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http://cddb.nhlbi.nih.gov/cddb/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. This database is intended to serve as a learning tool to obtain curated information for the design of microarray targets to scan collecting duct tissues (human, rat, mouse). The database focuses on regulatory and transporter proteins expressed in the collecting duct, but when collecting duct proteins are a member of a larger family of proteins, common additional members of the family are included even if they have not been demonstrated to be expressed in the collecting duct. An Internet-accessible database has been devised for major collecting duct proteins involved in transport and regulation of cellular processes. The individual proteins included in this database are those culled from literature searches and from previously published studies involving cDNA arrays and serial analysis of gene expression (SAGE). Design of microarray targets for the study of kidney collecting duct tissues is facilitated by the database, which includes links to curated base pair and amino acid sequence data, relevant literature, and related databases. Use of the database is illustrated by a search for water channel proteins, aquaporins, and by a subsequent search for vasopressin receptors. Links are shown to the literature and to sequence data for human, rat, and mouse, as well as to relevant web-based resources. Extension of the database is dynamic and is done through a maintenance interface. This permits creation of new categories, updating of existing entries, and addition of new ones. CDDB is a database that organizes lists of genes found in collecting duct tissues from three mammalian species: human, rat, and mouse. Proteins are divided into categories by family relationships and functional classification, and each category is assigned a section in the database. Each section includes links to the literature and to sequence information for genes, proteins, expressed sequence tags, and related information. The user can peruse a section or use a search engine at the bottom of the web page to search the database for a name or abbreviation or for a link to a sequence. Each entry in the database includes links to relevant papers in the kidney and collecting duct literature. It uses links to PubMed to generate MEDLINE searches for retrieval of references. In addition, each entry includes links to curated sequence data available in LocusLink. Individual links are made to sequence and protein data for human, rat, and mouse. Links are then added as curated sequences become available for proteins identified in the renal collecting duct and for proteins identified in kidney and similar in function or homologous to proteins identified in the collecting duct.

Proper citation: Collecting Duct Database (RRID:SCR_000759) 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   

•   Source: SciCrunch Registry

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

Database that aggregates sample information for reference samples (e.g. Coriell Cell lines) and samples for which data exist in one of the EBI''''s assay databases such as ArrayExpress, the European Nucleotide Archive or PRoteomics Identificates DatabasE. It provides links to assays for specific samples, and accepts direct submissions of sample information. The goals of the BioSample Database include: # recording and linking of sample information consistently within EBI databases such as ENA, ArrayExpress and PRIDE; # minimizing data entry efforts for EBI database submitters by enabling submitting sample descriptions once and referencing them later in data submissions to assay databases and # supporting cross database queries by sample characteristics. The database includes a growing set of reference samples, such as cell lines, which are repeatedly used in experiments and can be easily referenced from any database by their accession numbers. Accession numbers for the reference samples will be exchanged with a similar database at NCBI. The samples in the database can be queried by their attributes, such as sample types, disease names or sample providers. A simple tab-delimited format facilitates submissions of sample information to the database, initially via email to biosamples (at) ebi.ac.uk. Current data sources: * European Nucleotide Archive (424,811 samples) * PRIDE (17,001 samples) * ArrayExpress (1,187,884 samples) * ENCODE cell lines (119 samples) * CORIELL cell lines (27,002 samples) * Thousand Genome (2,628 samples) * HapMap (1,417 samples) * IMSR (248,660 samples)

Proper citation: BioSample Database at EBI (RRID:SCR_004856) Copy   

•   Source: SciCrunch Registry

http://www.biosino.org/bodyfluid/

A database of bodily fluid proteome data. It contains information on proteins from humanplasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk, and amniotic fluid. Our body fluid protein database, Sys-BodyFluid, contains 11 body fluid proteomes, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk, and amniotic fluid. Over 10,000 proteins are included in the Sys-BodyFluid. These body fluid proteome data come from 50 peer-review publications of different laboratories all over the world. Protein annotation are provided including protein description, Gene ontology, Domain information, Protein sequence and involved pathway. User can access the proteome data by protein name, protein accession number, sequence similarity. In addition, user could perform query cross different body fluids to get more comprehensive understanding. The difference and similarity between these 11 body fluids are also analyzed. Thus , the Sys-BodyFluid database could serve as a reference database for body fluid research and disease proteomics. plasm, serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk, and amniotic fluid, protein, proteomics

Proper citation: Sys-BodyFluid (RRID:SCR_005335) Copy   

•   Source: SciCrunch Registry

http://ssd.rbvi.ucsf.edu/

The SSD has been developed to address the need for resources and tools for understanding large sets of superpositions in order to understand evolutionary relationships and to make predictions of function. We have therefore created the Structure Superposition Database (SSD) for accessing, viewing and understanding large sets of structure superposition data. It contains the results of pairwise, all-by-all superpositions of a representative set of 115 (beta/alpha) barrel structures (TIM barrels). The initial implementation of the SSD contains the results of pairwise, all-by-all superpositions of a representative set of 115 (/alpha)8 barrel structures (TIM barrels). Future plans call for extending the database to include representative structure superpositions for many additional folds. The SSD can be browsed with a user interface module developed as an extension to Chimera, an extensible molecular modeling program. Features of the user interface module facilitate viewing multiple superpositions together.

Proper citation: Structure Superposition Database (RRID:SCR_005236) Copy   

•   Source: SciCrunch Registry

http://transpogene.tau.ac.il/

A publicly available database of Transposed elements (TEs) which are located within protein-coding genes of 7 organisms: human, mouse, chicken, zebrafish, fruilt fly, nematode and sea squirt. Using TranspoGene the user can learn about the many aspects of the effect these TEs have on their hosting genes, such as: exonization events (including alternative splicing-related data), insertion of TEs into introns, exons, and promoters, specific location of the TE over the gene, evolutionary divergence of the TE from its consensus sequence and involvement in diseases. TranspoGene database is quickly searchable through its website, enables many kinds of searches and is available for download. TranspoGene contains information regarding specific type and family of the TEs, genomic and mRNA location, sequence, supporting transcript accession and alignment to the TE consensus sequence. The database also contains host gene specific data: gene name, genomic location, Swiss-Prot and RefSeq accessions, diseases associated with the gene and splicing pattern. The TranspoGene and microTranspoGene databases can be used by researchers interested in the effect of TE insertion on the eukaryotic transcriptome.

Proper citation: TranspoGene (RRID:SCR_005634) Copy   

•   Source: SciCrunch Registry

http://bigg.ucsd.edu/

A knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.

Proper citation: BiGG Database (RRID:SCR_005809) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://indel.bioinfo.sdu.edu.cn/gridsphere/gridsphere

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. Indel Flanking Region Database is an online resource for indels and the flanking regions of proteins in SCOP superfamilies, including amino acid sequences, lengths, locations, secondary structure constitutions, hydrophilicity / hydrophobicity, domain information, 3D structures and so on. It aims at providing a comprehensive dataset for analyzing the qualities of amino acid insertion/deletions(indels), substitutions and the relationship between them. The indels were obtained through the pairwise alignment of homologous structures in SCOP superfamilies. The IndelFR database contains 2,925,017 indels with flanking regions extracted from 373,402 structural alignment pairs of 12,573 non-redundant domains from 1053 superfamilies. IndelFR has already been used for molecular evolution studies and may help to promote future functional studies of indels and their flanking regions.

Proper citation: IndelFR - Indel Flanking Region Database (RRID:SCR_006050) Copy   

•   Source: SciCrunch Registry

http://possum.cbrc.jp/PoSSuM/

Relational database of all the discovered similar pairs in a huge number of protein-ligand binding sites with annotations of various types (e.g., CATH, SCOP, EC number, Gene ontology). They used a tremendously fast algorithm called SketchSort that enables the enumeration of similar pairs in a huge number of protein-ligand binding sites. They conducted all-pair similarity searches for 3.4 million known and potential binding sites using the proposed method and discovered over 24 million similar pairs of binding sites. PoSSuM enables rapid exploration of similar binding sites among structures with different global folds as well as similar ones. Moreover, PoSSuM is useful for predicting the binding ligand for unbound structures. Basically, the users can search similar binding pockets using two search modes: # Search K is useful for finding similar binding sites for a known ligand-binding site. Post a known ligand-binding site (a pair of PDB ID and HET code) in the PDB, and PoSSuM will search similar sites for the query site. # Search P is useful for predicting ligands that potentially bind to a structure of interest. Post a known protein structure (PDB ID) in the PDB, and PoSSuM will search similar known-ligand binding sites for the query structure.

Proper citation: PoSSuM (RRID:SCR_006109) Copy   

•   Source: SciCrunch Registry

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

A virus protein interactions database that collects and annotates all the interactions between human and viral proteins and integrates this information in the human protein interaction network. It uses the PSI-MI standard and is fully integrated with the MINT database. You can search for any viral or human protein by entering either common names or database identifiers or display a complete viral interactome.

Proper citation: VirusMINT (RRID:SCR_005987) Copy   

•   Source: SciCrunch Registry

http://www.kabatdatabase.com/

The Kabat Database determines the combining site of antibodies based on the available amino acid sequences. The precise delineation of complementarity determining regions (CDR) of both light and heavy chains provides the first example of how properly aligned sequences can be used to derive structural and functional information of biological macromolecules. The Kabat database now includes nucleotide sequences, sequences of T cell receptors for antigens (TCR), major histocompatibility complex (MHC) class I and II molecules, and other proteins of immunological interest. The Kabat Database searching and analysis tools package is an ASP.NET web-based portal containing lookup tools, sequence matching tools, alignment tools, length distribution tools, positional correlation tools and much more. The searching and analysis tools are custom made for the aligned data sets contained in both the SQL Server and ASCII text flat file formats. The searching and analysis tools may be run on a single PC workstation or in a distributed environment. The analysis tools are written in ASP.NET and C# and are available in Visual Studio .NET 2003/2005/2008 formats. The Kabat Database was initially started in 1970 to determine the combining site of antibodies based on the available amino acid sequences at that time. Bence Jones proteins, mostly from human, were aligned, using the now-known Kabat numbering system, and a quantitative measure, variability, was calculated for every position. Three peaks, at positions 24-34, 50-56 and 89-97, were identified and proposed to form the complementarity determining regions (CDR) of light chains. Subsequently, antibody heavy chain amino acid sequences were also aligned using a different numbering system, since the locations of their CDRs (31-35B, 50-65 and 95-102) are different from those of the light chains. CDRL1 starts right after the first invariant Cys 23 of light chains, while CDRH1 is eight amino acid residues away from the first invariant Cys 22 of heavy chains. During the past 30 years, the Kabat database has grown to include nucleotide sequences, sequences of T cell receptors for antigens (TCR), major histocompatibility complex (MHC) class I and II molecules and other proteins of immunological interest. It has been used extensively by immunologists to derive useful structural and functional information from the primary sequences of these proteins.

Proper citation: Kabat Database of Sequences of Proteins of Immunological Interest (RRID:SCR_006465) Copy   

•   Source: SciCrunch Registry

http://proportal.mit.edu/

ProPortal is a database containing genomic, metagenomic, transcriptomic and field data for the marine cyanobacterium Prochlorococcus. Our goal is to provide a source of cross-referenced data across multiple scales of biological organization--from the genome to the ecosystem--embracing the full diversity of ecotypic variation within this microbial taxon, its sister group, Synechococcus and phage that infect them. The site currently contains the genomes of 13 Prochlorococcus strains, 11 Synechococcus strains and 28 cyanophage strains that infect one or both groups. Cyanobacterial and cyanophage genes are clustered into orthologous groups that can be accessed by keyword search or through a genome browser. Users can also identify orthologous gene clusters shared by cyanobacterial and cyanophage genomes. Gene expression data for Prochlorococcus ecotypes MED4 and MIT9313 allow users to identify genes that are up or downregulated in response to environmental stressors. In addition, the transcriptome in synchronized cells grown on a 24-h light-dark cycle reveals the choreography of gene expression in cells in a ''natural'' state. Metagenomic sequences from the Global Ocean Survey from Prochlorococcus, Synechococcus and phage genomes are archived so users can examine the differences between populations from diverse habitats. Finally, an example of cyanobacterial population data from the field is included.

Proper citation: ProPortal (RRID:SCR_006112) Copy   

•   Source: SciCrunch Registry

http://tardis.nibio.go.jp/homstrad/

A curated database of structure-based alignments for homologous protein families. All known protein structure are clustered into homologous families (i.e., common ancestry), and the sequences of representative members of each family are aligned on the basis of their 3D structures using the programs MNYFIT, STAMP and COMPARER. These structure-based alignments are annotated with JOY and examined individually.

Proper citation: HOMSTRAD - Homologous Structure Alignment Database (RRID:SCR_006544) Copy   

•   Source: SciCrunch Registry

http://www.wwpdb.org/

Public global Protein Data Bank archive of macromolecular structural data overseen by organizations that act as deposition, data processing and distribution centers for PDB data. Members are: RCSB PDB (USA), PDBe (Europe) and PDBj (Japan), and BMRB (USA). This site provides information about services provided by individual member organizations and about projects undertaken by wwPDB. Data available via websites of its member organizations.

Proper citation: Worldwide Protein Data Bank (wwPDB) (RRID:SCR_006555) Copy   

•   Source: SciCrunch Registry

http://www.grt.kyushu-u.ac.jp/spad/

It is divided to four categories based on extracellular signal molecules (Growth factor, Cytokine, and Hormone) and stress, that initiate the intracellular signaling pathway. SPAD is compiled in order to describe information on interaction between protein and protein, protein and DNA as well as information on sequences of DNA and proteins. There are multiple signal transduction pathways: cascade of information from plasma membrane to nucleus in response to an extracellular stimulus in living organisms. Extracellular signal molecule binds specific intracellular receptor, and initiates the signaling pathway. Now, there is a large amount of information about the signaling pathway which controls the gene expression and cellular proliferation. We have developed an integrated database SPAD to understand the overview of signaling transduction.

Proper citation: Signaling Pathway Database (RRID:SCR_008243) Copy   

•   Source: SciCrunch Registry

http://escience.invitrogen.com/ipath/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 26, 2016. LINNEA Pathways is a user-friendly comprehensive online resource for gene- or protein-based scientific research. It is based on a total of 248 signaling and metabolic human biological pathway maps created for Invitrogen by GeneGo. The current version of iPath features 225 maps displaying human regulatory and metabolic pathways established in experimental literature produced by MetaCore from GeneGo, Inc. The map objects (proteins, genes, EC functions, and compounds) are connected via metabolic transformations and physical protein interactions, which were assembled by the GeneGo team of experienced annotators, geneticists, and biochemists. The pathways are organized in a vertical fashion following the general signaling path from signaling molecules and membrane receptors, via signal transduction cascades, to transcription factors and their gene targets. Following the natural organization of cellular machinery with highly interconnected pathways and modules, many maps are linked together via hyperlinked box symbols. Such linkage allows the reconstruction of a big picture view of human cell biology., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Invitrogen iPath (RRID:SCR_008120) Copy   

•   Source: SciCrunch Registry

http://itfp.biosino.org/itfp/

ITFP is an integrated transcription factor (TF) platform, which included abundant TFs and targets message of mammalian. Support vector machine (SVM) algorithm combined with error-correcting output coding (ECOC) algorithm was utilized to identify and classify transcription factor from protein sequence of Human, Mouse and Rat. For transcription factor targets, a reverse engineering method named ARACNE was used to derive potential interaction pairs between transcription factor and downstream regulated gene from Human, Mouse and Rat gene expression profile data. Detailed information of gene expression profile data can be found in help page. Moreover, all data provided by the platform is free for non-commercial users and can be downloaded through links on help page.

Proper citation: Intergrated Transcription Factor Platform (RRID:SCR_008119) Copy   

•   Source: SciCrunch Registry

http://wodaklab.org/iRefWeb/

iRefWeb is an interface to a relational database containing the latest build of the interaction Reference Index (iRefIndex) which integrates protein interaction data from nine different interaction databases: BioGRID, BIND, CORUM, DIP, HPRD, INTACT, MINT, MPPI, MPACT and OPHID. Integration is achieved through a rigorously documented procedure for mapping protein IDs across databases, enabling systematic backtracking of the links used to establish the identity of the interaction partners. The iRefWeb interface groups interaction records from the different databases into a single non-redundant view. In particular iRefWeb facilitates comparing interaction records as seen by the various source databases relative to the PubMeds they were annotated from. iRefWeb is one of several views of the iRefIndex resource. Data are also available in a tab-delimited plain-text format (PSI-MITAB) as well as planned releases of a PSI-XML formatted version and a Cytoscape plugin. Further details about the iRefIndex project as well as data downloads are available from here . The method used to build iRefIndex is described in a recent publication.

Proper citation: Interaction Reference Index Web Interface (RRID:SCR_008118) Copy   

•   Source: SciCrunch Registry

http://www.ingenuity.com/

A horizontally and vertically structured database that pulls scientific and medical information and describes it consistently using the Ingenuity Ontology. The Knowledge Base pulls information from journals, public molecular content databases, and textbooks. Data is curated and and integrated into the Knowledge Base .

Proper citation: Ingenuity Pathways Knowledge Base (RRID:SCR_008117) Copy   

•   Source: SciCrunch Registry