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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   

•   Source: SciCrunch Registry

http://kinase.bioinformatics.tw/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. A comprehensive human kinase interactome and phospho-protein database. PhosphoPOINT also annotates any amino acids near the phosphorylation sites where a cSNP may cause a phosphorylation site to be lost, and at the same time identifies how such alteration of the phosphorylation site may lead to human disease. PhosphoPOINT integrates 4,195 phospho-proteins, 518 serine/threonine/tyrosine kinases, and their corresponding PPI datasets with the goal of delineating the interactions among kinases, their potential substrates and their interacting (phospho)-proteins. PhosphoPOINT has integrated human protein kinases, phospho-proteins, and PPI datasets with the goal to delineate four kinds of links among kinases. These include their interacting proteins, substrates, and substrates as well as interacting phospho-proteins. Some of these interacting proteins for kinases are phospho-proteins, which might have the potential to serve as substrates for the interacting kinases.

Proper citation: PhosphoPOINT (RRID:SCR_002109) 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://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://www.hiv.lanl.gov/content/immunology/index

An annotated, searchable collection of HIV-1 cytotoxic and helper T-cell epitopes and antibody binding sites, plus related tools and information. The goal of this database is to provide a comprehensive listing of defined HIV epitopes. These data are also printed in the HIV Molecular Immunology compendium, which is updated yearly and provided free of charge to scientific researchers, both by online download and as a printed copy. The data included in this database are extracted from the HIV immunology literature. HIV-specific B-cell and T-cell responses are summarized and annotated. Immunological responses are divided into three sections, CTL (CD8+), T helper (CD4+), and antibody. Within these sections, defined epitopes are organized by protein and binding sites within each protein, moving from left to right through the coding regions spanning the HIV genome. We include human responses to natural HIV infections, as well as vaccine studies in a range of animal models and human trials. Responses that are not specifically defined, such as responses to whole proteins or monoclonal antibody responses to discontinuous epitopes, are summarized at the end of each protein sub-section. Studies describing general HIV responses to the virus, but not to any specific protein, are included at the end of each section. The annotation includes information such as cross-reactivity, escape mutations, antibody sequence, TCR usage, functional domains that overlap with an epitope, immune response associations with rates of progression and therapy, and how specific epitopes were experimentally defined. Basic information such as HLA specificities for T-cell epitopes, isotypes of monoclonal antibodies, and epitope sequences are included whenever possible. All studies that we can find that incorporate the use of a specific monoclonal antibody are included in the entry for that antibody. A single T-cell epitope can have multiple entries, generally one entry per study. Finally, tables and maps of all defined linear epitopes relative to the HXB2 reference proteins are provided. Alignments of CTL, helper T-cell, and antibody epitopes are available through the search interfaces. Only responses to HIV-1 and HIV-2 are included in the database.

Proper citation: HIV Molecular Immunology Database (RRID:SCR_002893) 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://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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://eyesite.cryst.bbk.ac.uk/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. An information and modeling database for families of proteins that function in the eye. Homologues are collected from all species and clustered according to tissue type, function and sequence similarity. A principal feature of the site is structural annotations, which range from experimentally solved structures to close structural neighbors to distant structure predictions. Many pre-generated homology models are provided. Other features include domain architecture analysis and pre-generated sequence alignments, and the site is extensively linked to other bioinformatic resources on the web.

Proper citation: EyeSite (RRID:SCR_002669) Copy   

•   Source: SciCrunch Registry

https://simtk.org/home/allopathfinder

Software application and code base that allows users to compute likely allosteric pathways in proteins. The underlying assumption is that residues participating in allosteric communication should be fairly conserved and that communication happens through residues that are close in space. The initial application for the code provided was to study the allosteric communication in myosin. Myosin is a well-studied molecular motor protein that walks along actin filaments to achieve cellular tasks such as movement of cargo proteins. It couples ATP hydrolysis to highly-coordinated conformational changes that result in a power-stroke motion, or "walking" of myosin. Communication between a set of residues must link the three functional regions of myosin and transduce energy: the catalytic ATP binding region, the lever arm, and the actin-binding domain. They are investigating which residues are likely to participate in allosteric communication pathways. The application is a collection of C++/QT code, suitable for reproducing the computational results of the paper. (PMID 17900617) In addition, they provide input and alignment information to reproduce Figure 3 (a key figure) in the paper. Examples provided will show users how to use AlloPathFinder with other protein families, assumed to exhibit an allosteric communication. To run the application a multiple sequence alignment of representative proteins from the protein family is required along with at least one protein structure.

Proper citation: Allopathfinder (RRID:SCR_002702) Copy   

•   Source: SciCrunch Registry

http://www.drugbank.ca/

Bioinformatics and cheminformatics database that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information.

Proper citation: DrugBank (RRID:SCR_002700) Copy   

•   Source: SciCrunch Registry

http://romi.bu.edu/elisa/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. ELISA is an online database that combines functional annotation with structure and sequence homology modeling to place proteins into sequence-structure-function neighborhoods. The atomic unit of the database is a set of sequences and structural templates that those sequences encode. A graph that is built from the structural comparison of these templates is called PDUG (protein domain universe graph). It introduces a method of functional inference through a probabilistic calculation done on an arbitrary set of PDUG nodes. Further, all PDUG structures are mapped onto all fully sequenced proteomes allowing an easy interface for evolutionary analysis and research into comparative proteomics. ELISA is the first database with applicability to evolutionary structural genomics explicitly in mind.

Proper citation: Evolutionary Lineage Inferred from Structural Analysis (RRID:SCR_002343) Copy   

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

Database for genomes that have been completely sequenced, have active research community to contribute gene-specific information, or that are scheduled for intense sequence analysis. Includes nomenclature, map location, gene products and their attributes, markers, phenotypes, and links to citations, sequences, variation details, maps, expression, homologs, protein domains and external databases. All entries follow NCBI's format for data collections. Content of Entrez Gene represents result of curation and automated integration of data from NCBI's Reference Sequence project (RefSeq), from collaborating model organism databases, and from many other databases available from NCBI. Records are assigned unique, stable and tracked integers as identifiers. Content is updated as new information becomes available.

Proper citation: Entrez Gene (RRID:SCR_002473) Copy   

•   Source: SciCrunch Registry

http://www.geneatlas.org/gene/main.jsp

This website allows visitors to search for genes of interest based on their spatial expression patterns in the Postnatal Day 7 mouse brain. Geneatlas provides two searching tools: A graphical interface for customized spatial queries; A textual interface for querying annotated structures. Geneatlas is the product of a collaboration between researchers at Baylor College of Medicine, Rice University, and University of Houston.

Proper citation: Gene Atlas (RRID:SCR_008089) Copy   

•   Source: SciCrunch Registry

http://www.metnetdb.org/MetNet_db.htm

MetNet database contains information on networks of metabolic and regulatory and interactions in Arabidopsis. This information is based on input from biologists in their area of expertise. Types of interactions in MetNetDB include transcription, translation, protein modification, assembly, allosteric regulation, translocation from one subcellular compartment to another. Other fields describing the interactions are subcellular location, confidence, directionality, references, evidence, and synonyms. Data on entities (DNA, RNA, polypeptides, protein complexes, metabolites) are derived from web databases (gene related databases: TAIR, GO, MapMan/GabiPD; protein related databases: PPDB, AMPDB, AtNoPDB, AraPerox, PLprot, BRENDA; metabolite related databases: ChEBI, PubChem, KEGG, NCI compound library, NIST MS library), in some cases with additional annotation by experts. Network information from MetNetDB can be converted to an XML file by XML Builder. From this XML file, it can be transferred to exploRase, which uses the network in conjunction with statistical analysis of expression data; to Cytoscape/FCM, which finds cycles and pathways in the network, and visualizes and models it in combination with expression data; and to MetNetVR, where the network can be visualized in 3D.

Proper citation: Metabolic Network Exchange (RRID:SCR_008124) Copy   

•   Source: SciCrunch Registry

http://kinasedb.ontology.ims.u-tokyo.ac.jp

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. KinasePathwayDatabase is an integrated database concerning completed sequenced major eukaryotes, which contains the classification of protein kinases and their functional conservation and orthologous tables among species, protein-protein interaction data, domain information, structural information, and automatic pathway graph image interface. The protein-protein interactions are extracted by natural language processing (NLP) from abstracts using basic word pattern and protein name dictionary GENA: developed by our group. In this system, pathways are easily compared among species using protein interactions data more than 47,000 and orthologous tables.

Proper citation: Kinase Pathway Database (RRID:SCR_008199) Copy   

•   Source: SciCrunch Registry

http://www.roselab.jhu.edu/coil/

The Protein Coil Library is a library of protein structure fragments derived from the Protein Data Bank (PDB). The fragments in this library are those fragments in the PDB that cannot be classified as either alpha-helix or beta-strand. Three-dimensional structures as well as side-chain and backbone torsion angles are stored in the database. The Protein Coil Library allows rapid and comprehensive access to non-alpha-helix and non-beta-strand fragments contained in the Protein Data Bank (PDB). The library contains both sequence and structure information together with calculated torsion angles for both the backbone and side chains. Several search options are implemented, including a query function that uses output from popular PDB-culling servers directly. Additionally, several popular searches are stored and updated for immediate access. The library is a useful tool for exploring conformational propensities, turn motifs, and a recent model of the unfolded state. The library stores the complete torsion angle descriptions for the fragments as well as the three dimensional structures of the fragments themselves. The goal of extracting and pre-calculating this data is to allow for more straightforward investigation of peptide structure without the background of secondary structure elements. In addition to searching by PDB ID, it is possible to download a particular size class, perform a batch search of PDB/chain ID''s, or download precompiled lists of PDB ID''s of interest (PDB Select, etc.). For users interested in browsing the entire database at once or maintaining their own locally-updated copy of the library, FTP access instructions are also provided. The files stored in the coil library FTP site or returned after a batch search are organized heirarchically by PDB ID. This is done to reduce filesystem access times and fascilitate searches using the UNIX find utility. At the lowest directory level in the heirarchy, files are further sorted by fragment length. As a result, the number of files in a particular directory is generally less then 50, yielding relatively fast access on UNIX/Linux filesystems. The heirarchical organization is based on the middle two letters of the PDB ID. For example, hen egg lysozyme, which has a PDB ID of 1HEL, will be located in the directory h/he/. At the final level, fragments of varying sizes are stored in directories that correspond to their fragment length. Again, using lysozyme as an example, any seven-residue fragments, if they exist, will reside in the directory h/he/7/. Similarly, seven-residue fragments from 2HEX and 1HE0 will also be in this location. Sponsors: The Protein Coil Library is funded by Johns Hopkins University.

Proper citation: The Protein Coil Library (RRID:SCR_008233) Copy   

•   Source: SciCrunch Registry