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http://www.credrivermice.org/expression/

Database of microarray analysis of twelve major classes of fluorescent labeled neurons within the adult mouse forebrain that provide the first comprehensive view of gene expression differences. The publicly available datasets demonstrate a profound molecular heterogeneity among neuronal subtypes, represented disproportionately by gene paralogs, and begin to reveal the genetic programs underlying the fundamental divisions between neuronal classes including that between glutamatergic and GABAergic neurons. Five of the 12 populations were chosen from cingulate cortex and included several subtypes of GABAergic interneurons and pyramidal neurons. The remaining seven were derived from the somatosensory cortex, hippocampus, amygdala and thalamus. Using these expression profiles, they were able to construct a taxonomic tree that reflected the expected major relationships between these populations, such as the distinction between cortical interneurons and projection neurons. The taxonomic tree indicated highly heterogeneous gene expression even within a single region. This dataset should be useful for the classification of unknown neuronal subtypes, the investigation of specifically expressed genes and the genetic manipulation of specific neuronal circuit elements. Datasets: * Full: Here you can query gene expression results for the neuronal populations * Strain: Here you can query the same expression results accessed under the full checkbox, with one additional population (CT6-CG2) included as a control for the effects of mouse strain. This population is identical to CT6-CG (YFPH) except the neurons were derived from wild-type mice of three distinct strains: G42, G30, and GIN. * Arlotta: Here you can query the same expression results accessed under the full checkbox, with nine additional populations from the dataset of Arlotta et al., 2005. These populations were purified by FACS after retrograde labeling with fluorescent microspheres. Populations are designated by the prefix ACS for corticospinal neurons, ACC for corticocallosal neurons and ACT for corticotectal neurons, followed by the suffix E18 for gestational age 18 embryos, or P3, P6 and P14 for postnatal day 3, 6 and 14 pups. For each successful gene query the following information is returned: # Signal level line plot: Signal level is plotted on Y-axis (log base 2) for each sample. Samples include the thirty six representing the twelve populations profiled in Sugino et al. In addition, six samples from homogenized (=dissociated and but not sorted) cortex are included representing two different strains: G42-HO is homogenate from strain G42, GIN-HO is homogenate from stain GIN. # Signal level raster plots: Signal level is represented by color (dark red is low, bright red is high) for all samples. Color scale is set to match minimum (dark red) and maximum (bright yellow) signal levels within the displayed set of probe sets. # Scaled signal level raster plots: Same as 2) except color scale is adjusted separately for each gene according to its maximum and minimum signal level. # Table: Basic information about the returned probe sets: * Affymetrix affyid of probe set * NCBI gene symbol, NCBI gene name * NCBI geneID * P-value score from ANOVA for each gene is also given if available (_anv column). P-value represents the probability that there is no difference in the expression across cell types.

Proper citation: Mouse Neuronal Expression Database (RRID:SCR_002043) Copy   

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https://cell-innovation.nig.ac.jp/GNP/index_e.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Integrated database of experiment data generated by participating research institutes and public databases relating to: 1) transcription starting position of human genes in the human genome, 2) conjunction to control region on transcriptional factors and the human genome 3) protein-protein interaction with a central focus on transcription factors organized for use in genome level research. Gene Search is the function to search the integrated database by using keywords and public IDs. The search results can be visualized by: * Genome Explorer : provides annotation of landmarks (genes, transcription start sites, etc.) aligned in accordance with their genome locations. * PPI Network : provides a graphical view of protein-protein interaction (PPI) network from the experimental data generated under the project and the public datasets. * Expression Profile : clusters genes by expression pattern and display the result with heatmap. The function provides genes which have relation of coregulation and anti-coregulation. * Comparison Viewer : This function gives the view to compare the genomic regions between human and mouse homologous genes. The viewer shows the distribution of transcription start sites (TSS) as the way of separable by tissues or time points with other landmarks on genome region. * Gene Stock : This is the function to save the gene list that you are interested until the session is closed.

Proper citation: Genome Network Platform (RRID:SCR_001737) Copy   

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http://srv00.recas.ba.infn.it/ASPicDB/

A database to access reliable annotations of the alternative splicing pattern of human genes, obtained by ASPic algorithm (Castrignano et al. 2006), and to the functional annotation of predicted isoforms. Users may select and extract specific sets of data related to genes, transcripts and introns fulfilling a combination of user-defined criteria. Several tabular and graphical views of the results are presented, providing a comprehensive assessment of the functional implication of alternative splicing in the gene set under investigation. ASPicDB also includes information on tissue-specific splicing patterns of normal and cancer cells, based on available EST data and their library source annotation.

Proper citation: ASPicDB (RRID:SCR_002102) Copy   

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. The Alternative Splicing and Transcript Diversity (ASTD) database project is creating a database of alternative splice events and transcripts of genes from human, mouse and rat. Full length transcripts are generated with the aim of understanding the mechanism of alternative splicing on a genome-wide scale. The current release of the human genome consists of: 16715 genes, 14101 have more than one splice isoform, with an average of 5.6 splice patterns per gene. 10831 transcripts are annotated as full length with a transcription start site and a poly(A). The current release of the mouse genome consists of: 16491 genes, 13028 have more than one splice isoform, with an average of 4 splice patterns per gene. 6011 transcripts are annotated as full length with a transcription start site and a poly(A). The current release of the rat genome consists of: 10424 genes, 6344 have more than one splice isoform, with an average of 2.6 splice patterns per gene. 1250 transcripts are annotated as full length with a transcription start site and a poly(A). Sponsors: The ASTD project at EBI is supported by a grant from the EC: Eurasnet Network of Excellence (LSHG-CT-2005-518238). It was also supported by the ASD grant from the EC (QLRT-CT-2001-02062) until November 2005 and the ATD grant from the EC (LSHG-CT-2003-503329) until May 2007.

Proper citation: The Alternatve Splicing Database (RRID:SCR_001883) Copy   

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http://www.arexdb.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. The Arabidopsis gene Expression Database collects Arabidopsis gene expression data from genome-wide and gene-specific sources and integrative search tools are provided. Currently the database contains only root gene expression data, but has the capability to contain data from any part of the plant. The aim of Arabidopsis gene Expression Database is to: (1) Integrate genome-wide and gene-specific ("traditional") types of expression pattern data, using ontologies to describe data whenever possible, in particular to describe expression patterns. (2) Provide user-friendly search tools, for example to search for genes expressed with a certain pattern, or to search for the expression pattern of specific genes (from gene-specific experiments and from microarray data). Expression pattern predicted from the microarray data is called digital in situ.

Proper citation: AREX (RRID:SCR_002170) Copy   

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https://brd.cancer.gov/

Database to find literature, established protocols, and best practices for collecting, storing, and handling biological samples. Its main purpose is to help researchers improve the quality and reproducibility of biospecimen-based medical and genetic research. Provides a library of validated protocols that laboratories use to properly process human biospecimens (like blood or tissue) without degrading their molecular integrity. Offers procedural guidelines (BEBPs) backed by scientific literature to protect samples from pre-analytical variables (e.g., storage temperature, time-to-freezing). Catalogs thousands of peer-reviewed articles focused on biospecimen science.

Proper citation: Biospecimen Research Database (RRID:SCR_001944) Copy   

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http://hertellab.mmg.uci.edu/cgi-bin/HEXEvent/HEXEventWEB.cgi

A free database that provides a list of human internal exons and reports all their known splice events based on EST information from the UCSC Genome Browser. This list can be restricted by the user to either only a specific region in the genome (by specifying the chromosome, the strand and the start and end position), to a whole chromosome or to a group of genes. Furthermore, exons can be filtered according to their splicing type (constitutive exons, cassette exons and exons with one or more alternative 3' and/or 5' splice sites). In order to extract a customized set of exons, the user-specific definitions of exon types can be fixed. The user needs to specify in what fraction of ESTs an exon is allowed to be alternatively spliced in order to still be called constitutive. Furthermore, the user can restrict the set of requested cassette exons by a certain upper inclusion level, which, for instance, is useful when only looking for low-inclusion exons.

Proper citation: HEXEvent (RRID:SCR_002106) Copy   

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http://www.bacteriome.org

Database integrating physical (protein-protein) and functional interactions within the context of an E. coli knowledgebase. Presently the resource offers access to two types of network: * A network of functional interactions derived through exploiting available functional genomic datasets within a Bayesian framework * Two networks of experimentally derived protein-protein interactions - a "core" network consisting of interactions deemed to be of "high quality"; and an "extended" network which extends the "core" network by including interactions for which experimental evidence is less strong.

Proper citation: Bacteriome.org (RRID:SCR_001934) Copy   

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http://www.cbil.upenn.edu/ParaDBs/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 28,2025. These databases were constructed by extracting the organism specific ESTs from dbEST, removing polyA sequences from the ends and trimming 5' and 3' regions with greater than 25% N's in a 20 base pair window. These quality sequences were then aligned using the cap2 program and the consensus sequences thus generated put into a database that is available on the web. A number of parasitic organisms were chosen that have between 3000 and 15000 ESTs. The attempt here is to provide useful information and analyses to the scientific community without curating the results in any way. A total of 55192 ESTs, deposited into dbEST/GenBank, were included in the analyses. The resulting sequences have been clustered into nonredundant gene assemblies and deposited into a relational database that supports a variety of sequence and text searches. This database has been used to compare the gene assemblies using BLAST similarity comparisons to the public protein databases to identify putative genes. Of these new entries, approximately 15%-20% represent putative homologs with a conservative cutoff of p < 10(-9), thus identifying many conserved genes that are likely to share common functions with other well-studied organisms. Gene assemblies were also used to identify strain polymorphisms, examine stage-specific expression, and identify gene families. An interesting class of genes that are confined to members of this phylum and not shared by plants, animals, or fungi, was identified. These genes likely mediate the novel biological features of members of the Apicomplexa and hence offer great potential for biological investigation and as possible therapeutic targets.

Proper citation: Parasite Databases of Clustered ESTs (RRID:SCR_002262) Copy   

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http://blocks.fhcrc.org/codehop.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.Service to design PCR primers from protein multiple sequence alignments. NOTICE: This version of CODEHOP is no longer maintained.

Proper citation: CODEHOP (RRID:SCR_002898) Copy   

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http://www.ebi.ac.uk/compneur-srv/LGICdb/

Database providing access to information about transmembrane proteins that exist under different conformations, with three primary subfamilies: the cys-loop superfamily, the ATP gated channels superfamily, and the glutamate activated cationic channels superfamily. Due to the lack of evolutionary relationship, these three superfamilies are treated separately. It currently contains 554 entries of ligand-activated ion channel subunits. In this database one may find: the nucleic and proteic sequences of the subunits. Multiple sequence alignments can be generated, and some phylogenetic studies of the superfamilies are provided. Additionally, the atomic coordinates of subunits, or portion of subunits, are provided when available. Redundancy is kept to a minimum, i.e. one entry per gene. Each entry in the database has been manually constructed and checked by a researcher of the field in order to reduce the inaccuracies to a minimum. NOTE: This database is not actively maintained anymore. People should not consider it as an up-to-date trustable resource. For any new work, they should consider using alternative sources, such as UniProt, Ensembl, Protein Databank etc.

Proper citation: Ligand-Gated Ion Channel Database (RRID:SCR_002418) Copy   

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http://genecards.org

Database of human genes that provides concise genomic, proteomic, transcriptomic, genetic and functional information on all known and predicted human genes. Information featured in GeneCards includes orthologies, disease relationships, mutations and SNPs, gene expression, gene function, pathways, protein-protein interactions, related drugs and compounds and direct links to cutting edge research reagents and tools such as antibodies, recombinant proteins, clones, expression assays and RNAi reagents.

Proper citation: GeneCards (RRID:SCR_002773) Copy   

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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   

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http://zfin.org

Model organism database that serves as central repository and web-based resource for zebrafish genetic, genomic, phenotypic and developmental data. Data represented are derived from three primary sources: curation of zebrafish publications, individual research laboratories and collaborations with bioinformatics organizations. Data formats include text, images and graphical representations.Serves as primary community database resource for laboratory use of zebrafish. Developed and supports integrated zebrafish genetic, genomic, developmental and physiological information and link this information extensively to corresponding data in other model organism and human databases.

Proper citation: Zebrafish Information Network (ZFIN) (RRID:SCR_002560) Copy   

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http://karg.cbi.pku.edu.cn

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. Database of data and knowledge linking genes and chromosome regions to addiction that were extracted from reviewing more than 1,000 peer-reviewed publications from between 1976 and 2006. This list of publications included review papers on addiction selected from results of PUBMED query "(addiction OR drug abuse) AND review" as well as research papers selected from PUBMED query "(addiction OR drug abuse) AND (gene OR microarray OR proteomics OR QTL OR population association OR genetic linkage)". The data spanned multiple technology platforms including classical hypothesis-testing of single genes, identification of significantly differentially expressed genes in microarray experiments, identification of significantly differentially expressed proteins in proteomics assays, identification of addiction-vulnerable chromosome regions in animal QTL studies, genetic linkage studies, population association studies, and OMIM annotations. From each publication they collected the genes, proteins, or chromosome regions linked to addiction, as well as metadata such as species, nature of the addictive substance, studied brain regions, technology platforms, and experimental parameters. In total, they collected 2,343 items of evidence linking 1,500 human genes to addiction. Among them 396 genes were supported by two or more items of evidence. The interface supports browsing of the genes by chromosome or pathways, advanced text search by gene ID, organism, type of addictive substance, technology platform, protein domain, and/or PUBMED ID, and sequence search by BLAST similarity. All data, database schema, and MySQL commands are freely available for download.

Proper citation: Knowledgebase for Addiction Related Genes (RRID:SCR_002687) Copy   

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http://ecogene.org/

Database that contains updated information about the Escherichia coli K-12 genome and proteome sequences, including extensive gene bibliographies. Users are able to download customized tables, perform Boolean query comparisons, generate sets of paired DNA sequences, and download any E. coli K-12 genomic DNA sub-sequence. BLAST functions, microarray data, an alphabetical index of genes, and gene overlap queries are also available. The Database Table Downloads Page provides a full list of EG numbers cross-referenced to the new cross-database ECK numbers and other common accession numbers, as well as gene names and synonyms. Monthly release archival downloads are available, but the live, daily updated version of EcoGene is the default mysql database for download queries.

Proper citation: EcoGene (RRID:SCR_002437) Copy   

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http://www.ifti.org/ootfd/

ooTFD (object-oriented Transcription Factors Database) is a successor to TFD, the original Transcription Factors Database. This database is aimed at capturing information regarding the polypeptide interactions which comprise and define the properties of transcription factors. ooTFD contains information about transcription factor binding sites, as well as composite relationships within transcription factors, which frequently occur as multisubunit proteins that form a complex interface to cellular processes outside the transcription machinery through protein-protein interactions. ooTFD contains information represented in TFD but also allows the representation of containment, composite, and interaction relationships between transcription factor polypeptides. It is designed to represent information about all transcription factors, both eukaryotic and prokaryotic, basal as well as regulatory factors, and multiprotein complexes as well as monomers.

Proper citation: object-oriented Transcription Factors Database (RRID:SCR_002435) Copy   

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http://cubic.bioc.columbia.edu/db/LOC3d/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. LOC3d is a database of predicted subcellular localization for eukaryotic proteins of known 3-D structure taken from the Protein Databank. Subcellular localization is currently predicted using four different methods: predictNLS (nuclear localization signal), LOChom (using homology), LOCkey (using keywords) and LOC3d (neural network based prediction). The reported localization is based on the method which predicts localization of a given protein with the highest confidence. LOCtree is a novel system of support vector machines (SVMs) that predict the subcellular localization of proteins, and DNA-binding propensity for nuclear proteins, by incorporating a hierarchical ontology of localization classes modeled onto biological processing pathways. Biological similarities are incorporated from the description of cellular components provided by the gene ontology consortium (GO). GO definitions have been simplified and tailored to the problem of protein sorting. Technically the ontology has been implemented using a decision tree with SVMs as the nodes. LOCtree, was extremely successful at learning evolutionary similarities among subcellular localization classes and was significantly more accurate than other traditional networks at predicting subcellular localization. Whenever available, LOCtree also reports predictions based on the following: 1) Nuclear localization signals found by PredictNLS, 2) Localization inferred using Prosite motifs and Pfam domains found in the protein, and 3) SWISS-PROT keywords associated with a protein. Localization is inferred in the last two cases using the entropy-based LOCkey algorithm. Additional information can be found in the LOCtree manuscript and associated PredictNLS and LOCkey publications.

Proper citation: Database oDatabase of Predicted Subcellular Localization for Eukaryotic PDB Chainsf Predicted Subcellular Localization for Eukaryotic PDB Chains (RRID:SCR_002831) Copy   

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http://rulai.cshl.edu/tools/ESE

A web-based analysis service for identifying exonic splicing enhancers in eukaryotic genes. ESEfinder accept sequences in the FASTA format. A typical mammalian gene is composed of several relatively short exons that are interrupted by much longer introns. To generate correct mature mRNAs, the exons must be identified and joined together precisely and efficiently, in a process that requires the coordinated action of five small nuclear (sn)RNAs (U1, U2, U4, U5 and U6) and more than 60 polypeptides. The inaccurate recognition of exon/intron boundaries or the failure to remove an intron generates aberrant mRNAs that are either unstable or code for defective or deleterious protein isoforms. Exonic enhancers are thought to serve as binding sites for specific serine/arginine-rich (SR) proteins, a family of structurally related and highly conserved splicing factors characterized by one or two RNA-recognition motifs (RRM) and by a distinctive C-terminal domain highly enriched in RS dipeptides (the RS domain). The RRMs mediate sequence-specific binding to the RNA, and so determine substrate specificity, whereas the RS domain appears to be involved mainly in protein-protein interactions. SR proteins bound to ESEs can promote exon definition by directly recruiting the splicing machinery through their RS domain and/or by antagonizing the action of nearby silencer elements. Sponsors: ESEfinder is supported by the Cold Spring Harbor Laboratory.

Proper citation: Exonic Splicing Enhancer Finder (RRID:SCR_002835) Copy   

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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