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http://cbl-gorilla.cs.technion.ac.il/

A tool for identifying and visualizing enriched GO terms in ranked lists of genes. It can be run in one of two modes: * Searching for enriched GO terms that appear densely at the top of a ranked list of genes or * Searching for enriched GO terms in a target list of genes compared to a background list of genes.

Proper citation: GOrilla: Gene Ontology Enrichment Analysis and Visualization Tool (RRID:SCR_006848) Copy   

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http://gostat.wehi.edu.au

GOstat is a tool that allows you to find statistically overrepresented Gene Ontologies within a group of genes. The Gene-Ontology database (GO: http://www.geneontology.org) provides a useful tool to annotate and analyze the function of large numbers of genes. Modern experimental techniques, as e.g. DNA microarrays, often result in long lists of genes. To learn about the biology in this kind of data it is desirable to find functional annotation or Gene-Ontology groups which are highly represented in the data. This program (GOstat) should help in the analysis of such lists and will provide statistics about the GO terms contained in the data and sort the GO annotations giving the most representative GO terms first. Run GOstat: * Go to search form - Computes GO statistics of a list of genes selected from a microarray. * GOstat Display - You can store results from a previously run and view them here, either by uploading them as a file or putting them on a selected URL. * Upload Custom GO Annotations - This allows you to upload your own GO annotation database and use it with GOstat. Variants of GOstat: * Rank GOstat - Takes input from all genes on microarray instead of using a fixed cutoff and uses ranks using a Wilcoxon test or either ranks or pvalues to score GOs using Kolmogorov-Smirnov statistics. * Gene Abundance GOstats - Takes input from all genes on microarray and sums up the gene abundances for each GO to compute statistics. * Two list GOstat - Compares GO statistics in two independent lists of genes, not necessarily one of them being the complete list the other list is sampled from. Platform: Online tool, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GOstat (RRID:SCR_008535) Copy   

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http://gsgator.ewha.ac.kr/

A web-based platform for functional interpretation of gene sets with features such as cross-species Gene Set Analysis (GSA), Flexible and Interactive GSA, simultaneous GSA for multiple gene set, and and a fully integrated network viewer for both visualizing GSA results and molecular networks.

Proper citation: gsGator (RRID:SCR_012035) Copy   

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http://gowiki.tamu.edu/wiki/

A wiki where users of the Gene Ontology can contribute and view notes about how specific GO terms are used. GONUTS can also be used as a GO term browser, or to search for GO annotations of specific genes from included organisms. The rationale for this wiki is based on helping new users of the gene ontology understand and use it. The GONUTS wiki is not an official product of the the Gene Ontology consortium. The GO consortium has a public wiki at their website, http://wiki.geneontology.org/. Maintaining the ontology involves many decisions to carefully choose terms and relationships. These decisions are currently made at GO meetings and via online discussion using the GO mailing lists and the Sourceforge curator request tracker. However, it is difficult for someone starting to use GO to understand these decisions. Some insight can be obtained by mining the tracker, the listservs and the minutes of GO meetings, but this is difficult, as these discussions are often dispersed and sometimes don't contain the GO accessions in the relevant messages. Wikis provide a way to create collaboratively written documentation for each GO term to explain how it should be used, how to satisfy the true path requirement, and whether an annotation should be placed at a different level. In addition, the wiki pages provide a discussion space, where users can post questions and discuss possible changes to the ontology. GONUTS is currently set up so anyone can view or search, but only registered users can edit or add pages. Currently registered users can create new users, and we are working to add at least one registered user for each participating database (So far we have registered users at EcoliHub, EcoCyc, GOA, BeeBase, SGD, dictyBase, FlyBase, WormBase, TAIR, Rat Genome Database, ZFIN, MGI, UCL and AgBase...

Proper citation: GONUTS (RRID:SCR_000653) Copy   

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http://scicrunch.org/Aging

Portal devoted to aging relevant scientific data and resources.

Proper citation: Aging Portal (RRID:SCR_000496) Copy   

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https://factory.euromov.eu/sml/index.php

Open source Java library dedicated to semantic measures computation and analysis. Tools based on the SML are also provided through the SML-Toolkit, a command line software giving access to some of the functionalities of the library. The SML and the toolkit can be used to compute semantic similarity and semantic relatedness between semantic elements (e.g. concepts, terms) or entities semantically characterized (e.g. entities defined in a semantic graph, documents annotated by concepts defined in an ontology).

Proper citation: Semantic Measures Library (RRID:SCR_001383) 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.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://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://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://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://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   

•   Source: SciCrunch Registry

http://services.nbic.nl/copub/portal/

Text mining tool that detects co-occuring biomedical concepts in abstracts from the MedLine literature database. It allows batch input of multiple human, mouse or rat genes and produces lists of keywords from several biomedical thesauri that are significantly correlated with the set of input genes. These lists link to Medline abstracts in which the co-occurring input genes and correlated keywords are highlighted. Furthermore, CoPub can graphically visualize differentially expressed genes and over-represented keywords in a network, providing detailed insight in the relationships between genes and keywords, and revealing the most influential genes as highly connected hubs.

Proper citation: CoPub (RRID:SCR_005327) Copy   

•   Source: SciCrunch Registry

http://www.garban.org/garban/home.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 12, 2012. GARBAN is a tool for analysis and rapid functional annotation of data arising from cDNA microarrays and proteomics techniques. GARBAN has been implemented with bioinformatic tools to rapidly compare, classify, and graphically represent multiple sets of data (genes/ESTs, or proteins), with the specific aim of facilitating the identification of molecular markers in pathological and pharmacological studies. GARBAN has links to the major genomic and proteomic databases (Ensembl, GeneBank, UniProt Knowledgebase, InterPro, etc.), and follows the criteria of the Gene Ontology Consortium (GO) for ontological classifications. Source may be shared: e-mail garban (at) ceit.es. Platform: Online tool

Proper citation: GARBAN (RRID:SCR_005778) Copy   

•   Source: SciCrunch Registry

http://corneliu.henegar.info/FunCluster.htm

FunCluster is a genomic data analysis algorithm which performs functional analysis of gene expression data obtained from cDNA microarray experiments. Besides automated functional annotation of gene expression data, FunCluster functional analysis aims to detect co-regulated biological processes through a specially designed clustering procedure involving biological annotations and gene expression data. FunCluster''''s functional analysis relies on Gene Ontology and KEGG annotations and is currently available for three organisms: Homo Sapiens, Mus Musculus and Saccharomyces Cerevisiae. FunCluster is provided as a standalone R package, which can be run on any operating system for which an R environment implementation is available (Windows, Mac OS, various flavors of Linux and Unix). Download it from the FunCluster website, or from the worldwide mirrors of CRAN. FunCluster is provided freely under the GNU General Public License 2.0. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FunCluster (RRID:SCR_005774) Copy   

•   Source: SciCrunch Registry

http://great.stanford.edu/public/html/splash.php

Data analysis service that predicts functions of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. The utility of GREAT extends to data generated for transcription-associated factors, open chromatin, localized epigenomic markers and similar functional data sets, and comparative genomics sets. Platform: Online tool

Proper citation: GREAT: Genomic Regions Enrichment of Annotations Tool (RRID:SCR_005807) Copy   

•   Source: SciCrunch Registry

http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual#GOHyperGAll

To test a sample population of genes for overrepresentation of GO terms, the R/BioC function GOHyperGAll computes for all GO nodes a hypergeometric distribution test and returns the corresponding p-values. A subsequent filter function performs a GO Slim analysis using default or custom GO Slim categories. Basic knowledge about R and BioConductor is required for using this tool. 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: GOHyperGAll (RRID:SCR_005766) Copy   

•   Source: SciCrunch Registry