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SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.

(last updated: Oct 12, 2019)

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Resource NameResource TypeDescriptionKeywordsResource IDProper CitationParent OrganizationRelated ConditionFunding AgencyRelationReferenceWebsite StatusAlternate IDsAlternate URLsOld URLs
L2L Microarray Analysis ToolResource, data analysis service, data analysis software, data processing software, production service resource, analysis service resource, software application, database, service resource, storage service resource, software resource, data repository, data or information resourceNO LONGER IN SERVICE. Documented on August 26, 2019.\\n\\nDatabase of published microarray gene expression data, and a software tool for comparing that published data to a user''''s own microarray results. It is very simple to use - all you need is a web browser and a list of the probes that went up or down in your experiment. If you find L2L useful please consider contributing your published data to the L2L Microarray Database in the form of list files. L2L finds true biological patterns in gene expression data by systematically comparing your own list of genes to lists of genes that have been experimentally determined to be co-expressed in response to a particular stimulus - in other words, published lists of microarray results. The patterns it finds can point to the underlying disease process or affected molecular function that actually generated the observed changed in gene expression. Its insights are far more systematic than critical gene analyses, and more biologically relevant than pure Gene Ontology-based analyses. The publications included in the L2L MDB initially reflected topics thought to be related to Cockayne syndrome: aging, cancer, and DNA damage. Since then, the scope of the publications included has expanded considerably, to include chromatin structure, immune and inflammatory mediators, the hypoxic response, adipogenesis, growth factors, hormones, cell cycle regulators, and others. Despite the parochial origins of the database, the wide range of topics covered will make L2L of general interest to any investigator using microarrays to study human biology. In addition to the L2L Microarray Database, L2L contains three sets of lists derived from Gene Ontology categories: Biological Process, Cellular Component, and Molecular Function. As with the L2L MDB, each GO sub-category is represented by a text file that contains annotation information and a list of the HUGO symbols of the genes assigned to that sub-category or any of its descendants. You don''''t need to download L2L to use it to analyze your microarray data. There is an easy-to-use web-based analysis tool, and you have the option of downloading your results so you can view them at any time on your own computer, using any web browser. However, if you prefer, the entire L2L project, and all of its components, can be downloaded from the download page. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatiblemicroarray, gene expression, adipogenesis, biological, biological process, cancer, cell cycle regulator, cellular component, chromatin, cockayne syndrome, dna damage, growth factor, hormone, human biology, hypoxic response, immune mediator, inflammatory mediator, molecular function, molecular neuroanatomy resource, adipocyte, development, hypoxia, immune, inflammation, metabolism, mitogen, neuro, rna, vascular, transcription, tissue, splicing, mouse, human, rat, source code, statistical analysis, gene, chromatin structureSCR_013440(L2L Microarray Analysis Tool, RRID:SCR_013440)University of Washington; Seattle; USA Cockayne syndrome, DNA damage, Other, Aging, CancerCora May Poncin Foundation, NIGMSrelated to: Gene Ontology, listed by: Gene Ontology ToolsPMID:16168088Last checked upnif-0000-10463
Flash GviewerResource, software resourceFlash GViewer is a customizable Flash movie that can be easily inserted into a web page to display each chromosome in a genome along with the locations of individual features on the chromosomes. It is intended to provide an overview of the genomic locations of a specific set of features - eg. genes and QTLs associated with a specific phenotype, etc. rather than as a way to view all features on the genome. The features can hyperlink out to a detail page to enable to GViewer to be used as a navigation tool. In addition the bands on the chromosomes can link to defineable URL and new region selection sliders can be used to select a specific chromosome region and then link out to a genome browser for higher resolution information. Genome maps for Rat, Mouse, Human and C. elegans are provided but other genome maps can be easily created. Annotation data can be provided as static text files or produced as XML via server scripts. This tool is not GO-specific, but was built for the purpose of viewing GO annotation data. Platform: Online toolvisualization, chromosome, video, gene, qtl, genome, navitgation, phenotype, ontology or annotation visualizationSCR_012870(Flash Gviewer, RRID:SCR_012870)Medical College of Wisconsin; Wisconsin; USA related to: Gene Ontology, listed by: Gene Ontology ToolsLast checked upnlx_149333
GenNavResource, analysis service resource, data analysis service, service resource, production service resourceGenNav searches GO terms and annotated gene products, and provides a graphical display of a term's position in the GO DAG. Platform: Online toolimage, gene, ontology or annotation browserSCR_000147(GenNav, RRID:SCR_000147)National Library of Medicine related to: Gene Ontology, listed by: Gene Ontology ToolsLast checked downnlx_149123
High-Throughput GoMinerResource, data analysis service, production service resource, analysis service resource, web application, service resource, software resourceA web program that organizes lists of genes of interest (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology and automates the analysis of multiple microarrays then integrates the results across all of them in exportable output files and visualizations. High-Throughput GoMiner is an enhancement of GoMiner and is implemented with both a command line interface and a web interface. The program can also: efficiently perform automated batch processing of an arbitrary number of microarrays; produce a human- or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories; integrate the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories; provide a fast form of false discovery rate multiple comparisons calculation; and provide annotations and visualizations for relating transcription factor binding sites to genes and GO categories.term enrichment, gene ontology, gene, microarray, common variable immune deficiency, high-throughput, visualization, databaseSCR_000173(High-Throughput GoMiner, RRID:SCR_000173)National Cancer Institute NCIrelated to: Gene Ontology, GoMiner, listed by: Gene Ontology ToolsPMID:15998470Last checked upnlx_149300
TM4 Microarray Software Suite - TIGR MultiExperiment ViewerResource, data analysis software, data processing software, software application, data visualization software, software resource, software toolkitA desktop application for the analysis, visualization and data-mining of large-scale genomic data. It is a versatile microarray tool, incorporating sophisticated algorithms for clustering, visualization, classification, statistical analysis and biological theme discovery. MeV generates informative and interrelated displays of expression and annotation data from single or multiple experiments. A huge array of alrogithms are included in MeV modules, and are available at a button-click, such as K-means clustering, Hierarchical clustering, t-Tests, Significance Analysis of Microarrays, Gene Set Enrichment Analysis, and EASE. Extensive documentation is available for helping new users get started with MeV. A Quickstart Guide provides the tutorial a brand new person will need to get their first dataset loaded and displayed in the program. Returning MEV users will want to check out the release notes to see what new features are available in the latest versions of the program. Tutorials have been written about several of its more involved features.gene expression, analysis, annotation, classification, microarray, visualization, statistical analysis, clustering, biological theme, graphic, annotation, data miningSCR_001915(TM4 Microarray Software Suite - TIGR MultiExperiment Viewer, RRID:SCR_001915)Dana-Farber Cancer Institute , J. Craig Venter Institute , University of Washington; Seattle; USA NCRRrelated to: Gene Ontology, listed by: Gene Ontology ToolsReferences (2)Last checked upnif-0000-10486, SCR_005576, OMICS_00781
FunSimMatResource, data analysis service, data access protocol, production service resource, analysis service resource, database, web service, service resource, software resource, data or information resourceFunSimMat is a comprehensive resource of semantic and functional similarity values. It allows ranking disease candidate proteins for OMIM diseases and searching for functional similarity values for proteins (extracted from UniProt), and protein families (Pfam, SMART). FunSimMat provides several different semantic and functional similarity measures for each protein pair using the Gene Ontology annotation from UniProtKB and the Gene Ontology Annotation project at EBI (GOA). There are several search options available: Disease candidate prioritization: * Rank candidate proteins using any OMIM disease entry * Compare a list of proteins to any OMIM disease entry * Compare all human proteins to any OMIM disease entry Functional similarity: * Compare one protein / protein family to a list of proteins / protein families * Compare a list of GO terms to a list of proteins / protein families Semantic similarity: * For a list of GO terms, FunSimMat performs an all-against-all comparison and displays the semantic similarity values. FunSimMat provides an XML-RPC interface for performing automatic queries and processing of the results as well as a RestLike Interface. Platform: Online toolfunctional similarity value, protein family, protein similarity, semantic similarity value, similarity value, functional similarity, disease gene candidate prioritization, disease, protein, protein family, disease candidate prioritization, semantic similarity, gene ontology, visualization, annotation, database or data warehouseSCR_002729(FunSimMat, RRID:SCR_002729)Max-Planck-Institute for Informatics; Saarbrucken; Germany European Union, German National Genome Research Network, Klinische Forschergrupperelated to: Gene Ontology, listed by: Gene Ontology ToolsReferences (2)Last checked upnif-0000-02860
BioPerlResource, wiki, source code, narrative resource, software repository, software resource, software toolkit, data or information resourceBioPerl is a community effort to produce Perl code which is useful in biology. This toolkit of perl modules is useful in building bioinformatics solutions in Perl. It is built in an object-oriented manner so that many modules depend on each other to achieve a task. The collection of modules in the bioperl-live repository consist of the core of the functionality of bioperl. Additionally auxiliary modules for creating graphical interfaces (bioperl-gui), persistent storage in RDMBS (bioperl-db), running and parsing the results from hundreds of bioinformatics applications (Run package), software to automate bioinformatic analyses (bioperl-pipeline) are all available as Git modules in our repository. The BioPerl toolkit provides a library of hundreds of routines for processing sequence, annotation, alignment, and sequence analysis reports. It often serves as a bridge between different computational biology applications assisting the user to construct analysis pipelines. This chapter illustrates how BioPerl facilitates tasks such as writing scripts summarizing information from BLAST reports or extracting key annotation details from a GenBank sequence record. BioPerl includes modules written by Sohel Merchant of the GO Consortium for parsing and manipulating OBO ontologies. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatibleperl, biology, ontology, library, sequence, analysis, computational, application, pipeline, bioinformatics, sequence, annotation, module, life science, python, java, genome, software library, parse, manipulateSCR_002989(BioPerl, RRID:SCR_002989) European Bioinformatics Institute , Duke University; North Carolina; USA NHGRI, NIGMSrelated to: Gene Ontology, OBO, listed by: Gene Ontology ToolsPMID:12368254Last checked downnif-0000-30188
GOToolBox Functional Investigation of Gene DatasetsResource, software resource, source code, service resourceThe GOToolBox web server provides a series of programs allowing the functional investigation of groups of genes, based on the Gene Ontology resource. The web version of the GOToolBox is free for non-commercial users only. Users from commercial companies are allowed to use the site during a reasonable testing period. For a regular use of the web version, a license fee should be paid. We have developed methods and tools based on the Gene Ontology (GO) resource allowing the identification of statistically over- or under-represented terms in a gene dataset; the clustering of functionally related genes within a set; and the retrieval of genes sharing annotations with a query gene. GO annotations can also be constrained to a slim hierarchy or a given level of the ontology. The source codes are available upon request, and distributed under the GPL license. Platform: Online toolgene, annotation, statistical analysis, slimmer-type tool, function, cluster, gene association, gene ontologySCR_003192(GOToolBox Functional Investigation of Gene Datasets, RRID:SCR_003192)Center for Genomic Regulation; Barcelona; Spain Action Bioinformatique inter-EPST, Fondation pour la Recherche Medicale, French Ministere de l'Education de la Recherche et de la Technologierelated to: Gene Ontology, listed by: Gene Ontology ToolsPMID:15575967Last checked upnif-0000-30623
StRAnGERResource, software resource, software application, data analysis software, data processing softwareStRAnGER (Statistical Ranking of ANotated Genomic Experimental Results) is a web application for the automated statistical analysis of annotated gene profiling experiments, exploiting controlled biological vocabularies, like the Gene Ontology or the KEGG pathways terms. Starting from annotated lists of differentially expressed genes StRAnGER repartitions and reorders the initial distribution of terms to define a new distribution of elements where each element pools terms holding the same enrichment score. The elements are then prioritized according to StRAnGER''''s algorithm and, by applying bootstrapping techniques, a corrected measure of the statistical significance of these elements is derived, enabling the selection of terms mapped to these elements, unambiguously associated with respective significant gene sets. Besides their high statistical score, another selection criterion for the terms is the number of their members, something that incurs a biological prioritization in line with a Systems Biology context. Platform: Online toolcontrolled vocabulary, functional analysis, genomics, annotation, visualization, statistical analysis, term enrichment, ontology or annotation visualizationSCR_004247(StRAnGER, RRID:SCR_004247)National Hellenic Research Foundation related to: Gene Ontology, listed by: Gene Ontology ToolsPMID:21293737Last checked upnlx_25932
Short Time-series Expression Miner (STEM)Resource, software resource, software application, data processing softwareThe Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments (~8 time points or fewer). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database supporting GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category or user defined gene set, identifying which temporal expression profiles were enriched for these genes. (Note: While STEM is designed primarily to analyze data from short time course experiments it can be used to analyze data from any small set of experiments which can naturally be ordered sequentially including dose response experiments.) Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatiblestatistical analysis, term enrichment, visualization, cluster, compare, short time series, gene expression, microarray, expression profile, gene, gene ontology, gene enrichment analysesSCR_005016(Short Time-series Expression Miner (STEM), RRID:SCR_005016)Carnegie Mellon University; Pennsylvania; USA NIAID, NSFrelated to: Gene Ontology, listed by: Gene Ontology ToolsReferences (2)Last checked upnlx_97053
GORetrieverResource, analysis service resource, data analysis service, service resource, production service resourceGORetriever is used to find all of the GO annotations corresponding to a list of user-supplied protein identifiers. GORetriever produces a list of proteins and their annotations and a separate list of entries with no GO annotation. Platform: Online toolgene, annotation, protein, ontology or annotation search engineSCR_005633(GORetriever, RRID:SCR_005633)AgBase Mississippi State University; Mississippi; USA, MSU Bagley College of Engineering, MSU College of College of Veterinary Medicine, MSU Life Science and Biotechnology Institute, MSU Office of Research, USDArelated to: Gene Ontology, listed by: Gene Ontology ToolsReferences (2)Last checked upnlx_149140
Gene Ontology Browsing Utility (GOBU)Resource, software resource, source codeGene Ontology Browsing Utility (GOBU) (GOBU) is a Java-based software program for integrating biological annotation catalogs under an extendable software architecture. Users may interact with the Gene Ontology and user-defined hierarchy data of genes, and then use its plugins to (and not limited to) (1) browse the GO hierarchy with user defined data, (2) browse GO-oriented expression levels in the user data, (3) compute GO enrichment, and/or (4) customize data reporting. A set of classes and utility functions has been established so that a customized program can be made as a plugin or a command-line tool that programmically manipulate the Gene Ontology and specified user data. See the source code repository for examples. Reference Lin WD, Chen YC, Ho JM, Hsiao CD. GOBU: Toward an Integration Interface for Biological Objects. Journal of Information Science and Engineering. 2006 22(1):19-29. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatibleannotation, gene, browser, computation, visualization, software library, statistical analysis, term enrichment, ontology or annotation browser, ontology or annotation visualizationSCR_005662(Gene Ontology Browsing Utility (GOBU), RRID:SCR_005662)Academia Sinica; Taipei; Taiwan related to: Gene Ontology, listed by: Gene Ontology ToolsLast checked downnlx_149098
GeneToolsResource, data analysis service, bibliography, production service resource, analysis service resource, topical portal, text mining tool, service resource, portal, data or information resourceGeneTools is a collection of web-based tools that brings together information from a broad range of resources, and provides this in a manner particularly useful for genome-wide analyses. Today, the two main tools connected to this database are the NMC Annotation Database V2.0 and eGOn V2.0 (explore Gene Ontology). The NMC Annotation Database V2.0 provides information from UniGene, EntrezGene, SwissProt and Gene Ontology (GO). Major features are: * Single search/Batch search, extraction of data for single or batches of genes. * Manage reporter lists: in folders and share selected lists with other users. * Manual GO Annotation: add your own Gene Ontology (GO) annotations to genes of interest. * Export: to Excel, text or XML format. eGOn V2.0 facilitates interpretation of GO annotation. GO terms are retrieved in batch modus from EntrezGene and the GO database and displayed in the GO directed acyclic hierarchical graph (DAG). Essential features of eGOn V2.0 are: * Visualization: gene annotations are visualized in the GO DAG or as a table view. The granularity of the GO DAG can be edited freely by the user. * Filtering: GO annotations can be filtered on evidence codes. * Include user defined GO annotations: previously added to the Annotation database. * Statistical analysis: Several gene lists are analyzed simultaneously to compare the distribution of the annotated genes over the GO hierarchy. Statistical tests are implemented to allow the user to compute GO annotation dissimilarity within or between gene lists. * Connection to Annotation database: Links to Annotation database gene and protein information are offered directly from the GO DAG or in exported data. * Export: GO DAG information, statistical results and gene and protein information can be exported in excel, text or XML format. Platform: Online toolgenome-wide analyses, annotation, gene, visualization, ontology or annotation browser, ontology or annotation search engine, ontology or annotation visualization, database or data warehouse, statistical analysis, term enrichment, browser, search engineSCR_005663(GeneTools, RRID:SCR_005663)Norwegian University of Science and Technology; Trondheim; Norway National Council on Cardiovascular Diseases, Norwegian Research Council, Norwegian University of Science and Technology; Trondheim; Norwayrelated to: Gene Ontology, listed by: Gene Ontology ToolsPMID:17062145Last checked downnlx_149102, nif-0000-30011, SCR_002911, nif-0000-00407, SCR_007388
go-mooseResource, software resource, software application, data analysis software, data processing softwarego-moose is intended as a replacement for the aging go-perl and go-db-perl Perl libraries. It is written using the object oriented Moose libraries. It can be used for performing a number of analyses on GO data, including the remapping of GO annotations to a selected subset of GO terms. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatiblesoftware library, slimmer-type tool, analysis, gene ontology, other analysisSCR_005666(go-moose, RRID:SCR_005666)SourceForge , Berkeley Bioinformatics Open-Source Projects , Lawrence Berkeley National Laboratory related to: Gene Ontology, go-perl, go-db-perl, listed by: Gene Ontology ToolsLast checked upnlx_149189
Network Ontology AnalysisResource, analysis service resource, data analysis service, service resource, production service resourceNetwork Ontology Analysis (NOA) (abbreviated to NOA) is a freely available collection of Gene Ontology tools aiming to analyze functions of gene network instead of gene list. Network rewiring facilitates the function changes between conditions even with the same gene list. Therefore, it is necessary to annotate the specific function of networks by considering the fundamental roles of interactions from the viewpoint of systems biology. NOA is such a novel functional enrichment analysis method capable to handle both dynamic and static networks. The application of NOA in biological networks shows that NOA can not only capture changing functions in rewiring networks but also find more relevant and specific functions in traditional static networks. Platform: Online toolgene, ontology, ontology or annotation browser, statistical analysis, term enrichment, browserSCR_005667(Network Ontology Analysis, RRID:SCR_005667)Chinese Academy of Sciences; Beijing; China Chief Scientist Program of SIBS, Knowledge Innovation Program of CAS, NSFC, Shanghai NSFrelated to: Gene Ontology, listed by: Gene Ontology ToolsPMID:21543451Last checked upnlx_149105
OBO-EditResource, software resource, software application, source codeOBO-Edit is an open source, platform-independent application written in Java for viewing and editing any OBO format ontologies. OBO-Edit is a graph-based tool; its emphasis on the overall graph structure of an ontology provides a friendly interface for biologists, and makes OBO-Edit excellent for the rapid generation of large ontologies focusing on relationships between relatively simple classes. The UI components are cleanly separated from the data model and data adapters, so these can be reused in other applications. The oboedit foward-chaining reasoner can also be used independently (for example, for traversing ontology graphs). OBO-Edit uses the OBO format flat file. See the GO wiki,, for instructions on downloading the source code. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatibleontology, browser, search engine, visualization, editor, software library, ontology or annotation browser, ontology or annotation search engine, ontology or annotation visualization, ontology or annotation editorSCR_005668(OBO-Edit, RRID:SCR_005668)Gene Ontology related to: OBO, Ontology Lookup Service, Phenote: A Phenotype Annotation Tool using Ontologies, listed by: Gene Ontology ToolsPMID:17545183Last checked upnlx_149107http://org.geneontology.oboedit
Onto-CompareResource, data analysis service, production service resource, analysis service resource, database, service resource, data or information resourceMicroarrays are at the center of a revolution in biotechnology, allowing researchers to screen tens of thousands of genes simultaneously. Typically, they have been used in exploratory research to help formulate hypotheses. In most cases, this phase is followed by a more focused, hypothesis driven stage in which certain specific biological processes and pathways are thought to be involved. Since a single biological process can still involve hundreds of genes, microarrays are still the preferred approach as proven by the availability of focused arrays from several manufacturers. Since focused arrays from different manufacturers use different sets of genes, each array will represent any given regulatory pathway to a different extent. We argue that a functional analysis of the arrays available should be the most important criterion used in the array selection. We developed Onto-Compare as a database that can provide this functionality, based on the GO nomenclature. Compare commercially available microarrays based on GO. User account required. Platform: Online toolmicroarray, gene, ontology, gene expression, data-mining, browser, visualization, analysis, compare, search engine, ontology or annotation browser, ontology or annotation search engine, ontology or annotation visualization, database or data warehouse, other analysis, compare commercially available microarrays based on goSCR_005669(Onto-Compare, RRID:SCR_005669)Wayne State University; Michigan; USA related to: Gene Ontology, listed by: Gene Ontology ToolsReferences (2)Last checked downnlx_149108
Onto-ExpressResource, data analysis service, database, analysis service resource, production service resource, service resource, data or information resourceThe typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independently of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function and chromosome location. Statistical significance values are calculated for each category. We demonstrated the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer data sets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms (Draghici, Genomics, 81(2), 2003). Other results obtained with Onto-Express can be found in Khatri, Genomics. 79(2), 2002. Custom level of abstraction of the Gene Ontology. User account required. Platform: Online toolmicroarray, gene, ontology, gene expression, biochemical function, biological process, cellular role, cellular component, molecular function, chromosome location, java, data-mining, browser, visualization, analysis, statistical analysis, term enrichment, search engine, other analysis, ontology or annotation browser, ontology or annotation search engine, ontology or annotation visualization, database or data warehouse, custom level of abstraction of the gene ontologySCR_005670(Onto-Express, RRID:SCR_005670)Wayne State University; Michigan; USA NICHD, Wayne State University School of Medicine; Michigan; USArelated to: Gene Ontology, listed by: Gene Ontology ToolsReferences (3)Last checked downnlx_149110
CGAP GO BrowserResource, data set, service resource, data or information resourceWith the CGAP GO browser, you can browse through the GO vocabularies, and find human and mouse genes assigned to each term. GO data updated every few months. Platform: Online toolgene, biological process, cellular component, molecular function, browser, ontology, ontology or annotation browserSCR_005676(CGAP GO Browser, RRID:SCR_005676)Cancer Genome Anatomy Project NCIrelated to: Gene Ontology, listed by: Gene Ontology ToolsLast checked upnlx_149116
COBrAResource, software resourceCOBrA is a Java-based ontology editor for bio-ontologies that distinguishes itself from other editors by supporting the linking of concepts between two ontologies, and providing sophisticated analysis and verification functions. In addition to the Gene Ontology and Open Biology Ontologies formats, COBrA can import and export ontologies in the Semantic Web formats RDF, RDFS and OWL. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatibleontology, anatomy, browser, ontology or annotation browserSCR_005677(COBrA, RRID:SCR_005677)University of Edinburgh; Scotland; United Kingdom BBSRCrelated to: Gene Ontology, OBO, listed by: Gene Ontology ToolsPMID:15513995Last checked downnlx_149117
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