nearly complete picture of genetic heritability for AMD.

Proper citation: International AMD Genetics Consortium (RRID:SCR_004009) Copy   

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http://www.wholecellkb.org/

A collection of free, open-source model organism databases designed specifically to enable comprehensive, dynamic simulations of entire cells and organisms. WholeCellKB provides comprehensive, quantitative descriptions of individual species including: * Their subcellular organization, * Their chromosome sequences, * The essentiality, location, length, direction, and homologs of each gene, * The organization and promoter of each transcription unit, * The expression and degradation rate of each RNA gene product, * The specific folding and maturation pathway of each RNA and protein species including the localization, N-terminal cleavage, signal sequence, prosthetic groups, disulfide bonds, and chaperone interactions of each protein species, * The subunit composition of each macromolecular complex, * Their genetic code, * The binding sites and footprint of every DNA-binding protein, * The structure, charge, and hydrophobicity of every metabolite, * The stoichiometry, catalysis, coenzymes, energetics, and kinetics of every chemical reaction, * The regulatory strength of each transcription factor on each promoter, * Their chemical composition, and * The composition of its typical SP-4 laboratory growth medium. WholeCellKB currently contains a single database of Mycoplasma genitalium, an extremely small gram-positive bacterium and common human pathogen. This database is the most comprehensive description of any single organism to date, and was used to develop the first whole-cell computational model. Users can download the WholeCellKB source code and content to create and customize - including the content, data model, and user interface - their own model organism database.

Proper citation: WholeCellKB (RRID:SCR_004104) Copy   

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http://www.arabidopsis.org

Database of genetic and molecular biology data for the model higher plant Arabidopsis thaliana. Data available includes the complete genome sequence along with gene structure, gene product information, metabolism, gene expression, DNA and seed stocks, genome maps, genetic and physical markers, publications, and information about the Arabidopsis research community. Gene product function data is updated every two weeks from the latest published research literature and community data submissions. Gene structures are updated 1-2 times per year using computational and manual methods as well as community submissions of new and updated genes. TAIR also provides extensive linkouts from data pages to other Arabidopsis resources. The data can be searched, viewed and analyzed. Datasets can also be downloaded. Pages on news, job postings, conference announcements, Arabidopsis lab protocols, and useful links are provided.

Proper citation: TAIR (RRID:SCR_004618) Copy   

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http://www.genedb.org/Homepage/Lmajor

Database of the most recent sequence updates and annotations for the L. major genome. New annotations are constantly being added to keep up with published manuscripts and feedback from the Trypanosomatid research community. You may search by Protein Length, Molecular Mass, Gene Type, Date, Location, Protein Targeting, Transmembrane Helices, Product, GO, EC, Pfam ID, Curation and Comments, and Dbxrefs. BLAST and other tools are available. Leishmania species cause a spectrum of human diseases in tropical and subtropical regions of the world. We have sequenced the 36 chromosomes of the 32.8-megabase haploid genome of Leishmania major (Friedlin strain) and predict 911 RNA genes, 39 pseudogenes, and 8272 protein-coding genes, of which 36% can be ascribed a putative function. These include genes involved in host-pathogen interactions, such as proteolytic enzymes, and extensive machinery for synthesis of complex surface glycoconjugates. The Pathogen Genomics group at the Wellcome Trust Sanger Institute played a major role in sequencing the genome of Leishmania major (see Ivens et al.) Details of the centres involved and which chromosomes they sequenced, are given. The sequence data were obtained by adopting several parallel approaches, including complete cosmid sequencing, whole chromosome shotguns and/or BAC sequencing/skimming. The Leishmania parasite is an intracellular pathogen of the immune system targeting macrophages and dendritic cells. The disease Leishmaniasis affects the populations of 88 counties worldwide with symptoms ranging from disfiguring cutaneous and muco-cutaneous lesions that can cause widespread destruction of mucous membranes to visceral disease affecting the haemopoetic organs. In collaboration with GeneDB, the EuPathDB genomic sequence data and annotations are regularly deposited on TriTrypDB where they can be integrated with other datasets and queried using customized queries.

Proper citation: GeneDB Lmajor (RRID:SCR_004613) Copy   

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http://discovery.hsci.harvard.edu/

An online database of curated cancer stem cell (CSC) experiments coupled to the Galaxy analytical framework. Driven by a need to improve our understanding of molecular processes that are common and unique across cancer stem cells (CSCs), the SCDE allows users to consistently describe, share and compare CSC data at the gene and pathway level. The initial focus has been on carefully curating tissue and cancer stem cell-related experiments from blood, intestine and brain to create a high quality resource containing 53 public studies and 1098 assays. The experimental information is captured and stored in the multi-omics Investigation/Study/Assay (ISA-Tab) format and can be queried in the data repository. A linked Galaxy framework provides a comprehensive, flexible environment populated with novel tools for gene list comparisons against molecular signatures in GeneSigDB and MSigDB, curated experiments in the SCDE and pathways in WikiPathways. Investigation/Study/Assay (ISA) infrastructure is the first general-purpose format and freely available desktop software suite targeted to experimentalists, curators and developers and that: * assists in the reporting and local management of experimental metadata (i.e. sample characteristics, technology and measurement types, sample-to-data relationships) from studies employing one or a combination of technologies; * empowers users to uptake community-defined minimum information checklists and ontologies, where required; * formats studies for submission to a growing number of international public repositories endorsing the tools, currently ENA (genomics), PRIDE (proteomics) and ArrayExpress (transcriptomics). Galaxy allows you to do analyses you cannot do anywhere else without the need to install or download anything. You can analyze multiple alignments, compare genomic annotations, profile metagenomic samples and much much more. Best of all, Galaxy''''s history system provides a complete analyses record that can be shared. Every history is an analysis workflow, which can be used to reproduce the entire experiment. The code for this Galaxy instance is available for download from BitBucket.

Proper citation: Stem Cell Discovery Engine (RRID:SCR_004453) Copy   

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http://www.ncbi.nlm.nih.gov/nucest

Nucleotide database as collection of sequences from several sources, including GenBank, RefSeq, TPA and PDB. Genome, gene and transcript sequence data provide the foundation for biomedical research and discovery.

Proper citation: Nucleotide database (RRID:SCR_004630) Copy   

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http://rulai.cshl.edu/SCPD/

A promoter database of Saccharomyces cerevisiae. Users can explore the promoter regions of ~6000 genes and ORFs in yeast genome, annotate putative regulatory sites of all genes and ORFs, locate intergenic regions, and retrieve sequence of the promoter region. In regards to regulatory elements and transcription factors, users can provide information on transcriptionally related genes, browse matrix and consensus sequences, view the correlation between elements, observe binding affinity and expression, and look at genomewise distribution. SCPD also provides some simple but useful tools for promoter sequence analysis. Gene, consensus and matrix records may be submitted.

Proper citation: SCPD - Saccharomyces cerevisiae promoter database (RRID:SCR_004412) Copy   

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http://caintegrator-info.nci.nih.gov/rembrandt

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 28,2023. REMBRANDT is a data repository containing diverse types of molecular research and clinical trials data related to brain cancers, including gliomas, along with a wide variety of web-based analysis tools that readily facilitate the understanding of critical correlations among the different data types. REMBRANDT aims to be the access portal for a national molecular, genetic, and clinical database of several thousand primary brain tumors that is fully open and accessible to all investigators (including intramural and extramural researchers), as well as the public at-large. The main focus is to molecularly characterize a large number of adult and pediatric primary brain tumors and to correlate those data with extensive retrospective and prospective clinical data. Specific data types hosted here are gene expression profiles, real time PCR assays, CGH and SNP array information, sequencing data, tissue array results and images, proteomic profiles, and patients'''' response to various treatments. Clinical trials'''' information and protocols are also accessible. The data can be downloaded as raw files containing all the information gathered through the primary experiments or can be mined using the informatics support provided. This comprehensive brain tumor data portal will allow for easy ad hoc querying across multiple domains, thus allowing physician-scientists to make the right decisions during patient treatments., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Repository of molecular brain neoplasia data (RRID:SCR_004704) Copy   

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http://birdgenenames.org/cgnc/

International group of researchers interested in providing standardized gene nomenclature for chicken genes. A Chicken Gene Annotation Tool is available from CGNC-UK which assigns chicken nomenclature based on predicted orthology to human genes. The CGNC-US database includes CGNC-UK information and adds manually biocurated from biocurators and interested contributors. A Human Chicken Ortholog Predictions Search is available. Both resources are part of a united CGNC effort and nomenclature data is shared and co-ordinated between these two resources. They strongly encourage researchers with domain knowledge to participate in this nomenclature effort by requesting a login and providing gene nomenclature for their genes of interest. Please contact them for further information or assistance. The AGNC works in conjunction with public resources such as NCBI and Ensembl and in consultation with existing nomenclature committees, including the Chicken Gene Nomenclature Committee (CGNC). The Avian and Chicken nomenclature efforts are co-ordinated and chicken data is shared between these two groups.

Proper citation: Chicken Gene Nomenclature Consortium (RRID:SCR_004966) Copy   

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http://blast.ncbi.nlm.nih.gov/Blast.cgi

Web search tool to find regions of similarity between biological sequences. Program compares nucleotide or protein sequences to sequence databases and calculates statistical significance. Used for identifying homologous sequences.

Proper citation: NCBI BLAST (RRID:SCR_004870) Copy   

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http://smd.princeton.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on December 17, 2021. Database to store, annotate, view, analyze and share microarray data. It provides registered users access to their own data, provides users access to public data, and tools with which to analyze those data, to any public user anywhere in the world. The GenePattern software package has been incorporated directly into SMD, providing access to many new analysis tools, as well as a plug-in architecture that allows users to directly integrate and share additional tools through SMD. This extension is available with the SMD source code that is fully and freely available to others under an Open Source license, enabling other groups to create a local installation of SMD with an enriched data analysis capability. SMD search options allow the user to Search By Experiments, Search By Datasets, or Search By Gene Names. Web services are provided using common standards, such as Simple Object Access Protocol (SOAP). This enables both local and remote researchers to connect to an installation of the database and retrieve data using pre-defined methods, without needing to resort to use of a web browser.

Proper citation: SMD (RRID:SCR_004987) Copy   

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http://www.pantherdb.org/

System that classifies genes by their functions, using published scientific experimental evidence and evolutionary relationships to predict function even in absence of direct experimental evidence. Orthologs view is curated orthology relationships between genes for human, mouse, rat, fish, worm, and fly., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PANTHER (RRID:SCR_004869) Copy   

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