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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   

•   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://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   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/searches/AMA_form.shtml

Ontology that organizes anatomical structures for the adult mouse (Theiler stage 28) spatially and functionally, using ''is a'' and ''part of'' relationships. The ontology is used to describe expression data for the adult mouse and phenotype data pertinent to anatomy in standardized ways. The browser can be used to view anatomical terms and their relationships in a hierarchical display.

Proper citation: Adult Mouse Anatomy Ontology (RRID:SCR_006568) Copy   

•   Source: SciCrunch Registry

http://embryoimaging.org/

Collection of high resolution images and movies of mouse and human embryos produced using high resolution episcopic microscopy (HREM). Each data set is a series of block-face images generated during sectioning through an entire embryo, typically cut at 2-3 micrometers. Datasets are organized by approximate developmental stage and each embryo has been assigned a specimen ID (SID) for identification. This is an ongoing project funded by the Wellcome Trust to provide comprehensive imaging of normal and mutant mouse embryos that will complement the standard anatomical texts and form the basis for systematic phenotyping. * Movies: A 3D reconstruction shows each embryo, and lower resolution movies created through each orthogonal plane enable you to quickly review the data set. * Image Stacks: In the stack viewer, you can step through the images in sequence, zoom in to see fine details and adjust the image contrast. * NEW: Embryo Comparison: Two image stacks can now be compared in the stack viewer.

Proper citation: Embryo Imaging (RRID:SCR_006329) Copy   

•   Source: SciCrunch Registry

http://isaac.bioapps.biozentrum.uni-wuerzburg.de/isaac/modules/genome/species.xhtml

Web based tool to enable the analysis of sets of genes, transcripts and proteins under different biological viewpoints and to interactively modify these sets at any point of the analysis. Detailed history and snapshot information allows tracing each action. One can switch back to previous states and perform new analyses. Sets can be viewed in the context of genomes, protein functions, protein interactions, pathways, regulation, diseases and drugs. Additionally, users can switch between species with an automatic, orthology based translation of existing gene sets. Sets as well as results of analyses can be exchanged between members of groups.

Proper citation: InterSpecies Analysing Application using Containers (RRID:SCR_006243) Copy   

•   Source: SciCrunch Registry

http://www-personal.umich.edu/~brdsmith/Research.html

Data set of image collections and movies including Magnetic Resonance Imaging of Embryos, Human Embryo Imaging, MRI of Cardiovascular Development, and Live Embryo Imaging. Individual MRI slice images, three-dimensional images, animations, stereo-pair animations, animations of organ systems, and photo-micrographs are included.

Proper citation: Brad Smith Magnetic Resonance Imaging of Embryos (RRID:SCR_006300) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://mips.helmholtz-muenchen.de/genre/proj/corum

Database of manually annotated protein complexes from mammalian organisms. Annotation includes protein complex function, localization, subunit composition, literature references and more. All information is obtained from individual experiments published in scientific articles, but data from high-throughput experiments is excluded.
The majority of protein complexes in CORUM originates from man (65%), followed by mouse (14%) and rat (14%)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CORUM (RRID:SCR_002254) Copy   

•   Source: SciCrunch Registry

http://mpromdb.wistar.upenn.edu/

A curated database that strives to annotate gene promoters identified from ChIP-Seq experiment results. The long term goal of the database is to provide an integrated resource for mammalian gene transcriptional regulation and epigenetics. Users can search based on Enterz gene id/symbol, or by tissue/cell specific activity and filter results based on any combination of tissue/cell specificity, known/novel, CpG/NonCpG, and protein-coding/non-coding gene promoters. It is also integrated with GBrowse genome browser for visualiztion of ChIP-seq profiles and display the annotations.

Proper citation: MPromDb (RRID:SCR_002136) Copy   

•   Source: SciCrunch Registry

http://genome.imim.es/datasets/abs2005/index.html

Public database of known binding sites identified in promoters of orthologous vertebrate genes that have been manually curated from bibliography. We have annotated 650 experimental binding sites from 68 transcription factors and 100 orthologous target genes in human, mouse, rat or chicken genome sequences. Computational predictions and promoter alignment information are also provided for each entry. For each gene, TFBSs conserved in orthologous sequences from at least two different species must be available. Promoter sequences as well as the original GenBank or RefSeq entries are additionally supplied in case of future identification conflicts. The final TSS annotation has been refined using the database dbTSS. Up to this release, 500 bps upstream the annotated transcription start site (TSS) according to REFSEQ annotations have been always extracted to form the collection of promoter sequences from human, mouse, rat and chicken. For each regulatory site, the position, the motif and the sequence in which the site is present are available in a simple format. Cross-references to EntrezGene, PubMed and RefSeq are also provided for each annotation. Apart from the experimental promoter annotations, predictions by popular collections of weight matrices are also provided for each promoter sequence. In addition, global and local alignments and graphical dotplots are also available.

Proper citation: ABS: A Database of Annotated Regulatory Binding Sites From Orthologous Promoters (RRID:SCR_002276) Copy   

•   Source: SciCrunch Registry

http://mint.bio.uniroma2.it/domino/

Open-access database comprising more than 3900 annotated experiments describing interactions mediated by protein-interaction domains. The curation effort aims at covering the interactions mediated by the following domains (SH3, SH2, 14-3-3, PDZ, PTB, WW, EVH, VHS, FHA, EH, FF, BRCT, Bromo, Chromo, GYF). The interactions deposited in DOMINO are annotated according to the PSI MI standard and can be easily analyzed in the context of the global protein interaction network as downloaded from major interaction databases like MINT, INTACT, DIP, MIPS/MPACT. It can be searched with a versatile search tool and the interaction networks can be visualized with a convenient graphic display applet that explicitly identifies the domains/sites involved in the interactions.

Proper citation: DOMINO: Domain peptide interactions (RRID:SCR_002392) Copy   

•   Source: SciCrunch Registry

http://www.ariadnegenomics.com/products/databases/resnet/

Databases that represent sets of pre-compiled information on biological relationships and associations, interactions and facts which have been extracted from the biomedical literature using Ariadne's MedScan technology. ResNet databases store information harvested from the entire PubMed in a formal structure that allows searching, retrieval and updating by Pathway Studio user. ResNet is seamlessly installed when Pathway Studio is installed. There are several available ResNet databases: *ResNet Mammalian Database includes data for Human, Rat, and Mouse *ResNet Plant Database has data on Arabidopsis, Rice and several other plants. Features of ResNet: *All extracted relations have linked access to the original article or abstract *Synonyms and homologs are included to maintain gene identity and to obviate redundancy in search results *Users can update ResNet as often as required using the MedScan technology built into all Ariadne products *Updates are made available by Ariadne every quarter To purchase Pathway Studio software with ResNet database, for information, or to schedule a web demonstration, call our sales department at (240) 453-6272, or (866) 340-5040 (toll free)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: RESNET (RRID:SCR_002121) Copy   

•   Source: SciCrunch Registry

http://dbtss.hgc.jp/

Database of transcriptional start sites (TSSs) representing exact positions in the genome based on a unique experimentally validated TSS sequencing method, TSS Seq. A major part of human adult and embryonic tissues are covered. DBTSS contains 491 million TSS tag sequences collected from a total of 20 tissues and 7 cell cultures. Also integrated is generated RNA-seq data of subcellular- fractionated RNAs and ChIP Seq data of histone modifications, RNA polymerase II and several transcriptional regulatory factors in cultured cell lines. Also included is external epigenomic data, such as chromatin map of the ENCODE project. They associated those TSS information with public and original SNV data, in order to identify single nucleotide variations (SNVs) in the regulatory regions.

Proper citation: DBTSS: Database of Transcriptional Start Sites (RRID:SCR_002354) Copy   

•   Source: SciCrunch Registry

http://geneseer.com/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. Database to access gene information through common names and allows identification of homologs and paralogs for a given gene. This publicly available tool leverages public sequence data, gene metadata information, and other publicly available data to calculate and display orthologous and paralogous gene relationships for all genes from several species, including yeasts, insects, worms, vertebrates, mammals, and primates such as humans.

Proper citation: GeneSeer (RRID:SCR_002626) Copy   

•   Source: SciCrunch Registry

http://genome.ufl.edu/mapper/

A platform composed of three modules: the Database, the Search Engine, and rSNPs, for the computational identification of transcription factor binding sites (TFBSs) in multiple genomes, that combines TRANSFAC and JASPAR data with the search power of profile hidden Markov models (HMMs). The Database contains putative TFBSs found in the upstream sequences of genes from the human, mouse and D.melanogaster genomes. For each gene, they scanned the region from 10,000 base pairs upstream of the transcript start to 50 base pairs downstream of the coding sequence start against all their models. Therefore, the database contains putative binding sites in the gene promoter and in the initial introns and non-coding exons. Information displayed for each putative binding site includes the transcription factor name, its position (absolute on the chromosome, or relative to the gene), the score of the prediction, and the region of the gene the site belongs to. If the selected gene has homologs in any of the other two organisms, the program optionally displays the putative TFBSs in the homologs. The Search Engine allows the identification, visualization and selection of putative TFBSs occurring in the promoter or other regions of a gene from the human, mouse, D.melanogaster, C.elegans or S.cerevisiae genomes. In addition, it allows the user to upload a sequence to query and to build a model by supplying a multiple sequence alignment of binding sites for a transcription factor of interest. rSNPs MAPPER is designed to identify Single Nucleotide Polymorphisms (SNPs) that may have an effect on the presence of one or more TFBSs.

Proper citation: MAPPER - Multi-genome Analysis of Positions and Patterns of Elements of Regulation (RRID:SCR_003077) Copy   

•   Source: SciCrunch Registry

http://www.lipidmaps.org/data/proteome/LMPD.php

Database of lipid related proteins representing human and mouse proteins involved in lipid metabolism. Collection of lipid related genes and proteins contains data for genes and proteins from Homo sapiens, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae, Caenorhabditis elegans, Escherichia coli, Macaca mulata, Drosophila melanogaster, Arabidopsis thaliana and Danio rerio.

Proper citation: LIPID MAPS Proteome Database (RRID:SCR_003062) Copy   

•   Source: SciCrunch Registry

http://www.germonline.org/

Cross-species microarray expression database focusing on high-throughput expression data relevant for germline development, meiosis and gametogenesis as well as the mitotic cell cycle. The database contains a unique combination of information: 1) High-throughput expression data obtained with whole-genome high-density oligonucleotide microarrays (GeneChips). 2) Sample annotation (mouse over the sample name and click on it) using the Multiomics Information Management and Annotation System (MIMAS 3.0). 3) In vivo protein-DNA binding data and protein-protein interaction data (available for selected species). 4) Genome annotation information from Ensembl version 50. 5) Orthologs are identified using data from Ensembl and OMA and linked to each other via a section in the report pages. The portal provides access to the Saccharomyces Genomics Viewer (SGV) which facilitates online interpretation of complex data from experiments with high-density oligonucleotide tiling microarrays that cover the entire yeast genome. The database displays only expression data obtained with high-density oligonucleotide microarrays (GeneChips)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 15,2026.

Proper citation: GermOnline (RRID:SCR_002807) Copy   

•   Source: SciCrunch Registry

http://hollywood.mit.edu/hollywood/

Database built upon genomic annotation of splicing patterns of known genes derived from spliced alignment of complementary DNAs (cDNAs) and expressed sequence tags for human and mouse. It links features such as splice site sequence and strength, exonic splicing enhancers and silencers, conserved and non-conserved patterns of splicing, and cDNA library information for inferred alternative exons. A query tool allows searches for sets of exons with specific splicing characteristics or splicing regulatory element composition, or gives a graphical or sequence-level summary of splicing patterns for a specific gene. A graphical representation of gene splicing patterns is provided, and these patterns can alternatively be layered onto existing information in the UCSC Genome Browser.

Proper citation: Hollywood (RRID:SCR_002920) Copy   

•   Source: SciCrunch Registry

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

Automated system for constructing putative homology groups from complete gene sets of wide range of eukaryotic species. Databse that provides system for automatic detection of homologs, including paralogs and orthologs, among annotated genes of sequenced eukaryotic genomes. HomoloGene processing uses proteins from input organisms to compare and sequence homologs, mapping back to corresponding DNA sequences. Reports include homology and phenotype information drawn from Online Mendelian Inheritance in Man, Mouse Genome Informatics, Zebrafish Information Network, Saccharomyces Genome Database and FlyBase.

Proper citation: HomoloGene (RRID:SCR_002924) Copy   

•   Source: SciCrunch Registry