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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   

•   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   

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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.cs.ualberta.ca/~bioinfo/PA/GOSUB/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 30, 2015. Refer to Proteome Analyst 3.0. Subcellular Localization and GO General Molecular Function predictions for many model organism proteomes using Protein Analyst, with a very high coverage rate. When users blast their proteins against the database of results, they will not only be shown blast homologs from the model organisms, but also the Subcellular Localization and GO General Molecular Function predictions as well.

Proper citation: Proteome Analyst PA-GOSUB (RRID:SCR_008234) Copy   

•   Source: SciCrunch Registry

http://www.uwstructuralgenomics.org/

It is a specialized research center supported by the Protein Structure Initiative (PSI) of the National Institute of General Medical Sciences (NIGMS), one of the National Institutes of Health (NIH). PSI is a federal, university, and industry effort aimed at dramatically reducing the costs and lessening the time it takes to determine a three-dimensional protein structure. The long-range goal of PSI is to solve 10,000 protein structures in 10 years and to make the three-dimensional atomic-level structures of most proteins easily obtainable from knowledge of their corresponding DNA sequences. CESG is located within the Department of Biochemistry at the University of Wisconsin-Madison (Madison, WI) and the Department of Biochemistry at the Medical College of Wisconsin (Milwaukee, WI). CESG develops new methods and technologies to address unique eukaryotic bottlenecks and disseminates its methodologies and experimental results to the scientific community worldwide through: :- Cell-Free Protein Production Workshops :- Plasmids at PSI Materials Repository :- Posters Presented at Scientific Meetings :- Publications in PubMed / PubMed Central :- Sesame (LIMS) Available for Researchers :- Solved Structures in the Protein Data Bank :- Technology Dissemination Reports They have welcomed requests by researchers to solve eukaryotic protein structures, particularly medically relevant proteins, through our Online Structure Request System for Researchers. They have solved many community-nominated targets and deposited information about these targets in public databases and published on our investigations and findings. Sponsors: CESG is supported by NIH / NIGMS Protein Structure Initiative grant numbers U54 GM074901 and P50 GM064598.

Proper citation: CESG (RRID:SCR_008451) Copy   

•   Source: SciCrunch Registry

http://expasy.org/

Portal which provides access to scientific databases and software tools (i.e., resources) in different areas of life sciences including proteomics, genomics, phylogeny, systems biology, population genetics, transcriptomics etc. It contains resources from many different SIB groups as well as external institutions.

Proper citation: ExPASy Bioinformatics Resource Portal (RRID:SCR_012880) Copy   

•   Source: SciCrunch Registry

https://cryosparc.com/

Software integrated platform used for obtaining 3D structural information from single particle cryo-EM data. Enables automated, high quality and high-throughput structure discovery of proteins, viruses and molecular complexes for research and drug discovery.

Proper citation: cryoSPARC (RRID:SCR_016501) Copy   

•   Source: SciCrunch Registry

https://www.jax.org/research-and-faculty/resources/optogenetics-resource

Mouse lines expressing proteins that activate, inhibit or detect neuronal activity are available from The Jackson Laboratory Repository. Many of these strains have been generated by HHMI Janelia Farm GENIE Project or by Allen Institute for Brain Science.

Proper citation: Optogenetics Resource at JAX (RRID:SCR_017508) Copy   

•   Source: SciCrunch Registry

http://saxs.ifsc.usp.br/

Software tool as online calculator of molecular weight of proteins in dilute solution from experimental SAXS data measured on relative scale. Software package for easy processing of small angle X ray scattering data from mono disperse systems in diluted solution.

Proper citation: SAXS Molecular Weight (RRID:SCR_018137) Copy   

•   Source: SciCrunch Registry

http://cbm.bio.uniroma2.it/surface

A database containing the results of a large-scale protein annotation and local structural comparison project. A non-redundant set of protein chains is used to build a database of protein surface patches, defined as putative surface functional sites. Each patch is annotated with sequence and structure-derived information about function or interaction abilities. Users can search the annotations and the results of the surface patches comparisons stored in the DB based on PDB code, PROSITE pattern, or ligand. A new procedure for structure comparison is used to exert an all-versus-all patches comparison. Selection of the results obtained with stringent parameters offers a similarity score that can be used to associate different patches and may allow reliable annotation by similarity. protein, protein structure, structural comparison, protein ligand, protein surface, protein morphology

Proper citation: SURFACE: Surface Residues and Functions Annotated, Compared and Evaluated (RRID:SCR_007953) Copy   

•   Source: SciCrunch Registry

http://compbio.mcs.anl.gov/sentra/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A database of signal transduction proteins encoded in completely sequenced prokaryotic genomes. Sentra consists of two principal components, a manually curated list of signal transduction proteins in 202 completely sequenced prokaryotic genomes and an automatically generated listing of predicted signaling proteins in 235 sequenced genomes that are awaiting manual curation. In addition to two-component histidine kinases and response regulators, the database now lists manually curated Ser/Thr/Tyr protein kinases and protein phosphatases, as well as adenylate and diguanylate cyclases and c-di-GMP phosphodiesterases, as defined in several recent reviews. All entries in Sentra are extensively annotated with relevant information from public databases (e.g. UniProt, KEGG, PDB and NCBI). Sentra's infrastructure was redesigned to support interactive cross-genome comparisons of signal transduction capabilities of prokaryotic organisms from a taxonomic and phenotypic perspective and in the framework of signal transduction pathways from KEGG. Sentra leverages the PUMA2 system to support interactive analysis and annotation of signal transduction proteins by the users.

Proper citation: SENTRA: a database of prokaryotic signal transduction proteins (RRID:SCR_007922) 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