Gene Ontology Tools

Gene Ontology (GO), the de facto standard in gene functionality description, is used widely in functional annotation and enrichment analysis. Here, we introduce agriGO, an integrated web-based GO analysis toolkit for the agricultural community, using the advantages of our previous GO enrichment tool (EasyGO), to meet analysis demands from new technologies and research objectives. EasyGO is valuable for its proficiency, and has proved useful in uncovering biological knowledge in massive data sets from high-throughput experiments. For agriGO, the system architecture and website interface were redesigned to improve performance and accessibility. The supported organisms and gene identifiers were substantially expanded (including 38 agricultural species composed of 274 data types). The requirement on user input is more flexible, in that user-defined reference and annotation are accepted. Moreover, a new analysis approach using Gene Set Enrichment Analysis strategy and customizable features is provided. Four tools, SEA (Singular enrichment analysis), PAGE (Parametric Analysis of Gene set Enrichment), BLAST4ID (Transfer IDs by BLAST) and SEACOMPARE (Cross comparison of SEA), are integrated as a toolkit to meet different demands. We also provide a cross-comparison service so that different data sets can be compared and explored in a visualized way. Lastly, agriGO functions as a GO data repository with search and download functions; agriGO is publicly accessible at http://bioinfo.cau.edu.cn/agriGO/.

• ontology or annotation browser
• ontology or annotation visualization
• database or data warehouse
• statistical analysis
• term enrichment
• text mining

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
• term enrichment
• other analysis not mentioned above

CateGOrizer takes batch input of GO term IDs in a list format or unformatted plain text file, allows users to choose one of the available classifications such as GO_slim, GOA, EGAD, MGI_GO_slim, GO-ROOT, or a self-defined classification list, find its parental "branch" and performs an accumulative classification count, and returns the results in a sorted table of counts, percentages, and a pie chart (if it takes longer than standard "time out" period, it will email the user with a URL link to the results).

• statistical analysis
• GO Terms classification (by predefined parental GO terms such as GO Slim).

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
• other analysis not mentioned above
• semantic similarity measure

The Functional Similarity Search Tool 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 not mentioned above
• functional similarity comparison

FunSimMat is a comprehensive resource of semantic and functional similarity values. It allows ranking disease candidate proteins 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.

• database or data warehouse
• disease gene candidate prioritization, functional similarity search, semantic similarity search

GOFFA is accessed through ArrayTrack, and allows users to input a list of genes and find significant associated gene ontologies. Users can also browse for gene ontology terms in a hierarchical structure, search based on name, graphically view the most significant GO paths, and graphically view a simple tree structure showing the most significant GO terms (GO Tree Prune).

• ontology or annotation browser
• ontology or annotation search engine
• ontology or annotation visualization
• database or data warehouse
• statistical analysis
• term enrichment
• integrated with and can be directly accessed from data stored in ArrayTrack

This finds the significant GO terms shared among a list of genes from your organism of choice, displaying the results in a table and as a graph (showing the terms and their ancestry). The user may optionally provide background information or a custom gene association file. This tool is capable of batch processing multiple queries at once.

• ontology or annotation visualization
• term enrichment

This is a tool for mapping the granular GO annotations for genes in a list to a set of broader, high-level GO parents terms (sometimes referred to as GO Slim terms), allowing you to bin your genes into broad categories.

• term enrichment

GOOSE, the GO Online SQL Environment, provides a direct interface to the GO database, allowing users to run custom queries without having to install a copy of the GO database locally.

• database or data warehouse

GOanna is used to find annotations for proteins using a similarity search. The input can be a list of IDs or it can be a list of sequences in FASTA format. GOanna will retrieve the sequences if necessary and conduct the specified BLAST search against a user-specified database of GO annotated proteins. The resulting file contains GO annotations of the top BLAST hits. The sequence alignments are also provided so the user can use these to access the quality of the match.

• ontology or annotation search engine
• ontology or annotation editor

GOAnnotator is a tool for assisting the GO annotation of UniProt entries by linking the GO terms present in the uncurated annotations with evidence text automatically extracted from the documents linked to UniProt entries.

• text mining

GORetriever 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.

• ontology or annotation search engine

GOrilla is a web-based application that identifies enriched GO terms in ranked lists of genes, without requiring the user to provide explicit target and background sets. These are determined in a data driven manner. GOrilla employs a flexible threshold statistical approach to discover GO terms that are significantly enriched at the top of a ranked gene list. The tool supports several input formats: gene symbol, gene and protein RefSeq, Uniprot, Unigene and Ensembl. Supported organisms include: human, mouse, rat, yeast, zebrafish, D. melanogaster, C. elegans and A. thaliana. The input to GOrilla is either a ranked gene list or target and background sets. The graphical output shows the results in the context of the GO DAG.

• ontology or annotation visualization
• statistical analysis
• term enrichment

GOSlimViewer is used to summarize the GO function associated with a data set using prepared GO Slim sets. The input is a tab separated list of gene product IDs and GO IDs.

• ontology or annotation browser

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
• other analysis not mentioned above
• comparative genomics analysis, functional similarity comparison, semantic similarity comparison

We previously developed GoMiner, an application that organizes lists of 'interesting' genes (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. The original version of GoMiner was oriented toward visualization and interpretation of the results from a single microarray (or other high-throughput experimental platform), using a graphical user interface. Although that version can be used to examine the results from a number of microarrays one at a time, that is a rather tedious task, and original GoMiner includes no apparatus for obtaining a global picture of results from an experiment that consists of multiple microarrays. We wanted to provide a computational resource that automates the analysis of multiple microarrays and then integrates the results across all of them in useful exportable output files and visualizations.
High-Throughput GoMiner, has those capabilities and a number of others: It (i) efficiently performs the computationally-intensive task of automated batch processing of an arbitrary number of microarrays, (ii) produces a human- or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories, (iii) integrates the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories, (iv) provides a fast form of 'false discovery rate' multiple comparisons calculation, and (v) provides annotations and visualizations for relating transcription factor binding sites to genes and GO categories.
High-Throughput GoMiner achieves the desired goal of providing a computational resource that automates the analysis of multiple microarrays and integrates results across all of the microarrays. High-Throughput GoMiner will be useful in a wide range of applications, including the study of time-courses, evaluation of multiple drug treatments, comparison of multiple gene knock-outs or knock-downs, and screening of large numbers of chemical derivatives generated from a promising lead compound.

• term enrichment

OBO-Edit is an open source, platform-independent application 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.

OBO-Edit uses the OBO format flat file.

• ontology or annotation browser
• ontology or annotation search engine
• ontology or annotation editor

Onto-Express (OE) 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 annota- tions 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 not mentioned above
• custom annotations, custom level of abstraction

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 not mentioned above
• 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 not mentioned above
• 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 not mentioned above
• custom level of abstraction of the Gene Ontology

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 not mentioned above
• scripted search of the Onto-Tools database for gene annotations

ONTO-PERL is a collection of Perl modules to handle OBO-formatted ontologies (like the Gene Ontology). This code distribution gathers object-oriented modules (for dealing with ontology elements such as Term, Relationship and so forth), scripts (for typical tasks such as format conversions: obo2owl, owl2obo; besides, there are also many examples that can be easily adapted for specific applications), and a set of test files to ensure the suite's implementation quality.

• software library

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 not mentioned above
• Affymetrix probe IDs, Uniprot IDs, etc., translate GO terms into other identifiers like GenBank accession number

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
• other analysis not mentioned above
• protein interactions, semantic similarity

QuickGO is a web-based tool that allows easy browsing of the Gene Ontology (GO) and all associated electronic and manual GO annotations provided by the GO Consortium annotation groups. QuickGO offers 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.

• ontology or annotation browser
• ontology or annotation search engine
• ontology or annotation visualization
• database or data warehouse
• GO slim

REViGO summarizes lists of Gene Ontology terms by removing redundant terms and visualizing the remaining ones in scatterplots, interactive graphs, treemaps, or tag clouds

• statistical analysis