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http://www.cbs.dtu.dk/services/ProP/

Web application which predicts arginine and lysine propeptide cleavage sites in eukaryotic protein sequences using an ensemble of neural networks. Furin-specific prediction is the default. It is also possible to perform a general proprotein convertase prediction.

Proper citation: ProP Server (RRID:SCR_014936) Copy   

•   Source: SciCrunch Registry

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   

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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   

•   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://biocomputer.bio.cuhk.edu.hk/T3DB/

Database aimed to annotate all bacterial Type III Secretion System (T3SS) related structure, effector, regulator, and auxiliary genes.

Proper citation: Type-III-Secretion-System related database (RRID:SCR_002941) 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   

•   Source: SciCrunch Registry

http://babelomics.bioinfo.cipf.es

An integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Version 4 of Babelomics integrates primary (normalization, calls, etc.) and secondary (signatures, predictors, associations, TDTs, clustering, etc.) analysis tools within an environment that allows relating genomic data and/or interpreting them by means of different functional enrichment or gene set methods. Such interpretation is made not only using functional definitions (GO, KEGG, Biocarta, etc.) but also regulatory information (from Transfac, Jaspar, etc.) and other levels of regulation such as miRNA-mediated interference, protein-protein interactions, text-mining module definitions and the possibility of producing de novo annotations through the Blast2GO system . Babelomics has been extensively re-engineered and now it includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. In this release GEPAS and Babelomics have integrated into a unique web application with many new features and improvements: * Data input: import and quality control for the most common microarray formats * Normalization and base calling: for the most common expression, tiling and SNP microarrays (Affymetrix and Agilent). * Transcriptomics: diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and time-series analysis. * Genotyping: stratification analysis, association, TDT. * Functional profiling: functional enrichment and gene set enrichment analysis with functional terms (GO, KEGG, Biocarta, etc.), regulatory (Transfac, Jaspar, miRNAs, etc.), text-mining, derived bioentities, protein-protein interaction analysis. * Integrative analysis: Different variables can be related to each other (e.g. gene expression to gnomic copy number) and the results subjected to functional analysis. Platform: Online tool

Proper citation: Babelomics (RRID:SCR_002969) Copy   

•   Source: SciCrunch Registry

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://pir.georgetown.edu/resid/

A comprehensive collection of annotations and structures for protein modifications including amino-terminal, carboxyl-terminal and peptide chain cross-link post-translational modifications. It provides: systematic and alternate names, atomic formulas and masses, enzyme activities generating the modifications, keywords, literature citations, Gene Ontology cross-references, Protein Information Resource (PIR) and SWISS-PROT protein sequence database feature table annotations, structure diagrams and molecular models. Each RESID Database entry presents a chemically unique modification and shows how that modification is currently annotated in the protein sequence databases, Swiss-Prot and the Protein Information Resource (PIR). The RESID Database provides a table of corresponding equivalent feature annotations that is used in the UniProt project, an international effort to combine the resources of the Swiss-Prot, TrEMBL and PIR. As an annotation tool, the RESID Database is used in standardizing and enhancing modification descriptions in the feature tables of Swiss-Prot entries.

Proper citation: RESID (RRID:SCR_003505) Copy   

•   Source: SciCrunch Registry

http://pin.mskcc.org

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., documented September 2, 2016. Relational database containing the compositions of multi-protein complexes in the nucleus of budding yeast and human cells. Its content is limited to information curated from the proteomics literature and primarily comprises of components of the general transcription and DNA repair machinery. In addition to database browsing and searching capabilities, the PINdb web portal also includes user-friendly interactive tools for comparative analysis of the composition of multiple protein complexes and for clustering and visualizing network of protein complexes. Currently, PINdb contains mostly protein complexes that may be involved in gene transcription. To facilitate comparative analyses and identification of protein complexes, the compositional information is integrated with standardized gene nomenclature, annotation and protein sequences from public databases. The PINdb web interface provides a number of tools for (1) comparison of protein complexes, (2) search for a protein complex by its published name or by a partial list of its components and (3) browsing specific subsets or a functional classification of the complexes.

Proper citation: PINdb (RRID:SCR_003348) Copy   

•   Source: SciCrunch Registry

http://sonorus.princeton.edu/hefalmp/

HEFalMp (Human Experimental/FunctionAL MaPper) is a tool developed by Curtis Huttenhower in Olga Troyanskaya's lab at Princeton University. It was created to allow interactive exploration of functional maps. Functional mapping analyzes portions of these networks related to user-specified groups of genes and biological processes and displays the results as probabilities (for individual genes), functional association p-values (for groups of genes), or graphically (as an interaction network). HEFalMp contains information from roughly 15,000 microarray conditions, over 15,000 publications on genetic and physical protein interactions, and several types of DNA and protein sequence analyses and allows the exploration of over 200 H. sapiens process-specific functional relationship networks, including a global, process-independent network capturing the most general functional relationships. Looking to download functional maps? Keep an eye on the bottom of each page of results: every functional map of any kind is generated with a Download link at the bottom right. Most functional maps are provided as tab-delimited text to simplify downstream processing; graphical interaction networks are provided as Support Vector Graphics files, which can be viewed using the Adobe Viewer, any recent version of Firefox, or the excellent open source Inkscape tool.

Proper citation: Human Experimental/FunctionAL MaPper: Providing Functional Maps of the Human Genome (RRID:SCR_003506) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.charite.de/synsysnet/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 19,2025. A curated database for synaptic proteins that provides adequate definitions of pre- and post-synaptic proteins, proteins present in sub-domains of the synapse, e.g. the synaptic vesicle and associated proteins, lipid rafts and postsynaptic density. In addition to data that was and will be gathered from the experiments conducted within SynSys - A European expertise Network on building the synapse, they have extracted and manually curated all relevant data on these proteins from other sources and provided an ontology for these. Novel splice forms are being identified that can be matched with proteomics data. Information on proteins, their 3D structure, binding small molecules Protein-Protein-Interactions (PPIs) and Compound-Protein-Interactions are integrated. Proteins or compounds can be searched and Interactive Networks can be visualized. The point Diseases present neurological diseases, to illustrate the role of SynSysNet in the medication.

Proper citation: SynSysNet (RRID:SCR_003180) Copy   

•   Source: SciCrunch Registry

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

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

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

•   Source: SciCrunch Registry

http://geno3d-pbil.ibcp.fr

An automatic web server for protein molecular modelling. Starting with a query protein sequence, the server performs the homology modelling in six successive steps: (i) identify homologous proteins with known 3D structures by using PSI-BLAST; (ii) provide the user all potential templates through a very convenient user interface for target selection; (iii) perform the alignment of both query and subject sequences; (iv) extract geometrical restraints (dihedral angles and distances) for corresponding atoms between the query and the template; (v) perform the 3D construction of the protein by using a distance geometry approach and (vi) finally send the results by e-mail to the user. The strategy used in Geno3D is comparative protein structure modelling by spatial restraints (distances and dihedral) satisfaction.

Proper citation: GENO3D (RRID:SCR_003183) Copy   

•   Source: SciCrunch Registry

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

Databases of protein sequences and 3D structures of proteins. Collection of sequences from several sources, including translations from annotated coding regions in GenBank, RefSeq and TPA, as well as records from SwissProt, PIR, PRF, and PDB.

Proper citation: NCBI Protein Database (RRID:SCR_003257) Copy   

•   Source: SciCrunch Registry