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http://gpcr.biocomp.unibo.it/esldb

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 22,2022. database of protein subcellular localization annotation for eukaryotic organisms. It contains experimental annotations derived from primary protein databases, homology based annotations and computational predictions.

Proper citation: eSLDB - eukaryotic Subcellular Localization database (RRID:SCR_000052) Copy   

•   Source: SciCrunch Registry

http://lifespandb.sageweb.org/

Database that collects published lifespan data across multiple species. The entire database is available for download in various formats including XML, YAML and CSV.

Proper citation: Lifespan Observations Database (RRID:SCR_001609) Copy   

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

A database that focuses on experimentally verified protein-protein interactions mined from the scientific literature by expert curators. The curated data can be analyzed in the context of the high throughput data and viewed graphically with the MINT Viewer. This collection of molecular interaction databases can be used to search for, analyze and graphically display molecular interaction networks and pathways from a wide variety of species. MINT is comprised of separate database components. HomoMINT, is an inferred human protein interatction database. Domino, is database of domain peptide interactions. VirusMINT explores the interactions of viral proteins with human proteins. The MINT connect viewer allows you to enter a list of proteins (e.g. proteins in a pathway) to retrieve, display and download a network with all the interactions connecting them.

Proper citation: MINT (RRID:SCR_001523) Copy   

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http://proteomics.ucsd.edu/Software/NeuroPedia/index.html

A neuropeptide encyclopedia of peptide sequences (including genomic and taxonomic information) and spectral libraries of identified MS/MS spectra of homolog neuropeptides from multiple species.

Proper citation: NeuroPedia (RRID:SCR_001551) Copy   

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http://www.knockoutmouse.org/

Database of the international consortium working together to mutate all protein-coding genes in the mouse using a combination of gene trapping and gene targeting in C57BL/6 mouse embryonic stem (ES) cells. Detailed information on targeted genes is available. The IKMC includes the following programs: * Knockout Mouse Project (KOMP) (USA) ** CSD, a collaborative team at the Children''''s Hospital Oakland Research Institute (CHORI), the Wellcome Trust Sanger Institute and the University of California at Davis School of Veterinary Medicine , led by Pieter deJong, Ph.D., CHORI, along with K. C. Kent Lloyd, D.V.M., Ph.D., UC Davis; and Allan Bradley, Ph.D. FRS, and William Skarnes, Ph.D., at the Wellcome Trust Sanger Institute. ** Regeneron, a team at the VelociGene division of Regeneron Pharmaceuticals, Inc., led by David Valenzuela, Ph.D. and George D. Yancopoulos, M.D., Ph.D. * European Conditional Mouse Mutagenesis Program (EUCOMM) (Europe) * North American Conditional Mouse Mutagenesis Project (NorCOMM) (Canada) * Texas A&M Institute for Genomic Medicine (TIGM) (USA) Products (vectors, mice, ES cell lines) may be ordered from the above programs.

Proper citation: International Knockout Mouse Consortium (RRID:SCR_005574) Copy   

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http://swissregulon.unibas.ch/fcgi/sr/swissregulon

A database of genome-wide annotations of regulatory sites. The predictions are based on Bayesian probabilistic analysis of a combination of input information including: * Experimentally determined binding sites reported in the literature. * Known sequence-specificities of transcription factors. * ChIP-chip and ChIP-seq data. * Alignments of orthologous non-coding regions. Predictions were made using the PhyloGibbs, MotEvo, IRUS and ISMARA algorithms developed in their group, depending on the data available for each organism. Annotations can be viewed in a Gbrowse genome browser and can also be downloaded in flat file format.

Proper citation: SwissRegulon (RRID:SCR_005333) Copy   

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http://the_brain.bwh.harvard.edu/uniprobe/

Database that hosts experimental data from universal protein binding microarray (PBM) experiments (Berger et al., 2006) and their accompanying statistical analyses from prokaryotic and eukaryotic organisms, malarial parasites, yeast, worms, mouse, and human. It provides a centralized resource for accessing comprehensive data on the preferences of proteins for all possible sequence variants ("words") of length k ("k-mers"), as well as position weight matrix (PWM) and graphical sequence logo representations of the k-mer data. The database's web tools include a text-based search, a function for assessing motif similarity between user-entered data and database PWMs, and a function for locating putative binding sites along user-entered nucleotide sequences.

Proper citation: UniPROBE (RRID:SCR_005803) Copy   

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http://edwardslab.bmcb.georgetown.edu/downloads/

The Peptide Sequence Database contains putative peptide sequences from human, mouse, rat, and zebrafish. Compressed to eliminate redundancy, these are about 40 fold smaller than a brute force enumeration. Current and old releases are available for download. Each species'' peptide sequence database comprises peptide sequence data from releveant species specific UniGene and IPI clusters, plus all sequences from their consituent EST, mRNA and protein sequence databases, namely RefSeq proteins and mRNAs, UniProt''s SwissProt and TrEMBL, GenBank mRNA, ESTs, and high-throughput cDNAs, HInv-DB, VEGA, EMBL, IPI protein sequences, plus the enumeration of all combinations of UniProt sequence variants, Met loss PTM, and signal peptide cleavages. The README file contains some information about the non amino-acid symbols O (digest site corresponding to a protein N- or C-terminus) and J (no digest sequence join) used in these peptide sequence databases and information about how to configure various search engines to use them. Some search engines handle (very) long sequences badly and in some cases must be patched to use these peptide sequence databases. All search engines supported by the PepArML meta-search engine can (or can be patched to) successfully search these peptide sequence databases.

Proper citation: Peptide Sequence Database (RRID:SCR_005764) Copy   

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http://www.disgenet.org

Database and discovery platform containing publicly available collections of genes and variants associated to human diseases. Integrates data from curated repositories, GWAS catalogues, animal models and scientific literature.

Proper citation: DisGeNET (RRID:SCR_006178) Copy   

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http://www.chibi.ubc.ca/Gemma

Resource for reuse, sharing and meta-analysis of expression profiling data. Database and set of tools for meta analysis, reuse and sharing of genomics data. Targeted at analysis of gene expression profiles. Users can search, access and visualize coexpression and differential expression results.

Proper citation: Gemma (RRID:SCR_008007) Copy   

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http://bmbpcu36.leeds.ac.uk/RE1db_mkII/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. A database containing all genomic human and mouse binding sites of the Repressor Element 1 Silencing Transcription factor (REST), identified by PSSM. The RE1 silencing transcription factor (REST; also known as the neuron-restrictive silencer factor), is a nine zinc-finger transcription factor, related to the Gli-Kruppel family. REST binds to a conserved 21-nucleotide element, known as repressor element 1 (RE1; also known as the neuron-restrictive silencer element). REST was proposed to be a ''master'' silencer of neuron specific gene expression in non-neuronal tissues and undifferentiated neuroepithelium (precursor of neuronal cells), preventing the default expression of the neuronal phenotype during embryogenesis. It has been shown to function independently of orientation and distance from a gene promoter. REST has an important role during embryonic development, as homozygous gene knockout mice (Rest-/-) die by embryonic day 11.5. The constitutive expression of REST has also been shown to disrupt neuronal gene expression and cause axon path finding errors in chicken embryos (Paquette et al. 2000). RE1 sequences that are known to bind REST have also been found near to non-neuronal genes, including keratin and cytochrome P450 genes.

Proper citation: Neuron-Restrictive Silencer Factor (RRID:SCR_008546) Copy   

•   Source: SciCrunch Registry

http://dnatraffic.ibb.waw.pl/

DNAtraffic database is dedicated to be an unique comprehensive and richly annotated database of genome dynamics during the cell life. DNAtraffic contains extensive data on the nomenclature, ontology, structure and function of proteins related to control of the DNA integrity mechanisms such as chromatin remodeling, DNA repair and damage response pathways from eight model organisms commonly used in the DNA-related study: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on diseases related to the assembled human proteins. Database is richly annotated in the systemic information on the nomenclature, chemistry and structure of the DNA damage and drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA metabolism pathway analysis. Database includes illustrations of pathway, damage, protein and drug. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines it has to be extensively linked to numerous external data sources. Database represents the result of the manual annotation work aimed at making the DNAtraffic database much more useful for a wide range of systems biology applications. DNAtraffic database is freely available and can be queried by the name of DNA network process, DNA damage, protein, disease, and drug.

Proper citation: DNAtraffic (RRID:SCR_008886) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/data/source/nlx_154697-3/search?q=*

A virtual database currently indexing available cell lines from: Coriell Cell Repositories, International Mouse Strain Resource (IMSR), ATCC, NIH Human Pluripotent Stem Cell Registry, NIGMS Human Genetic Cell Repository, and Developmental Therapeutics Program.

Proper citation: Integrated Cell Lines (RRID:SCR_008994) Copy   

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http://appris.bioinfo.cnio.es/

A database that houses annotations of human splice isoforms. It adds reliable protein structural and functional data and information from cross-species conservation. A visual representation of the annotations for each gene allows users to easily identify functional changes brought about by splicing events. In addition to collecting, integrating and analyzing reliable predictions of the effect of splicing events, it also selects a single reference sequence for each gene, termed the principal isoform, based on the annotations of structure, function and conservation for each transcript.

Proper citation: APPRIS (RRID:SCR_012019) Copy   

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http://jbirc.jbic.or.jp/h-dbas/

A specialized database for human alternative splicing (AS) based on H-Invitational full-length cDNAs. H-DBAS offers unique data and viewer for human Alternative Splicing (AS) analysis. It contains: * Genome-wide representative alternative splicing variants (RASVs) identified from following datasets * H-Inv full-length cDNAs (resource summary): H-Invitational cDNA dataset * H-Inv all transcripts (resource summary): Published human mRNA dataset * Mouse full-length cDNAs (resource summary): Mouse cDNA dataset * RASVs affecting protein functions such as protein motif, GO, subcellular localization signal and transmembrane domain * Conserved RASVs compared with mouse genome and the full-length cDNAs (H-Inv full-length cDNAs only)

Proper citation: Human Transcriptome Database for Alternative Splicing (RRID:SCR_013305) Copy   

•   Source: SciCrunch Registry

http://dorina.mdc-berlin.de/rbp_browser/dorina.html

In animals, RNA binding proteins (RBPs) and microRNAs (miRNAs) post-transcriptionally regulate the expression of virtually all genes by binding to RNA. Recent advances in experimental and computational methods facilitate transcriptome-wide mapping of these interactions. It is thought that the combinatorial action of RBPs and miRNAs on target mRNAs form a post-transcriptional regulatory code. We provide a database that supports the quest for deciphering this regulatory code. Within doRiNA, we are systematically curating, storing and integrating binding site data for RBPs and miRNAs. Users are free to take a target (mRNA) or regulator (RBP and/or miRNA) centric view on the data. We have implemented a database framework with short query response times for complex searches (e.g. asking for all targets of a particular combination of regulators). All search results can be browsed, inspected and analyzed in conjunction with a huge selection of other genome-wide data, because our database is directly linked to a local copy of the UCSC genome browser. At the time of writing, doRiNA encompasses RBP data for the human, mouse and worm genomes. For computational miRNA target site predictions, we provide an update of PicTar predictions.

Proper citation: doRiNA (RRID:SCR_013222) Copy   

•   Source: SciCrunch Registry

http://agem.cnb.csic.es/VisualOmics/aGEM/

Database platform of an integrated view of eight databases (mouse gene expression resources: EMAGE, GXD, GENSAT, BioGPS, ABA, EUREXPRESS; human gene expression databases: HUDSEN, BioGPS and Human Protein Atlas) that allows the experimentalist to retrieve relevant statistical information relating gene expression, anatomical structure (space) and developmental stage (time). Moreover, general biological information from databases such as KEGG, OMIM and MTB is integrated too. It can be queried using gene and anatomical structure. Output information is presented in a friendly format, allowing the user to display expression maps and correlation matrices for a gene or structure during development. An in-depth study of a specific developmental stage is also possible using heatmaps that relate gene expression with anatomical components. This is a powerful tool in the gene expression field that makes easy the access to information related to the anatomical pattern of gene expression in human and mouse, so that it can complement many functional genomics studies. The platform allows the integration of gene expression data with spatial-temporal anatomic data by means of an intuitive and user friendly display., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: aGEM (RRID:SCR_013349) Copy   

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http://www.phenogo.org

PhenoGO is a computed database designed for high throughput mining that provides phenotypic and experimental context - such as the cell type, disease, tissue, and organ - to existing annotations between gene products and Gene Ontology (GO) terms, as specified in the Gene Ontology Annotations (GOA) for multiple model organisms. Phenotypic and Experimental (P&E) contexts to identifiers are computationally mapped to general biological ontologies, including: the Cell Ontology (CO), phenotypes from the Unified Medical Language System (UMLS), species from Taxonomy of the National Center for Biotechnology Information (NCBI) taxonomy, and specialized ontologies such as Mammalian Phenotype Ontology (MP) and Mouse Anatomy (MA).

Proper citation: PhenoGO (RRID:SCR_013646) Copy   

•   Source: SciCrunch Registry

http://servers.binf.ku.dk/bloodspot/

Database that provides gene expression profiles of genes and gene signatures in healthy and malignant hematopoiesis and includes data from both humans and mice. In addition to the default plot, which displays an integrated expression plot, two additional levels of visualization are available: an interactive tree showing the hierarchical relationship between the samples, and a Kaplan-Meier survival plot. The database is sub-divided into several datasets that are accessible for browsing.

Proper citation: BloodSpot (RRID:SCR_015563) Copy   

•   Source: SciCrunch Registry

http://compartments.jensenlab.org/Downloads

Web resource that integrates evidence on protein subcellular localization from manually curated literature, high-throughput screens, automatic text mining, and sequence-based prediction methods. All evidence is mapped to common protein identifiers and Gene Ontology terms, and further unify it by assigning confidence scores that facilitate comparison of the different types and sources of evidence and visualize these scores on a schematic cell.

Proper citation: COMPARTMENTS Subcellular localization database (RRID:SCR_015561) Copy   

•   Source: SciCrunch Registry