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http://www.ici.upmc.fr/cluego/

A Cytoscape plug-in that visualizes the non-redundant biological terms for large clusters of genes in a functionally grouped network. It can be used in combination with GOlorize. The identifiers can be uploaded from a text file or interactively from a network of Cytoscape. The type of identifiers supported can be easily extended by the user. ClueGO performs single cluster analysis and comparison of clusters. From the ontology sources used, the terms are selected by different filter criteria. The related terms which share similar associated genes can be combined to reduce redundancy. The ClueGO network is created with kappa statistics and reflects the relationships between the terms based on the similarity of their associated genes. On the network, the node colour can be switched between functional groups and clusters distribution. ClueGO charts are underlying the specificity and the common aspects of the biological role. The significance of the terms and groups is automatically calculated. ClueGO is easy updatable with the newest files from Gene Ontology and KEGG. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ClueGO (RRID:SCR_005748) Copy   

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http://www.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=clench

Cluster Enrichment (CLENCH) allows A. thaliana researchers to perform automated retrieval of GO annotations from TAIR and calculate enrichment of GO terms in gene group with respect to a reference set. Before calculating enrichment, CLENCH allows mapping of the returned annotations to arbitrary coarse levels using GO slim term lists (which can be edited by the user) and a local installation of GO. Platform: Windows compatible, Linux compatible,

Proper citation: CLENCH (RRID:SCR_005735) Copy   

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http://wiki.geneontology.org/index.php/GOlr

A public Solr index for the Gene Ontology. This index will replace some of the query functionality for GOOSE as well as become the new backend for AmiGO 2 and other services.

Proper citation: GOlr (RRID:SCR_003939) Copy   

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http://function.princeton.edu/GOLEM/index.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 7, 2017. Welcome to the home of GOLEM: An interactive, graphical gene-ontology visualization, navigation,and analysis tool on the web. GOLEM is a useful tool which allows the viewer to navigate and explore a local portion of the Gene Ontology (GO) hierarchy. Users can also load annotations for various organisms into the ontology in order to search for particular genes, or to limit the display to show only GO terms relevant to a particular organism, or to quickly search for GO terms enriched in a set of query genes. GOLEM is implemented in Java, and is available both for use on the web as an applet, and for download as a JAR package. A brief tutorial on how to use GOLEM is available both online and in the instructions included in the program. We also have a list of links to libraries used to make GOLEM, as well as the various organizations that curate organism annotations to the ontology. GOLEM is available as a .jar package and a macintosh .app for use on- or off- line as a stand-alone package. You will need to have Java (v.1.5 or greater) installed on your system to run GOLEM. Source code (including Eclipse project files) are also available. GOLEM (Gene Ontology Local Exploration Map)is a visualization and analysis tool for focused exploration of the gene ontology graph. GOLEM allows the user to dynamically expand and focus the local graph structure of the gene ontology hierarchy in the neighborhood of any chosen term. It also supports rapid analysis of an input list of genes to find enriched gene ontology terms. The GOLEM application permits the user either to utilize local gene ontology and annotations files in the absence of an Internet connection, or to access the most recent ontology and annotation information from the gene ontology webpage. GOLEM supports global and organism-specific searches by gene ontology term name, gene ontology id and gene name. CONCLUSION: GOLEM is a useful software tool for biologists interested in visualizing the local directed acyclic graph structure of the gene ontology hierarchy and searching for gene ontology terms enriched in genes of interest. It is freely available both as an application and as an applet.

Proper citation: GOLEM An interactive, graphical gene-ontology visualization, navigation, and analysis tool (RRID:SCR_003191) Copy   

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http://mouseNET.princeton.edu

A functional network for laboratory mouse based on integration of diverse genetic and genomic data. It allows the users to accurately predict novel functional assignments and network components. MouseNET uses a probabilistic Bayesian algorithm to identify genes that are most likely to be in the same pathway/functional neighborhood as your genes of interest. It then displays biological network for the resulting genes as a graph. The nodes in the graph are genes (clicking on each node will bring up SGD page for that gene) and edges are interactions (clicking on each edge will show evidence used to predict this interaction). Most likely, the first results to load on the results page will be a list of significant Gene Ontology terms. This list is calculated for the genes in the biological network created by the mouseNET algorithm. If a gene ontology term appears on this list with a low p-value, it is statistically significantly overrepresented in this biological network. The graph may be explored further. As you move the mouse over genes in the network, interactions involving these genes are highlighted.If you click on any of the highlighted interactions graph, evidence pop-up window will appear. The Evidence pop-up lists all evidence for this interaction, with links to the papers that produced this evidence - clicking these links will bring up the relevant source citation(s) in PubMed.

Proper citation: MouseNET (RRID:SCR_003357) Copy   

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http://genespeed.ccf.org/home/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. Database and customized tools to study the PFAM protein domain content of the transcriptome for all expressed genes of Homo sapiens, Mus musculus, Drosophila melanogaster, and Caenorhabditis elegans tethered to both a genomics array repository database and a range of external information resources. GeneSpeed has merged information from several existing data sets including the Gene Ontology Consortium, InterPro, Pfam, Unigene, as well as micro-array datasets. GeneSpeed is a database of PFAM domain homology contained within Unigene. Because Unigene is a non-redundant dbEST database, this provides a wide encompassing overview of the domain content of the expressed transcriptome. We have structured the GeneSpeed Database to include a rich toolset allowing the investigator to study all domain homology, no matter how remote. As a result, homology cutoff score decisions are determined by the scientist, not by a computer algorithm. This quality is one of the novel defining features of the GeneSpeed database giving the user complete control of database content. In addition to a domain content toolset, GeneSpeed provides an assortment of links to external databases, a unique and manually curated Transcription Factor Classification list, as well as links to our newly evolving GeneSpeed BetaCell Database. GeneSpeed BetaCell is a micro-array depository combined with custom array analysis tools created with an emphasis around the meta analysis of developmental time series micro-array datasets and their significance in pancreatic beta cells.

Proper citation: GeneSpeed- A Database of Unigene Domain Organization (RRID:SCR_002779) Copy   

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http://www.flymine.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. Integrated database of genomic, expression and protein data for Drosophila, Anopheles, C. elegans and other organisms. You can run flexible queries, export results and analyze lists of data. FlyMine presents data in categories, with each providing information on a particular type of data (for example Gene Expression or Protein Interactions). Template queries, as well as the QueryBuilder itself, allow you to perform searches that span data from more than one category. Advanced users can use a flexible query interface to construct their own data mining queries across the multiple integrated data sources, to modify existing template queries or to create your own template queries. Access our FlyMine data via our Application Programming Interface (API). We provide client libraries in the following languages: Perl, Python, Ruby and & Java API

Proper citation: FlyMine (RRID:SCR_002694) Copy   

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http://funsimmat.bioinf.mpi-inf.mpg.de

FunSimMat 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 tool

Proper citation: FunSimMat (RRID:SCR_002729) Copy   

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http://www.greenphyl.org/

A database designed for plant comparative and functional genomics based on complete genomes. It comprises complete proteome sequences from the major phylum of plant evolution. The clustering of these proteomes was performed to define a consistent and extensive set of homeomorphic plant families. Based on this, lists of gene families such as plant or species specific families and several tools are provided to facilitate comparative genomics within plant genomes. The analyses follow two main steps: gene family clustering and phylogenomic analysis of the generated families. Once a group of sequences (cluster) is validated, phylogenetic analyses are performed to predict homolog relationships such as orthologs and ultraparalogs.

Proper citation: GreenPhylDB (RRID:SCR_002834) Copy   

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http://insitu.fruitfly.org/cgi-bin/ex/insitu.pl

Database of embryonic expression patterns using a high throughput RNA in situ hybridization of the protein-coding genes identified in the Drosophila melanogaster genome with images and controlled vocabulary annotations. At the end of production pipeline gene expression patterns are documented by taking a large number of digital images of individual embryos. The quality and identity of the captured image data are verified by independently derived microarray time-course analysis of gene expression using Affymetrix GeneChip technology. Gene expression patterns are annotated with controlled vocabulary for developmental anatomy of Drosophila embryogenesis. Image, microarray and annotation data are stored in a modified version of Gene Ontology database and the entire dataset is available on the web in browsable and searchable form or MySQL dump can be downloaded. So far, they have examined expression of 7507 genes and documented them with 111184 digital photographs.

Proper citation: Patterns of Gene Expression in Drosophila Embryogenesis (RRID:SCR_002868) Copy   

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

Database that provides access to biological systems and their component genes, proteins, and small molecules, as well as literature describing those biosystems and other related data throughout Entrez. A biosystem, or biological system, is a group of molecules that interact directly or indirectly, where the grouping is relevant to the characterization of living matter. BioSystem records list and categorize components, such as the genes, proteins, and small molecules involved in a biological system. The companion FLink tool, in turn, allows you to input a list of proteins, genes, or small molecules and retrieve a ranked list of biosystems. A number of databases provide diagrams showing the components and products of biological pathways along with corresponding annotations and links to literature. This database was developed as a complementary project to (1) serve as a centralized repository of data; (2) connect the biosystem records with associated literature, molecular, and chemical data throughout the Entrez system; and (3) facilitate computation on biosystems data. The NCBI BioSystems Database currently contains records from several source databases: KEGG, BioCyc (including its Tier 1 EcoCyc and MetaCyc databases, and its Tier 2 databases), Reactome, the National Cancer Institute's Pathway Interaction Database, WikiPathways, and Gene Ontology (GO). It includes several types of records such as pathways, structural complexes, and functional sets, and is desiged to accomodate other record types, such as diseases, as data become available. Through these collaborations, the BioSystems database facilitates access to, and provides the ability to compute on, a wide range of biosystems data. If you are interested in depositing data into the BioSystems database, please contact them.

Proper citation: NCBI BioSystems Database (RRID:SCR_004690) Copy   

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http://www.ebi.ac.uk/QuickGO/

A web-based browser for Gene Ontology terms and annotations, which is provided by the UniProtKB-GOA group at the EBI. It is able to offer a range of facilities including bulk downloads of GO annotation data which can be extensively filtered by a range of different parameters and GO slim set generation. The software for QuickGO is freely available under the Apache 2 license. QuickGO can supply GO term information and GO annotation data via REST web services.

Proper citation: QuickGO (RRID:SCR_004608) Copy   

•   Source: SciCrunch Registry

http://www.openphacts.org/

Project that developed an open access discovery platform, called Open Pharmacological Space (OPS), via a semantic web approach, integrating pharmacological data from a variety of information resources and tools and services to question this integrated data to support pharmacological research. The project is based upon the assimilation of data already stored as triples, in the form subject-predicate-object. The software and data are available for download and local installation, under an open source and open access model. Tools and services are provided to query and visualize this data, and a sustainability plan will be in place, continuing the operation of the Open PHACTS Discovery Platform after the project funding ends. Throughout the project, a series of recommendations will be developed in conjunction with the community, building on open standards, to ensure wide applicability of the approaches used for integration of data.

Proper citation: Open PHACTS (RRID:SCR_005050) Copy   

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http://kobas.cbi.pku.edu.cn/

Web server to identify statistically enriched pathways, diseases, and GO terms for a set of genes or proteins, using pathway, disease, and GO knowledge from multiple famous databases. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). A standalone command line version is also available, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: KOBAS (RRID:SCR_006350) Copy   

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http://www.informatics.jax.org/searches/GO_form.shtml

With the MGI GO Browser, you can search for a GO term and view all mouse genes annotated to the term or any subterms. You can also browse the ontologies to view relationships between terms, term definitions, as well as the number of mouse genes annotated to a given term and its subterms. The MGI GO browser directly accesses the GO data in the MGI database, which is updated nightly. Platform: Online tool

Proper citation: MGI GO Browser (RRID:SCR_006489) Copy   

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http://www.fda.gov/ScienceResearch/BioinformaticsTools/Arraytrack/default.htm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 23,2023. Software tool developed for ArrayTrack that takes a list of genes and identifies terms in Gene Ontology associated with those genes. GOFFA provides tools to view/access the following: GO term hierarchy, full listing of GO terms annotated with the genes associated with a given term, Fisher's exact test p-value providing the probability of identifying that many genes for a given term by chance alone, and relative enrichment factor (E-value) giving the enrichment of a GO term for genes in the submitted list relative to the frequency of genes assigned to that term from the full set of GOFFA annotated genes for a particular species.

Proper citation: Gene Ontology For Functional Analysis (GOFFA) (RRID:SCR_006484) Copy   

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http://www.informatics.jax.org/mgihome/GO/project.shtml

This resource is part of the Gene Ontology Consortium which seeks to provide controlled vocabularies for the description of the molecular function, biological process, and cellular component of gene products. These terms are to be used as attributes of gene products by collaborating databases, facilitating uniform queries across them. GO team members at MGI participate in ontology development, outreach, and functional curation of mouse gene products. The GO vocabularies have a hierarchical structure that permits a range of detail from high-level, broadly descriptive terms to very low level, highly specific terms. This broad range is useful both in annotating genes and in searching for gene information using these terms as search criteria. GO terms are defined, allowing all databases to use the terms consistently and properly. GO annotations in the databases additionally include the publication reference which allowed the association to be made and an evidence statement citing how the association was determined.

Proper citation: Mouse Genome Informatics: The Gene Ontology Project (RRID:SCR_006447) Copy   

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http://www.ebi.ac.uk/ontology-lookup/

Interactive and programmatic interfaces to query, browse and navigate an increasing number of biomedical ontologies and controlled vocabularies. It provides a web service interface to query multiple ontologies from a single location with a unified output format. It can integrate any ontology available in the Open Biomedical Ontology (OBO) format. The database can be queried to obtain information on a single term or to browse a complete ontology using AJAX. Auto-completion provides a user-friendly search mechanism. An AJAX-based ontology viewer is available to browse a complete ontology or subsets of it. A weekly MySQL database export file can be downloaded from the EBI public FTP directory.

Proper citation: Ontology Lookup Service (RRID:SCR_006596) Copy   

•   Source: SciCrunch Registry

https://wiki.nci.nih.gov/display/LexEVS/LexGrid

LexGrid (Lexical Grid) provides support for a distributed network of lexical resources such as terminologies and ontologies via standards-based tools, storage formats, and access/update mechanisms. The Lexical Grid Vision is for a distributed network of terminological resources. It is the foundation of the National Center for Biomedical Ontology BioPortal interface and web-services, and can parse OBO format, as well as other formats such as OWL. Currently, there are many terminologies and ontologies in existence. Just about every terminology has its own format, its own set of tools, and its own update mechanisms. The only thing that most of these pieces have in common with each other is their incompatibility. This makes it very hard to use these resources to their full potential. We have designed the Lexical Grid as a way to bridge terminologies and ontologies with a common set of tools, formats and update mechanisms. The Lexical Grid is: * accessible through a set of common APIs * joined through shared indices * online accessible * downloadable * loosely coupled * locally extendable * globally revised * available in web-space on web-time * cross-linked The realization of this vision requires three interlocking components, which are: * Standards - access methods and formats need to be published and openly available * Tools - standards based tools must be readily available * Content - commonly used terminologies have to be available for access and download Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: LexGrid (RRID:SCR_006627) Copy   

•   Source: SciCrunch Registry

http://www.innatedb.com

Publicly available database of the genes, proteins, experimentally-verified interactions and signaling pathways involved in the innate immune response of humans, mice and bovines to microbial infection. The database captures coverage of the innate immunity interactome by integrating known interactions and pathways from major public databases together with manually-curated data into a centralized resource. The database can be mined as a knowledgebase or used with the integrated bioinformatics and visualization tools for the systems level analysis of the innate immune response. Although InnateDB curation focuses on innate immunity-relevant interactions and pathways, it also incorporates detailed annotation on the entire human, mouse and bovine interactomes by integrating data (178,000+ interactions & 3,900+ pathways) from several of the major public interaction and pathway databases. InnateDB also has integrated human, mouse and bovine orthology predictions generated using Ortholgue software. Ortholgue uses a phylogenetic distance-based method to identify possible paralogs in high-throughput orthology predictions. Integrated human and mouse conserved gene order and synteny information has also been determined to provide further support for orthology predictions. InnateDB Capabilities: * View statistics for manually-curated innate immunity relevant molecular interactions. New manually curated interactions are submitted weekly. * Search for genes and proteins of interest. * Search for experimentally-verified molecular interactions by gene/protein name, interaction type, cell type, etc. * Search genes/interactions belonging to 3,900 pathways. * Visualize interactions using an intuitive subcellular localization-based layout in Cerebral. * Upload your own list of genes along with associated gene expression data (from up to 10 experimental conditions) to interactively analyze this data in a molecular interaction network context. Once you have uploaded your data, you will be able to interactively visualize interaction networks with expression data overlaid; carry out Pathway, Gene Ontology and Transcription Factor Binding Site over-representation analyses; construct orthologous interaction networks in other species; and much more. * Access curated interaction data via a dedicated PSICQUIC webservice.

Proper citation: InnateDB (RRID:SCR_006714) Copy   

•   Source: SciCrunch Registry