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http://www.creline.org/

The CREATE consortium represents a core of major European and international mouse database holders and research groups involved in conditional mutagenesis, primarily to develop a strategy for the integration and dissemination of Cre driver strains for modelling aspects of complex human diseases in the mouse. Collectively the participants have amassed a significant number of these strains in their respective databases. Therefore one of the goals of CREATE is to provide a unified portal for worldwide access to these critical resources. The portal can either be searched through an advanced BioMart interface, by driver name, or by anatomical site of expression using Embryonic Mouse Anatomy Project (EMAP) and Mouse Anatomy (MA) ontology terms. Search results link back to the original source of the data for more detailed information and to IMSR to order mice if available. The ontology browser is particularly useful as it enables the CREATE consortium to identify cell and tissues that are not currently covered by existing lines. CREATE also aims to coordinate the production of suitable lines by the Cre generation projects described above. Through the CREATE portal, the CREATE consortium aims to develop a strategy for the production, integration and dissemination of new Cre driver strains for modelling aspects of complex human diseases in the mouse. CREATE is also developing a roadmap for harnessing emerging technologies and methods for improving Cre-mediated recombination in vivo through targeted, intensive workshops and discussion forums on the portal. This will entail review of construct design options for classical transgenic constructs (promoter/enhancer used, small size <2025 Kb) vs large transgenic constructs (BAC, P1, YAC etc.); methods used for Cre transgenic lines including random vs targeted integration, position independent expression loci, or replacement of endogenous coding sequences with Cre recombinase under the control of the endogenous locus. CREATE provides a platform for discussion of additional issues specific to inducible Cre strategies including background activity before induction, inducibility (kinetics), efficiency, and protocols used for induction of Cre recombinase activity. Additional components of the technology roadmap will be the cataloguing of other existing methodologies (rtTA, FLP, Dre) of mouse genome modification, sharing information on validated Cre mutant lines as well as identification and assessment of new methods of mutagenesis such as RNAi and other emerging technologies. Other discussion topics addressed through surveys on the CREATE portal include the characterization of Cre lines (specificity of expression/deletion; efficiency of expression/ deletion; reproducibility of deletion from animal to animal for the same floxed allele; reproducibility with different floxed alleles; timing of expression/deletion, etc.), the extent to which Cre expression changes upon backcrossing to specific genetic backgrounds through variegation and silencing; potential phenotypes caused by either integration- mediated mutagenesis or Cre ''toxicity''; and other factors affecting the specificity of Cre-mediated expression/deletion. CREATE regularly integrates common fields from the Cre-X, CreZOO and the MGI recombinase portal resources described below. The data in common consists of: * Transgene or Knock-in name. * MGI ID of allele. * Driver. * Anatomical site of expression. * Pubmed ID. * IMSR strain name and link. * Inducibility (YES/NO).

Proper citation: CREATE (RRID:SCR_006133) Copy   

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http://phenomebrowser.net/

PhenomeNet is a cross-species phenotype similarity network. It contains the experimentally observed phenotypes of multiple species as well as the phenotypes of human diseases. PhenomeNet provides a measure of phenotypic similarity between the phenotypes it contains. The latest release (from 22 June 2012) contains 124,730 complex phenotype nodes taken from the yeast, fish, worm, fly, rat, slime mold and mouse model organism databases as well as human disease phenotypes from OMIM and OrphaNet. The network is a complete graph in which edge weights represent the degree of phenotypic similarity. Phenotypic similarity can be used to identify and prioritize candidate disease genes, find genes participating in the same pathway and orthologous genes between species. To compute phenotypic similarity between two sets of phenotypes, we use a weighted Jaccard index. First, phenotype ontologies are used to infer all the implications of a phenotype observation using several phenotype ontologies. As a second step, the information content of each phenotype is computed and used as a weight in the Jaccard index. Phenotypic similarity is useful in several ways. Phenotypic similarity between a phenotype resulting from a genetic mutation and a disease can be used to suggest candidate genes for a disease. Phenotypic similarity can also identify genes in a same pathway or orthologous genes. PhenomeNet uses the axioms in multiple species-dependent phenotype ontologies to infer equivalent and related phenotypes across species. For this purpose, phenotype ontologies and phenotype annotations are integrated in a single ontology, and automated reasoning is used to infer equivalences. Specifically, for every phenotype, PhenomeNet infers the related mammalian phenotype and uses the Mammalian Phenotype Ontology for computing phenotypic similarity. Tools: * PhenomeBLAST - A tool for cross-species alignments of phenotypes * PhenomeDrug - method for drug-repurposing

Proper citation: phenomeNET (RRID:SCR_006165) Copy   

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http://compbio.charite.de/phenexplorer/

The PhenExplorer allows you to browse the Human Phenotype Ontology (HPO) in different ways, using the tabs ''''by features'''', ''''by disease'''', ''''by ontology'''' or ''''by genes''''. Clicking on a particular phenotypic feature (HPO-term) you can get a list of disease entries that are linked to it (i.e. diseases that are annotated with this HPO-term). You can also visualize this term in the context of the ontological structure. Finally, a lists of genes can be displayed, that are known to cause (when mutated) the linked diseases mentioned above. For each disease you can get the list of linked HPO-terms and genes. You can also search for specific genes and explore to which HPO-terms and diseases they are linked.

Proper citation: Phenexplorer (RRID:SCR_006156) Copy   

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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   

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http://www2.warwick.ac.uk/fac/sci/systemsbiology/staff/ott/tools_and_software/wigwams

A computational tool for analyzing multiple gene expression time series data sets for the same organism. The goal is to determine if there is evidence for gene regulatory mechanisms that are shared by multiple different expression responses.

Proper citation: Wigwams (RRID:SCR_006400) 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   

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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://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.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   

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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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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   

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http://snpeff.sourceforge.net/

Genetic variant annotation and effect prediction software toolbox that annotates and predicts effects of variants on genes (such as amino acid changes). By using standards, such as VCF, SnpEff makes it easy to integrate with other programs.

Proper citation: SnpEff (RRID:SCR_005191) Copy   

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http://www.ebi.ac.uk/Rebholz-srv/ebimed/

A web application that combines Information Retrieval and Extraction from Medline. EBIMed finds Medline abstracts in the same way PubMed does. Then it goes a step beyond and analyses them to offer a complete overview on associations between UniProt protein/gene names, GO annotations, Drugs and Species. The results are shown in a table that displays all the associations and links to the sentences that support them and to the original abstracts. By selecting relevant sentences and highlighting the biomedical terminology EBIMed enhances your ability to acquire knowledge, relate facts, discover implications and, overall, have a good overview economizing the effort in reading.

Proper citation: EBIMed (RRID:SCR_005314) Copy   

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http://ikmbio.csie.ncku.edu.tw/coin/home.php

A web-based system that assess articles according to their term correlations among sentences. It employs the co-occurrence relations and their network centralities to evaluate the influence of biomedical terms from Comparative Toxicogenomics Database (CTD)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CoIN (RRID:SCR_005332) Copy   

•   Source: SciCrunch Registry

http://en.wikipedia.org/wiki/Gene_Wiki

The Gene Wiki is a project that facilitates transferring information on human genes to Wikipedia article stubs with the goal of promoting collaboration and expansion of the articles. Number of gene articles The human genome contains an estimated 20,00025,000 protein-coding genes. The goal of the Gene Wiki project is to create seed articles for every notable human gene, that is, every gene whose function has been assigned in the peer-reviewed scientific literature. Approximately half of human genes have assigned function, therefore the total number of articles seeded by the Gene Wiki project would be expected to be in the range of 10,000 - 15,000. To date, approximately 10,271 articles have been created or augmented to include Gene Wiki project content. Expansion Once seed articles have been established, the hope and expectation is that these will be annotated and expanded by editors ranging in experience from the lay audience to students to professionals and academics. Proteins encoded by genes The majority of genes encode proteins hence understanding the function of a gene generally requires understanding of the function of the corresponding protein. In addition to including basic information about the gene, the project therefore also includes information about the protein encoded by the gene. Stubs for the Gene Wiki project are created by a bot and contain links to the following primary gene/protein databases * HUGO Gene Nomenclature Committee official gene name * Entrez Gene database * OMIM (Mendelian Inheritance in Man) database that catalogues all the known diseases with a genetic component * Amigo Gene Ontology * HomoloGene gene homologs in other species * SymAtlasRNA gene expression pattern in tissues * Protein Data Bank 3D structure of protein encoded by the gene * Uniprot (universal protein resource) a central repository of protein data

Proper citation: Gene Wiki (RRID:SCR_005317) Copy   

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http://enigma.ini.usc.edu/

Network that brings together researchers in imaging genomics, to understand brain structure and function, based on MRI, DTI, fMRI and genomewide association scan (GWAS) data. The ENIGMA Network has several goals: * to create a network of like-minded individuals, interested in pushing forward the field of imaging genetics * to ensure promising findings are replicated via member collaborations, in order to satisfy the mandates of most journals * to share ideas, algorithms, data, and information on promising findings or methods * to facilitate training, including workshops and conferences on key methods and emerging directions in imaging genetics. Data sharing with other members of the ENIGMA Network is optional and by no means a requirement of joining the network. Genetics and Imaging Protocols are available.

Proper citation: ENIGMA: Enhancing Neuro Imaging Genetics Through Meta-Analysis (RRID:SCR_005515) Copy   

•   Source: SciCrunch Registry