GO Tools: Other

AmiGO provides an interface to search and browse the ontology and annotation data provided by the GO consortium. Users can search for gene products and view the terms with which they are associated; alternatively, users can search or browse the ontology for GO terms of interest and see term details and gene product annotations. AmiGO also provides a BLAST search engine, which searches the sequences of genes and gene products that have been annotated to a GO term and submitted to the GO Consortium.

AmiGO accesses the GO mySQL database; more information is available from the GO database guide.

• ontology or annotation browser
• ontology or annotation search engine
• ontology or annotation visualization
• slimmer-type tool
• term enrichment
• other analysis

Bioconductor provides tools for the analysis and comprehension of high-throughput (microarray, sequence, flow, etc.) genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. There are more than core and user-contributed 400 packages. Bioconductor packages the GO ontology into our semi-annual release, with software tools to: query; join with diverse additional gene, microarray, and sequence annotations; incorporate GO into annotation, differential expression, and gene set enrichment work flows; and visualize.

• ontology or annotation browser
• ontology or annotation search engine
• ontology or annotation visualization
• database or data warehouse
• software library
• statistical analysis
• term enrichment
• text mining
• other analysis
• Flexible integration of GO into statistical analysis and comprehension of high-throughput genetic data.

Functional Semantic Similarity Measure between Gene Products (FuSSiMeG) provides a functional similarity measure between two proteins using the semantic similarity between the GO terms annotated with the proteins.

• statistical analysis
• term enrichment
• semantic similarity
• other analysis

The Functional Similarity Search Tool (FSST) has been implemented for comparing user defined sets of annotated entities. FSST supports the computation of functional similarity scores based on an individual ontology and of combined scores. Its multi-threaded Java implementation takes advantage of symmetric multi-processing computers, decreasing runtime considerably.

• other analysis
• functional similarity search

g:Profiler is a public web server for characterising and manipulating gene lists from high-throughput genomic data. g:Profiler has a simple user-friendly web interface with powerful visualisation. g:Profiler currently supports 85 species, including mammals, fungi, plants, insects, etc, from the Ensembl and Ensembl Genomes databases. g:Profiler consists of the following tools:

• g:GOSt retrieves most significant Gene Ontology (GO) terms, KEGG and REACTOME pathways, and TRANSFAC motifs to a user-specified group of genes, proteins or microarray probes. g:GOSt also allows analysis of ranked or ordered lists of genes, visual browsing of GO graph structure, interactive visualisation of retrieved results, and many other features. Multiple testing corrections are applied to extract only statistically important results.
• g:Convert allows conversion between gene or protein names, database IDs and microarray probes of more than 100 types. A mix of various IDs may be presented as input; output options include HTML, text and XLS spreadsheet.
• g:Orth retrieves orthologs for a given set of genes, proteins or probes in a selected organism. Graphical representation also shows orthologs present in all g:Profiler organisms.
• g:Sorter searches for similar expression profiles to a given gene, protein or probe in a large set of public microarray datasets from the Gene Expression Omnibus (GEO) database.
• g:Cocoa provides a compact interface for comparing enrichments of multiple gene lists.

• ontology or annotation visualization
• statistical analysis
• slimmer-type tool
• term enrichment
• protein interactions
• functional similarity
• other analysis
• coexpression similarity search, Gene ID conversion, network enrichment analysis., orthology mapping, search by genomic locus

GeneMANIA helps you predict the function of your favourite genes and gene sets.
GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input.
If members of your gene list make up a protein complex, GeneMANIA will return more potential members of the protein complex. If you enter a gene list, GeneMANIA will return connections between your genes, within the selected datasets.
GeneMANIA also performs Gene Ontology term enrichment of the query list along with the returned gene list.
GeneMANIA is also accessible via a Cytoscape plugin, designed for power users.
GeneMANIA is actively developed at the University of Toronto, in the Donnelly Centre for Cellular and Biomolecular Research, in the labs of Gary Bader and Quaid Morris, with input from an independent scientific advisory board. GeneMANIA development was funded by Genome Canada, through the Ontario Genomics Institute (2007-OGI-TD-05).

• database or data warehouse
• software library
• statistical analysis
• term enrichment
• other analysis
• an interaction browser and GeneMANIA suggests annotations for genes based on Gene Ontology term enrichment of highly interacting genes with the gene of interest., GeneMANIA is also a gene recommendation system

go-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.

• software library
• slimmer-type tool
• other analysis

GOTaxExplorer presents a new approach to comparative genomics that integrates functional information and families with the taxonomic classification. It integrates UniProt, Gene Ontology, NCBI Taxonomy, Pfam and SMART in one database. GOTaxExplorer provides four different query types: selection of entity sets, comparison of sets of Pfam families, semantic comparison of sets of GO terms, functional comparison of sets of gene products.
This permits to select custom sets of GO terms, families or taxonomic groups. For example, it is possible to compare arbitrarily selected organisms or groups of organisms from the taxonomic tree on the basis of the functionality of their genes. Furthermore, it enables to determine the distribution of specific molecular functions or protein families in the taxonomy. The comparison of sets of GO terms allows to assess the semantic similarity of two different GO terms. The functional comparison of gene products makes it possible to identify functionally equivalent and functionally related gene products from two organisms on the basis of GO annotations and a semantic similarity measure for GO.

• ontology or annotation search engine
• ontology or annotation visualization
• database or data warehouse
• functional similarity
• semantic similarity
• other analysis
• comparative genomics analysis

Microarrays 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.

• 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 GO

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.

• ontology or annotation browser
• ontology or annotation search engine
• ontology or annotation visualization
• database or data warehouse
• other analysis
• design custom microarrays based on GO terms of interest

The 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 et.al, Genomics, 81(2), 2003). Other results obtained with Onto-Express can be found in Khatri et.al., Genomics. 79(2), 2002.

• ontology or annotation browser
• ontology or annotation search engine
• ontology or annotation visualization
• database or data warehouse
• statistical analysis
• term enrichment
• other analysis
• custom level of abstraction of the Gene Ontology

Onto-Express is a web-based tool in the Onto-Tools suite that performs automated function profiling for a list of differentially expressed genes. However, Onto-Express does not support functional profiling for the organisms that do not have annotations in public domain, or use of custom (i.e. user-defined) ontologies. This limitation is also true for most of the other existing tools for functional profiling (10), which means that researchers working with uncommon organisms and/or new annotations or ontologies may be forced to construct such profiles manually. Onto-Express To Go (OE2GO) is a new tool added to the Onto-Tools ensemble to address these issues. OE2GO is built on top of OE to leverage its existing functionality. In OE2GO, the users now have an option to use either the Onto-Tools database as a source of functional annotations or provide their own annotations in a separate file. Currently, OE2GO supports annotation file in the Gene Ontology format.

• ontology or annotation browser
• ontology or annotation search engine
• ontology or annotation visualization
• statistical analysis
• term enrichment
• other analysis
• custom annotations, custom level of abstraction

Onto-Miner (OM) provide a single and convenient interface that allow the user to interrogate our databases regarding annotations of known genes. OM will return all known information about a given list of genes. Advantages or OM include the fact it allows queries with multiple genes and allows for scripting. This is unlike GenBank which uses a single gene navigation process.

• ontology or annotation search engine
• database or data warehouse
• other analysis
• scripted search of the Onto-Tools database for gene annotations

In the annotation world, the same piece of information can be stored and viewed differently across different databases. For instance, more than one Affymetrix probe ID can refer to the same GenBank sequence (accession number) and more than one nucleotide sequence from GenBank can be grouped in a single UniGene cluster. The result of Onto-Express depends on whether the input list contains Affymetrix probe IDs, GenBank accession numbers or UniGene cluster IDs. The user has to be aware of relations between the different forms of the data in order to interpret correctly the results. Even if the user is aware of the relationships and knows how to convert them, most existing tools allow conversions of individual genes. Onto-Translate is a tool that allows the user to perform easily such translations.

• database or data warehouse
• other analysis
• Affymetrix probe IDs, etc., translate GO terms into other identifiers like GenBank accession number, Uniprot IDs

ProteInOn calculates semantic similarity between GO terms or proteins annotated with GO terms. It also calculates term enrichment of protein sets, by applying a term representativity score, and gives additional information on protein interactions.

• statistical analysis
• term enrichment
• protein interactions
• semantic similarity
• other analysis

RamiGO is a Bioconductor.org software package available for the statistical programming language R and provides an interface for sending requests to the AmiGO visualize web server, retrieving DAG GO trees, parsing GraphViz DOT format files, exporting GML files for Cytoscape and also for interactively displaying GO trees, received from AmiGO visualize, in Cytoscape without importing or exporting them.
RamiGO utilizes the visualization of GO trees from AmiGO and makes it available from R.

• ontology or annotation search engine
• ontology or annotation visualization
• other analysis

ToppGene Suite is a one-stop portal for (i) gene list functional enrichment, (ii) candidate gene prioritization using either functional annotations or network analysis and (iii) identification and prioritization of novel disease candidate genes in the interactome. Functional annotation-based disease candidate gene prioritization uses a fuzzy-based similarity measure to compute the similarity between any two genes based on semantic annotations. The similarity scores from individual features are combined into an overall score using statistical meta-analysis. A P-value of each annotation of a test gene is derived by random sampling of the whole genome.

• database or data warehouse
• slimmer-type tool
• term enrichment
• protein interactions
• functional similarity
• other analysis
• disease gene ranking based on functional annotation similarity

Agile Protein Interaction Data Analyzer (APID) is an interactive bioinformatics web tool developed to integrate and analyze in a unified and comparative platform main currently known information about protein-protein interactions demonstrated by specific small-scale or large-scale experimental methods. At present, the application includes information coming from five main source databases enclosing an unified sever to explore more than 40000 different proteins and close to 150000 different proven interactions. The website includes search tools to query and browse upon the data, allowing selection of the interaction pairs based in calculated parameters that weight and qualify the reliability of each given protein interaction. Parameters for the proteins are connectivity, cluster coefficient, Gene Ontology (GO) functional environment, GO environment enrichment; for the interactions: number of methods, GO overlapping, Pfam domain-domain interaction. APID also includes a graphic interactive tool to visualize selected sub-networks and to navigate on them or along the whole interaction network.

• other analysis
• protein-protein interaction analysis

The EP:GO browser is built into EBI's Expression Profiler, a set of tools for clustering, analysis and visualization of gene expression and other genomic data. With it, you can search for GO terms and identify gene associations for a node, with or without associated subnodes, for the organism of your choice.

• other analysis

GOChase is a set of web-based utilities to detect and correct the errors in GO-based annotations.

• GOChase-History resolves the whole modification history of GO IDs.
• GOChase-Correct highlights merged GO IDs and redirects to the correct primary term into which the secondary ID was merged. For obsolete GO terms, the nearest non-discarded parent term is recommended by GOChase. This function may be used by GO browsers such as AmiGO and QuickGO to fix broken hyperlinks.
• A whole database (such as LocusLink) as a flat file can be loaded into GOChase, reporting the annotation errors and GOChase corrections.
• When one inputs a GO ID, GOChase will resolve all gene products annotated with the GO ID across all the major databases.

• other analysis
• historical views of GO

GoPubMed is a web server which allows users to explore PubMed search results with the Gene Ontology. GoPubMed submits a user's keywords to PubMed, retrieves the abstracts, detects Gene Ontology terms in the abstracts, displays the subset of Gene Ontology relevant to the original query, and allows the user to browse through the ontology displaying associated papers and their GO annotation.

• other analysis
• literature curation and exploration

Whatizit is a web application which highlights all kinds of "interesting" things in a document. One of its modules marks all GO terms as well as protein and gene names from UniProt and links them to their entries in GO and UniProt respectively.

• other analysis
• textual analysis