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http://www.uniprot.org/help/uniprotkb

Central repository for collection of functional information on proteins, with accurate and consistent annotation. In addition to capturing core data mandatory for each UniProtKB entry (mainly, the amino acid sequence, protein name or description, taxonomic data and citation information), as much annotation information as possible is added. This includes widely accepted biological ontologies, classifications and cross-references, and experimental and computational data. The UniProt Knowledgebase consists of two sections, UniProtKB/Swiss-Prot and UniProtKB/TrEMBL. UniProtKB/Swiss-Prot (reviewed) is a high quality manually annotated and non-redundant protein sequence database which brings together experimental results, computed features, and scientific conclusions. UniProtKB/TrEMBL (unreviewed) contains protein sequences associated with computationally generated annotation and large-scale functional characterization that await full manual annotation. Users may browse by taxonomy, keyword, gene ontology, enzyme class or pathway.

Proper citation: UniProtKB (RRID:SCR_004426) Copy   

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http://www.emouseatlas.org/emage

A database of in situ gene expression data in the developing mouse embryo and an accompanying suite of tools to search and analyze the data. mRNA in situ hybridization, protein immunohistochemistry and transgenic reporter data is included. The data held is spatially annotated to a framework of 3D mouse embryo models produced by EMAP (e-Mouse Atlas Project). These spatial annotations allow users to query EMAGE by spatial pattern as well as by gene name, anatomy term or Gene Ontology (GO) term. The conceptual framework which houses the descriptions of the gene expression patterns in EMAGE is the EMAP Mouse Embryo Anatomy Atlas. This consists of a set of 3D virtual embryos at different stages of development, as well as an accompanying ontology of anatomical terms found at each stage. The raw data images can be conventional 2D photographs (of sections or wholemount specimens) or 3D images of wholemount specimens derived from Optical Projection Tomography (OPT) or confocal microscopy. Users may submit data using a Data submission tool or without.

Proper citation: EMAGE Gene Expression Database (RRID:SCR_005391) Copy   

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http://coot.embl.de/g2d/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A database of candidate genes for mapped inherited human diseases. Candidate priorities are automatically established by a data mining algorithm that extracts putative genes in the chromosomal region where the disease is mapped, and evaluates their possible relation to the disease based on the phenotype of the disorder. Data analysis uses a scoring system developed for the possible functional relations of human genes to genetically inherited diseases that have been mapped onto chromosomal regions without assignment of a particular gene. Methodology can be divided in two parts: the association of genes to phenotypic features, and the identification of candidate genes on a chromosonal region by homology. This is an analysis of relations between phenotypic features and chemical objects, and from chemical objects to protein function terms, based on the whole MEDLINE and RefSeq databases.

Proper citation: Candidate Genes to Inherited Diseases (RRID:SCR_008190) Copy   

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http://genecodis.cnb.csic.es/

Web-based tool for the ontological analysis of large lists of genes. It can be used to determine biological annotations or combinations of annotations that are significantly associated to a list of genes under study with respect to a reference list. As well as single annotations, this tool allows users to simultaneously evaluate annotations from different sources, for example Biological Process and Cellular Component categories of Gene Ontology., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GeneCodis (RRID:SCR_006943) Copy   

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http://www.oeb.harvard.edu/faculty/hartl/old_site/lab/publications/GeneMerge.html

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. Web-based and standalone application that returns a wide range of functional genomic data for a given set of study genes and provides rank scores for over-representation of particular functions or categories in the data. It uses the hypergeometric test statistic which returns statistically correct results for samples of all sizes and is the #2 fastest GO tool available (Khatri and Draghici, 2005). GeneMerge can be used with any discrete, locus-based annotation data, including, literature references, genetic interactions, mutant phenotypes as well as traditional Gene Ontology queries. GeneMerge is particularly useful for the analysis of microarray data and other large biological datasets. The big advantage of GeneMerge over other similar programs is that you are not limited to analyzing your data from the perspective of a pre-packaged set of gene-association data. You can download or create gene-association files to analyze your data from an unlimited number of perspectives. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GeneMerge (RRID:SCR_005744) Copy   

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http://www.ucl.ac.uk/cardiovasculargeneontology/

Full Gene Ontology annotation to genes associated with cardiovascular processes. Every GO annotation made, is attributed to an identified source, such as a publication identifier (PMID), and an indication of the type of evidence which supports the association between the gene product and the GO term. Over 4,000 cardiovascular associated genes have been identified. A variety of tools have been provided to enable cardiovascular scientists to review the annotation of their ''''favorite'''' gene and suggest information that may be missing, inaccurate or incomplete in these annotations. Annotation suggestions can be sent through the feedback form or by email. The Gene Ontology (GO) vocabulary is the established standard for the functional annotation of gene products. By using GO to curate scientific literature and by integrating results from high-quality high-throughput experiments they will create an information-rich resource for the cardiovascular-research community, enabling researchers to rapidly evaluate and interpret existing data and generate hypotheses to guide future research.

Proper citation: Cardiovascular Gene Ontology Annotation Initiative (RRID:SCR_004795) Copy   

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http://www.geneontology.org/GO.refgenome.shtml

The GO Consortium coordinates an effort to maximize and optimize the GO annotation of a large and representative set of key genomes, known as ''reference genomes''. The goal of the Reference Genome Annotation project is to completely annotate twelve reference genomes so that those annotations may be used to effectively seed the automatic annotation efforts of other genomes. With more and more genomes being sequenced, we are in the middle of an explosion of genomic information. The limited resources to manually annotate the growing number of sequenced genomes imply that automatic annotation will be the method of choice for many groups. The Reference Genome project has two primary goals: to increase the depth and breadth of annotations for genes in each of the organisms in the project, and to create data sets and tools that enable other genome annotation efforts to infer GO annotations for homologous genes in their organisms. In addition, the project has several important incidental benefits, such as increasing annotation consistency across genome databases, and providing important improvements to the GO''s logical structure and biological content. All GO annotations from this project are included in the gene association files that each group submits to GO. Annotations can also be viewed using the GO search engine and browser AmiGO. Annotated families can be viewed with the homolog set browser.

Proper citation: RefGenome (RRID:SCR_004263) Copy   

•   Source: SciCrunch Registry

http://david.abcc.ncifcrf.gov/content.jsp?file=/ease/ease1.htm&type=1

Windows(c) desktop software application, customizable and standalone, that facilitates the biological interpretation of gene lists derived from the results of microarray, proteomic, and SAGE experiments. Provides statistical methods for discovering enriched biological themes within gene lists, generates gene annotation tables, and enables automated linking to online analysis tools. Offers statistical models to deal with multi-test comparison problem. Platform: Windows compatible

Proper citation: EASE: the Expression Analysis Systematic Explorer (RRID:SCR_013361) Copy   

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http://vortex.cs.wayne.edu/projects.htm#Onto-Design

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6,2023. Many Laboratories chose to design and print their own microarrays. At present, the choice of the genes to include on a certain microarray is a very laborious process requiring a high level of expertise. Onto-Design database is able to assist the designers of custom microarrays by providing the means to select genes based on their experiment. Design custom microarrays based on GO terms of interest. User account required. Platform: Online tool

Proper citation: Onto-Design (RRID:SCR_000601) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/release/bioc/html/topGO.html

Software package which provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied.

Proper citation: topGO (RRID:SCR_014798) Copy   

•   Source: SciCrunch Registry

http://llama.mshri.on.ca/funcassociate/

A web-based tool that accepts as input a list of genes, and returns a list of GO attributes that are over- (or under-) represented among the genes in the input list. Only those over- (or under-) representations that are statistically significant, after correcting for multiple hypotheses testing, are reported. Currently 37 organisms are supported. In addition to the input list of genes, users may specify a) whether this list should be regarded as ordered or unordered; b) the universe of genes to be considered by FuncAssociate; c) whether to report over-, or under-represented attributes, or both; and d) the p-value cutoff. A new version of FuncAssociate supports a wider range of naming schemes for input genes, and uses more frequently updated GO associations. However, some features of the original version, such as sorting by LOD or the option to see the gene-attribute table, are not yet implemented. Platform: Online tool

Proper citation: FuncAssociate: The Gene Set Functionator (RRID:SCR_005768) Copy   

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http://www.berkeleybop.org/goose/

A web utility providing a direct interface to perform SQL queries directly on the GO database, allowing users to run custom queries without having to install a copy of the GO database locally. GOOSE includes many sample queries to aid novice users and allows results to be retrieved as a web page or as tab-delimited text. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GO Online SQL Environment (GOOSE) (RRID:SCR_006174) Copy   

•   Source: SciCrunch Registry

http://metagp.ism.ac.jp/

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. Meta Gene Profiler (MetaGP) is a web application tool for discovering differentially expressed gene sets (meta genes) from the gene set library registered in our database. Once user submits gene expression profiles which are categorized into subtypes of conditioned experiments, or a list of genes with the valid pvalues, MetaGP assigns the integrated p-value to each gene set by combining the statistical evidences of genes that are obtained from gene-level analysis of significance. The current version supports the nine Affymetrix GeneChip arrays for the three organisms (human, mouse and rat). The significances of GO terms are graphically mapped onto the directed acyclic graph (DAG). The navigation systems of GO hierarchy enable us to summarize the significance of interesting sub-graphs on the web browser. Platform: Online tool

Proper citation: MetaGeneProfiler (RRID:SCR_005794) Copy   

•   Source: SciCrunch Registry

http://ccbb.jnu.ac.in/OntoVisT.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on February 07, 2013. Web based ontological visualization tool for interactive visualization of any ontological hierarchy for a specific node of interest, up to the chosen level of children and/or ancestor. It takes any ontology file in OBO format as input and generates output as DAG hierarchical graph for the chosen query. To enhance the navigation capabilities of complex networks, we have embedded several features such as search criteria, zoom in/out, center focus, nearest neighbor highlights and mouse hover events. The application has been tested on all 72 data sets available in OBO format through OBO foundry. The results for few of them can be accessed through OntoVisT-Gallery.

Proper citation: OntoVisT (RRID:SCR_005674) Copy   

•   Source: SciCrunch Registry

http://functionalgenomics.de/ontogate/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 13, 2012. OntoGate provides access to GenomeMatrix (GM) entries from Ontology terms and external datasets which have been associated with ontology terms, to find genes from different species in the GM, which have been mapped to the ontology terms. OntoGate includes a BLAST search of amino acid sequences corresponding to annotated genes. Platform: Online tool

Proper citation: OntoGate (RRID:SCR_005795) Copy   

•   Source: SciCrunch Registry

http://wego.genomics.org.cn/cgi-bin/wego/index.pl

Web Gene Ontology Annotation Plot (WEGO) is a simple but useful tool for plotting Gene Ontology (GO) annotation results. Different from other commercial software for chart creating, WEGO is designed to deal with the directed acyclic graph (DAG) structure of GO to facilitate histogram creation of GO annotation results. WEGO has been widely used in many important biological research projects, such as the rice genome project and the silkworm genome project. It has become one of the useful tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. Platform: Online tool

Proper citation: WEGO - Web Gene Ontology Annotation Plot (RRID:SCR_005827) Copy   

•   Source: SciCrunch Registry

http://revigo.irb.hr/

Web server that summarizes lists of Gene Ontology terms by removing redundant terms and visualizing the remaining ones in scatterplots, interactive graphs, treemaps, or tag clouds. Platform: Online tool, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: REViGO (RRID:SCR_005825) Copy   

•   Source: SciCrunch Registry

http://app.aporc.org/NOA/

Network 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 tool

Proper citation: Network Ontology Analysis (RRID:SCR_005667) Copy   

•   Source: SciCrunch Registry

http://snps-and-go.biocomp.unibo.it/snps-and-go/

A server for the prediction of single point protein mutations likely to be involved in the insurgence of diseases in humans.

Proper citation: SNPsandGO (RRID:SCR_005788) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.clemson.edu/G-SESAME/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 2,2025. G-SESAME contains a set of tools. They include: tools for measuring the semantic similarity of GO terms; tools for measuring the functional similarity of genes; and tools for clustering genes based on their GO term annotation information. Platform: Online tool, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: G-SESAME - Gene Semantic Similarity Analysis and Measurement Tools (RRID:SCR_005816) Copy   

•   Source: SciCrunch Registry