Tools   Select Another Resource Report Type

http://elm.eu.org

Computational biology resource for investigating candidate functional sites in eukarytic proteins. Functional sites which fit to the description linear motif are currently specified as patterns using Regular Expression rules. To improve the predictive power, context-based rules and logical filters are being developed and applied to reduce the amount of false positives. The current version of the ELM server provides core functionality including filtering by cell compartment, phylogeny, globular domain clash (using the SMART/Pfam databases) and structure. In addition, both the known ELM instances and any positionally conserved matches in sequences similar to ELM instance sequences are identified and displayed (see ELM instance mapper). Although the ELM resource contains a large collection of functional site motifs, the current set of motifs is not exhaustive.

Proper citation: Eukaryotic Linear Motif (RRID:SCR_003085) Copy   

•   Source: SciCrunch Registry

http://iimcb.genesilico.pl/MetaLocGramN/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023.A tool for subcellular localization prediction of Gram-negative proteins. You can also use MetaGramLocN via SOAP. SOAP enables you to invoke our method from scripts written in your programming language of choice.

Proper citation: MetaLocGramN (RRID:SCR_003154) Copy   

•   Source: SciCrunch Registry

http://www.psidev.info/

Initiative to define community standards for data representation in proteomics to facilitate data comparison, exchange and verification. The main organizational unit is the work group, with a Gel Electrophoresis (GEL) work group, a Mass Spectrometry (MS) work group, a Molecular Interactions (MI) work group, a Protein Modifications (MOD) work group, a Proteomics Informatics (PI) work group, and a Sample Processing (SP) work group. The Gel Electrophoresis (GEL) work group aims to develop reporting requirements that supplement the Minimum Information About a Proteomics Experiment (MIAPE) parent document, describing the minimum information that should be reported about gel-based experimental techniques used in proteomics. The group will also develop data formats for capturing MIAPE-compliant data about gel electrophoresis and informatics performed on gel images. The Mass Spectrometry Standards Working Group defines community data formats and controlled vocabulary terms facilitating data exchange and archiving in the field of proteomics mass spectrometry. A past achievement is the mzData standard, which captures mass spectrometry output data. mzData's aim is to unite the large number of current formats (pkl's, dta's, mgf's, .....) into a single format. mzData has been released but is now deprecated in favor of mzML. The Molecular Interactions workgroup is concentrating on improving the annotation and representation of molecular interaction data wherever it is published, be this in journal articles, authors web-sites or public domain databases; and improving the accessibility of molecular interaction data to the user community. By using a common standard data can be downloaded from multiple sources and easily combined using a single parser. The protein modification workgroup focuses on developing a consensus nomenclature and provide an ontology reconciling in a hierarchical representation the complementary descriptions of residue modifications. The protein modification ontology (PSI-MOD) is available in OBO format or in OBO.xml. A spreadsheet containing the mapping of the descriptive labels used in various databases and search engines, the consensus list of proposed short name for protein modifications established by collaborative effort of mass spectrometry community, and the proposed rules and recommendations for this nomenclature are available. These short names are included in the ontology as synonyms of the corresponding terms. The Proteomics Informatics Standards Group (PSI-PI) goals are to provide a set of minimal reporting requirements which augment the MIAPE reporting guidelines with respect to analysis of data derived from proteomics experiments; to provide vendor-neutral and standard formats for representing results of analyzing and processing experimental data; to foster adoption of the format by highlighting efforts made by vendors and individuals that utilize the format in their products. The remit of the Sample Processing Working Group is to produce reporting guidelines, data exchange formats and controlled vocabulary covering all separation techniques not considered to be "classical" one- or two-dimensional gel electrophoresis (cf. the Gel WG home page), along with other kinds of sample handling and processing (for example, "tagging" proteins or peptides, splitting, combining and storing samples). Where possible we seek to develop our products in collaboration with all proteomics stakeholders and, where relevant, developers from other standards communities, most notably metabolomics. * Minimum reporting requirements: The evolving Minimum Information About a Proteomics Experiment (MIAPE) documents offer guidelines on how to adequately report a proteomics experiment. It is expected that these documents will be published, and that the requirements within will be enforced by journals, compliant repositories and funders (cf. MIAME). * XML formats for data exchange: Derived from the FuGE general object model, the formats developed by this workgroup are designed to function both as standalone files and as part of a "parent" FuGE-ML document. These formats will facilitate data exchange between researchers, and submission to repositories or journals. * Controlled vocabularies (CVs) and ontology: Lists of clearly defined terms are crucial for the construction of unambiguously worded data files. In addition to providing supporting CVs for the individual data capture formats as part of the integrated PSI CV, the Sample Processing WG will contribute terms to the Functional Genomics Ontology (FuGO).

Proper citation: HUPO Proteomics Standards Initiative (RRID:SCR_003158) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/Tools/dalilite/indexhtml

Tool that computes optimal and suboptimal structural alignments between two protein structures. It will compare all chains in the first structure against all chains in the second (unless specific chain IDs are given). The resulting superimposed coordinate files can be downloaded or viewed interactively in Jmol. The Dali method optimizes a weighted sum of similarities of intramolecular distances. Suboptimal alignments do not overlap the optimal alignment or each other. Suboptimal alignments detected by the program are reported if the Z-score is above 2; they may be of interest if there are internal repeats in either structure. SOAP Web services are also available.

Proper citation: DaliLite Pairwise comparison of protein structures (RRID:SCR_003047) Copy   

•   Source: SciCrunch Registry

http://braintrap.inf.ed.ac.uk/braintrap/

This database contains information on protein expression in the Drosophila melanogaster brain. It consists of a collection of 3D confocal datasets taken from EYFP expressing protein trap Drosophila lines from the Cambridge Protein Trap project. Currently there are 884 brain scans from 535 protein trap lines in the database. Drosophila protein trap strains were generated by the St Johnston Lab and the Russell Lab at the University of Cambridge, UK. The piggyBac insertion method was used to insert constructs containing splice acceptor and donor sites, StrepII and FLAG affinity purification tags, and an EYFP exon (Venus). Brain images were acquired by Seymour Knowles-Barley, in the Armstrong Lab at the University of Edinburgh. Whole brain mounts were imaged by confocal microscopy, with a background immunohistochemical label added to aid the identification of brain structures. Additional immunohistochemical labeling of the EYFP protein using an anti-GFP antibody was also used in most cases. The trapped protein signal (EYFP / anti-GFP), background signal (NC82 label), and the merged signal can be viewed on the website by using the corresponding channel buttons. In all images the trapped protein / EYFP signal appears green and the background / NC82 channel appears magenta. Original .lsm image files are also available for download.

Proper citation: BrainTrap: Fly Brain Protein Trap Database (RRID:SCR_003398) Copy   

•   Source: SciCrunch Registry

http://brainarray.mbni.med.umich.edu/Brainarray/Database/ProbeMatchDB/ncbi_probmatch_para_step1.asp

Matches a list of microarray probes across different microrarray platforms (GeneChip, EST from different vendors, Operon Oligos) and species (human, mouse and rat), based on NCBI UniGene and HomoloGene. The capability to match protein sequence IDs has just been added to facilitate proteomic studies. The ProbeMatchDB is mainly used for the design of verification experiments or comparing the microarray results from different platforms. It can be used for finding equivalent EST clones in the Research Genetics sequence verified clone set based on results from Affymetirx GeneChips. It will also help to identify probes representing orthologous genes across human, mouse and rat on different microarray platforms.

Proper citation: ProbeMatchDB 2.0 (RRID:SCR_003433) Copy   

•   Source: SciCrunch Registry

http://www.cbs.dtu.dk/databases/NESbase

Database of proteins in which the presence of Leucine-rich nuclear export signal (NES) has been experimentally verified. It is curated from literature. Each NESbase entry contains information of whether NES was shown to be necessary and/or sufficient for export, and whether the export was shown to be mediated by the export receptor CRM1. The compiled information was used to make a sequence logo of the Leucine-rich NESs, displaying the conservation of amino acids within a window of 25 residues. Error reports and submissions of new data are most welcome!

Proper citation: NESbase (RRID:SCR_003268) Copy   

•   Source: SciCrunch Registry

http://prosite.expasy.org/

Database of protein families and domains that is based on the observation that, while there is a huge number of different proteins, most of them can be grouped, on the basis of similarities in their sequences, into a limited number of families. Proteins or protein domains belonging to a particular family generally share functional attributes and are derived from a common ancestor. It is complemented by ProRule, a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids. ScanProsite finds matches of your protein sequences to PROSITE signatures. PROSITE currently contains patterns and profiles specific for more than a thousand protein families or domains. Each of these signatures comes with documentation providing background information on the structure and function of these proteins. The database is available via FTP.

Proper citation: PROSITE (RRID:SCR_003457) Copy   

•   Source: SciCrunch Registry

http://caintegrator-info.nci.nih.gov/rembrandt

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 28,2023. An initiative to develop a molecular classification schema that is both clinically and biologically meaningful, based on gene expression and genomic data from tumors (Gliomas) of patients who will be prospectively followed through natural history and treatment phase of their illness. The study will also explore gene expression profiles to determine the responsiveness of the patients and correlate with discrete chromosomal abnormalities. The initiative was designed to obtain a large amount of molecular data on DNA and RNA of freshly collected tumor samples that were collected, processed and analyzed in a standardized fashion to allow for large-scale cross sample analysis. The sample collection is accompanied by careful and prospective clinical data acquisition, allowing a variety of matched molecular and clinical data permitting a wide variety of analyses. GMDI has accrued fresh frozen tumors in the retrospective phase (all from the Henry Ford Hospital, without germline DNA) and fresh frozen tumors in the prospective phase (from a variety of institutions). In addition to characterizing the samples from patients enrolled in GMDI, the microarray group has generated genomic-scale analyses of the many human and canine glioma initiating cells/glioma stem cells (GIC/GSC) lines, as well as many canine and murine normal neural stem cell (NSC) lines produced in laboratory.

Proper citation: Glioma Molecular Dignostic Initiatives (RRID:SCR_003329) Copy   

•   Source: SciCrunch Registry

http://nrresource.org

Collection of individual databases on members of the steroid and thyroid hormone receptor superfamily. Although the databases are located on different servers and are managed individually, they each form a node of the NRR. The NRR itself integrates the separate databases and allows an interactive forum for the dissemination of information about the superfamily. NRR Components: Androgen receptor, Estrogen receptor, Glucocorticoid receptor, Peroxisome proliferator, Steroid receptor protein, Thyroid receptor, Vitamin D receptor.

Proper citation: Nuclear Receptor Resource (RRID:SCR_003285) Copy   

•   Source: SciCrunch Registry

http://smart.embl.de/

Software tool for identification and annotation of genetically mobile domains and analysis of domain architectures.

Proper citation: SMART (RRID:SCR_005026) Copy   

•   Source: SciCrunch Registry

http://fields.scripps.edu/

Scientists at the Yates Lab at The Scripps Research Institute (TSRI) rely on information yielded by tandem mass spectrometry to identify proteins from complex mixtures. Using this powerful technique, researchers draw upon a cross section of fields to increase the scope, sensitivity, and throughput of technologies for practical proteomics. Biologists provide the questions that drive our research. By identifying complexes that are poorly understood or organism-wide issues requiring further exploration, we gain a theoretical understanding of issues that are tractable only through proteomic strategies. Analytical chemists and biochemists improve our tools for revealing the proteins present in biological samples. Targets for optimization include the isolations used to obtain proteins, the steps to generate peptides from these proteins, and the separation of peptides en route to the mass spectrometer. Chemistry is vital to increasing power of proteomic technology. Computer science yields tools on two scales. First, the sequence corresponding to each peptide''s tandem mass spectrum must be identified. Once those identifications have been completed, additional tools are needed to summarize and organize these identifications.

Proper citation: TSRI-Yates Lab (RRID:SCR_005699) Copy   

•   Source: SciCrunch Registry

http://sybil.sourceforge.net/

A web-based software package for comparative genomics.

Proper citation: Sybil (RRID:SCR_005593) Copy   

•   Source: SciCrunch Registry

http://www.topsan.org/

Collect, share, and distribute information about protein three-dimensional structures. It serves as a portal for the scientific community to learn about protein structures solved by SG centers, and also to contribute their expertise in annotating protein function. The premise of the TOPSAN project is that, no matter how much any individual knows about a particular protein, there are other members of the scientific community who know more about certain aspects of the same protein, and that the collective analyses from experts will be far more informative than any local group, let alone individual, could contribute. They believe that, if the members of the biological community are given the opportunity, authorship incentives, and an easy way to contribute their knowledge to the structure annotation, they would do so. Therefore, borrowing elements from successful, distributed, collaborative projects, such as Wikipedia (the free encyclopedia anyone can edit) and from other open source software development projects, TOPSAN will be a broad, collaborative effort to annotate protein structures, initially, those determined at the JCSG. They believe that the annotation of proteins solved by structural genomics consortia offers a unique opportunity to challenge the extant paradigm of how biological data is collected and distributed, and to connect structural genomics and structural biology to the entire biological research community. TOPSAN is designed to be scalable, modular and extensible. Furthermore, it is intended to be immediately useful in a simplistic way and will accommodate incremental improvements to functionality as usage becomes more sophisticated. Their annotation pages will offer the end user a combination of automatically generated as well as expert-curated annotations of protein structures. They will use available technology to increase the speed and granularity of the exchange of scientific ideas, and use incentive mechanisms that will encourage collaborative participation.

Proper citation: TOPSAN (RRID:SCR_005758) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/projects/gv/rbc/main.fcgi?cmd=init

The dbRBC database provides an open, publicly accessible platform for DNA and clinical data related to the human Red Blood Cells (RBC). A new bioinformatics resource, dbRBC, has been installed at the National Center of Biotechnology Information (NCBI). This resource combines the well established Blood Group Antigen Gene Mutation Database (BGMUT) with tools and interlinked resources developed at the NCBI. The main task of dbRBC is to provide access to publicly available genomic, protein and structural information linked to the red blood cell antigens. The site offers a number of resources: * BGMUT Database * Alignment Viewer * SBT Tool * Probe/Primer Resource * Typing Kit Interface * Obstacle

Proper citation: NCBI dbRBC (RRID:SCR_005959) Copy   

•   Source: SciCrunch Registry

http://www.clipz.unibas.ch/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 20,2019.Database and analysis environment for experimentally determined binding sites of RNA-binding proteins. It supports the automatic functional annotation of short reads resulting primarily from crosslinking and immunoprecipitation experiments (CLIP) performed with RNA-binding proteins in order to identify the binding sites of these proteins. The functional annotation could be also applied to short reads resulting from other types of experiments such as mRNA-Seq, Digital Gene Expression, small RNA cloning, etc. The platform enables visualization and mining of individual data sets as well as analysis involving multiple experimental data sets. The platform can support collaborative projects involving multiple users and groups of users as well as public and private datasets.

Proper citation: CLIPZ (RRID:SCR_005755) Copy   

•   Source: SciCrunch Registry

http://restraintsgrid.bmrb.wisc.edu/NRG/MRGridServlet

Original NMR (nuclear magnetic resonance) data as collected for over 2500 protein and nucleic acid structures with corresponding PDB entries. In addition to the original restraints, most of the distance, dihedral angle and RDC restraint data (>85%) were parsed, and those in over 500 entries were converted and filtered. The converted and filtered data sets constitute the Database Of Converted Restraints (DOCR) and the Filtered Restraints Database (FRED) respectively as described in the references. There are 9,672,968 parsed constraints in 7159 entries. (Mar. 2013)

Proper citation: NMR Restraints Grid (RRID:SCR_006127) Copy   

•   Source: SciCrunch Registry

http://www.umd.be/BRCA1/

The UMD-BRCA1/BRCA2 databases have been set up in a joined national effort through the network of 16 diagnostic laboratories to provide up-to-date information about mutations of the BRCA1 and BRCA2 genes identified in patients with breast and/or ovarian cancer. These databases currently contain published and unpublished information about the BRCA1/BRCA2 mutations reported in French diagnostic laboratories. This database includes 28 references and 5530 mutations (1440 different mutations and 786 protein variants) The databases of BRCA1 and BRCA2 mutations were built using the Universal Mutation Database tool. For each mutation, information is provided at several levels: * at the gene level: exon and codon number, wild type and mutant codon, mutation event, mutation name and, * at the protein level: wild type and mutant amino acid, binding domain, affected domain. If you want to submit a mutation, please contact R. Lidereau., S. Caputo. or E. Rouleau.

Proper citation: UMD-BRCA1/ BRCA2 databases (RRID:SCR_006128) Copy   

•   Source: SciCrunch Registry

http://isaac.bioapps.biozentrum.uni-wuerzburg.de/isaac/modules/genome/species.xhtml

Web based tool to enable the analysis of sets of genes, transcripts and proteins under different biological viewpoints and to interactively modify these sets at any point of the analysis. Detailed history and snapshot information allows tracing each action. One can switch back to previous states and perform new analyses. Sets can be viewed in the context of genomes, protein functions, protein interactions, pathways, regulation, diseases and drugs. Additionally, users can switch between species with an automatic, orthology based translation of existing gene sets. Sets as well as results of analyses can be exchanged between members of groups.

Proper citation: InterSpecies Analysing Application using Containers (RRID:SCR_006243) Copy   

•   Source: SciCrunch Registry

http://kronos.biol.uoa.gr/~mariak/dbDNA.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. An annotated and searchable collection of protein sequences for the families of DNA-binding proteins. DnaProt maximizes family information retrieval and helps reveal the relationships within the various functional binding classes. This classification system, implemented in an web-based management resource, is available for online DNA-binding pattern search and specific DNA-binding record retrieval. The database contains 3238 full-length sequences (retrieved from the SWISS-PROT database, release 38) that include, at least, a DNA-binding domain. Sequence entries are organized into families defined by PROSITE patterns, PRINTS motifs and de novo excised signatures. Combining global similarities and functional motifs into a single classification scheme, DNA-binding proteins are classified into 33 unique classes, which helps to reveal comprehensive family relationships. To maximize family information retrieval, DnaProt contains a collection of multiple alignments for each DNA-binding family while the recognized motifs can be used as diagnostically functional fingerprints. All available structural class representatives have been referenced. The resource was developed as a Web-based management system for online free access of customized data sets. Entries are fully hyperlinked to facilitate easy retrieval of the original records from the source databases while functional and phylogenetic annotation will be applied to newly sequenced genomes.

Proper citation: DnaProt (RRID:SCR_006230) Copy   

•   Source: SciCrunch Registry