GO Tools: Statistical Analysis

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

The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy, and takes advantage of Cytoscape's versatile visualization environment to produce an intuitive and customizable visual representation of the results.

• statistical analysis
• term enrichment

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.

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
• slimmer-type tool
• GO Terms classification (by predefined parental GO terms such as GO Slim).

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

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

Gene Ontology Browsing Utility (GOBU) (GOBU) is a Java-based software program for integrating biological annotation catalogs under an extendable software architecture. Users may interact with the Gene Ontology and user-defined hierarchy data of genes, and then use its plugins to (and not limited to) (1) browse the GO hierarchy with user defined data, (2) browse GO-oriented expression levels in the user data, (3) compute GO enrichment, and/or (4) customize data reporting.
A set of classes and utility functions has been established so that a customized program can be made as a plugin or a command-line tool that programmically manipulate the Gene Ontology and specified user data. See the source code repository for examples.

• ontology or annotation browser
• ontology or annotation visualization
• software library
• statistical analysis
• term enrichment

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

GeneMerge is a 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. GeneMerge 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.

• statistical analysis
• slimmer-type tool
• term enrichment
• text mining
• false discovery rate and Bonferroni correction

GeneTools is a collection of web-based tools that brings together information from a broad range of resources, and provides this in a manner particularly useful for genome-wide analyses. Today, the two main tools connected to this database are the NMC Annotation Database V2.0 and eGOn V2.0 (explore Gene Ontology).

The NMC Annotation Database V2.0 provides information from UniGene, EntrezGene, SwissProt and Gene Ontology (GO). Major features are:

• Single search/Batch search, extraction of data for single or batches of genes.
• Manage reporter lists: in folders and share selected lists with other users.
• Manual GO Annotation: add your own Gene Ontology (GO) annotations to genes of interest.
• Export: to Excel, text or XML format.

eGOn V2.0 facilitates interpretation of GO annotation. GO terms are retrieved in batch modus from EntrezGene and the GO database and displayed in the GO directed acyclic hierarchical graph (DAG). Essential features of eGOn V2.0 are:

• Visualization: gene annotations are visualized in the GO DAG or as a table view. The granularity of the GO DAG can be edited freely by the user.
• Filtering: GO annotations can be filtered on evidence codes.
• Include user defined GO annotations: previously added to the Annotation database.
• Statistical analysis: Several gene lists are analyzed simultaneously to compare the distribution of the annotated genes over the GO hierarchy.

Statistical tests are implemented to allow the user to compute GO annotation dissimilarity within or between gene lists.

• Connection to Annotation database: Links to Annotation database gene and protein information are offered directly from the GO DAG or in exported data.
• Export: GO DAG information, statistical results and gene and protein information can be exported in excel, text or XML format.

• ontology or annotation browser
• ontology or annotation search engine
• ontology or annotation visualization
• database or data warehouse
• statistical analysis
• term enrichment

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

GraphWeb allows the detection of modules from biological, heterogeneous and multi-species networks, and the interpretation of detected modules using Gene Ontology, cis-regulatory motifs and biological pathways.

• ontology or annotation visualization
• statistical analysis
• term enrichment
• protein interactions
• Gene ID conversion; orthology mapping; network visualisation; graph clustering

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.

• ontology or annotation browser
• statistical analysis
• term enrichment

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

PiNGO is a Java-based tool to easily find unknown genes in a network that are significantly associated with user-defined target Gene Ontology (GO) categories. PiNGO is implemented as a plugin for Cytoscape, a popular open source software platform for visualizing and integrating molecular interaction networks. PiNGO predicts the categorization of a gene based on the annotations of its neighbors, using the enrichment statistics of its sister tool BiNGO. Networks can either be selected from the Cytoscape interface or uploaded from file.

• ontology or annotation search engine
• statistical analysis
• term enrichment
• functional similarity
• functional prediction

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

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

• ontology or annotation visualization
• statistical analysis

The Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data (eight time points or less). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database and supports GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category, identifying which temporal expression profiles were enriched for these genes.

• statistical analysis
• term enrichment

StRAnGER (Statistical Ranking of ANotated Genomic Experimental Results) is a web application for the automated statistical analysis of annotated gene profiling experiments, exploiting controlled biological vocabularies, like the Gene Ontology or the KEGG pathways terms. Starting from annotated lists of differentially expressed genes StRAnGER repartitions and reorders the initial distribution of terms to define a new distribution of elements where each element pools terms holding the same enrichment score. The elements are then prioritized according to StRAnGER's algorithm and, by applying bootstrapping techniques, a corrected measure of the statistical significance of these elements is derived, enabling the selection of terms mapped to these elements, unambiguously associated with respective significant gene sets. Besides their high statistical score, another selection criterion for the terms is the number of their members, something that incurs a biological prioritization in line with a Systems Biology context.

• ontology or annotation visualization
• statistical analysis
• term enrichment

Avadis is a data analysis and visualization tool for gene expression data. Avadis has a built-in Gene Ontology browser to view ontology hierarchies. There are common ontology paths for multiple genes. Genes can be clustered based on ontology terms to identify functional signatures in gene expression clusters.

• statistical analysis

Blast2GO (B2G) joins in one universal application similarity search based GO annotation and functional analysis. B2G offers the possibility of direct statistical analysis on gene function information and visualization of relevant functional features on a highlighted GO direct acyclic graph (DAG). Furthermore B2G includes various statistics charts summarizing the results obtained at BLASTing, GO-mapping, annotation and enrichment analysis (Fisher's Exact Test). All analysis process steps are configurable and data import and export are supported at any stage. The application also accepts pre-existing BLAST or annotation files and takes them to subsequent steps. The tool offers a very suitable platform for high throughput functional genomics research in non-model species. B2G is a species-independent, intuitive and interactive desktop application which allows monitoring and comprehending the whole annotation and analysis process supported by additional features like GO Slim integration, evidence code (EC) consideration, a Batch-Mode or GO-Multilevel-Pies.

• ontology or annotation editor
• statistical analysis
• slimmer-type tool

ClueGO is 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.

• statistical analysis

Cluster Assignment for Biological Inference (CLASSIFI) is a data-mining tool that can be used to identify significant co-clustering of genes with similar functional properties (e.g. cellular response to DNA damage). Briefly, CLASSIFI uses the Gene Ontology gene annotation scheme to define the functional properties of all genes/probes in a microarray data set, and then applies a cumulative hypergeometric distribution analysis to determine if any statistically significant gene ontology co-clustering has occurred.

• statistical analysis

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.

• statistical analysis
• slimmer-type tool

EASE is useful for summarizing the predominant biological "theme" of a given gene list. Given a list of genes resulting from a microarray or other genome-scale experiment, EASE can rapidly calculate over-representation statistics for every possible Gene Ontology term with respect to all genes represented in the data set.

• statistical analysis

ermineJ is a tool for the analysis of gene sets (user defined or those defined by GO terms) in expression data. The software is designed to be used by biologists with little or no informatics background. A command-line interface is available for users who wish to script the use of ermineJ. Several different methods for scoring gene sets are implemented, with a focus on methods that don't rely on simple "over-representation" measures.

• statistical analysis

Exploratory Gene Association Networks (EGAN) is a software tool that allows a bench biologist to visualize and interpret the results of high-throughput exploratory assays in an interactive hypergraph of genes, relationships (protein-protein interactions, literature co-occurrence, etc.) and meta-data (annotation, signaling pathways, etc.). EGAN provides comprehensive, automated calculation of meta-data coincidence (over-representation, enrichment) for user- and assay-defined gene lists, and provides direct links to web resources and literature (NCBI Entrez Gene, PubMed, KEGG, Gene Ontology, iHOP, Google, etc.).

• statistical analysis

FatiGO assigns representative functional information (under-represented or over-represented Gene Ontology terms) to a given set of genes. Statistical significance is obtained using multiple-testing correction. FatiGO has been designed for functional annotation in the context of DNA microarray data analysis, and is linked to the Gene Expression Pattern Analysis Suite. FatiGO uses gene IDs from the major genomic and proteomic databases (GeneBank, UniProt, Unigene, Ensembl, etc.). FatiGO can also be used for functional annotation of any type of large-scale experiment.

• statistical analysis

FuncAssociate is 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 10 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 (still at the beta stage!) is now available. This version 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.

• statistical analysis

FuncExpression is a web-based resource for functional interpretation of large scale genomics data. FuncExpression can be used for the functional comparison of plant, animal, and fungal gene name lists generated from genomics and proteomics experiments. Multiple gene lists can be classified, compared and visualized. FuncExpression supports two way-integration of plant gene functional information and the gene expression data, which allows for further cross-validation with plant microarray data from related experiments at BarleyBase.

• statistical analysis

FunCluster is a genomic data analysis tool designed to perform a functional analysis of gene expression data obtained from cDNA microarray experiments. Besides automated functional annotation of gene expression data, FunCluster functional analysis allows to detect co-regulated biological processes (i.e. represented by annotating genomic themes) through a specifically designed co-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.

• statistical analysis

Functional Analysis of Transcriptional Networks (FunNet) is designed as an integrative tool for analyzing gene co-expression networks built from microarray expression data. The analytical model implemented in this tool involves two abstraction layers: transcriptional (i.e. gene expression profiles) and functional (i.e. biological themes indicating the roles of the analyzed transcripts). A functional analysis technique, which relies on Gene Ontology and KEGG annotations, is applied to extract a list of relevant biological themes from microarray gene expression data. Afterwards multiple-instance representations are built to relate relevant biological themes to their annotated transcripts. An original non-linear dynamical model is used to quantify the contextual proximity of relevant genomic themes based on their patterns of propagation in the gene co-expression network (i.e. capturing the similarity of the expression profiles of the transcriptional instances of annotating themes). In the end an unsupervised multiple-instance spectral clustering procedure is used to explore the modular architecture of the co-expression network by grouping together biological themes demonstrating a significant relationship in the co-expression network. Functional and transcriptional representations of the co-expression network are provided, together with detailed information on the contextual centrality of related transcripts and genomic themes.
FunNet is provided both as a web-based tool and as a standalone R package. The standalone R implementation can be run on any operating system for which an R environment implementation is available (Windows, Mac OS, various flavors of Linux and Unix) and can be downloaded from the FunNet website, or from the worldwide mirrors of CRAN. Both implementations of the FunNet tool are provided freely under the GNU General Public License 2.0.

• statistical analysis

Functional Information Viewer and Analyzer (FIVA) aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software is able to assist in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes.

• statistical analysis

FunSpec is a web-based tool for statistical evaluation of groups of genes and proteins (e.g. co-regulated genes, protein complexes, genetic interactors) with respect to existing annotations, including GO terms.

• statistical analysis

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@ceit.es.

• statistical analysis

Gene Ontology Enrichment Analysis Software Toolkit (GOEAST) is a web based software toolkit providing easy to use, visualizable, comprehensive and unbiased Gene Ontology (GO) analysis for high-throughput experimental results, especially for results from microarray hybridization experiments. The main function of GOEAST is to identify significantly enriched GO terms among give lists of genes using accurate statistical methods.

• statistical analysis

Gene Ontology Explorer (GOEx) combines data from protein fold changes with GO over-representation statistics to help draw conclusions in proteomic experiments. It is tightly integrated within the PatternLab for Proteomics project and, thus, lies within a complete computational environment that provides parsers and pattern recognition tools designed for spectral counting. GOEx offers three independent methods to query data: an interactive directed acyclic graph, a specialist mode where key words can be searched, and an automatic search. A details description of these methods is provided in the publication.

• statistical analysis

Gene Ontology for Motifs (GOMO) is an alignment- and threshold-free comparative genomics approach for assigning functional roles to DNA regulatory motifs from DNA sequence. The algorithm detects associations between a user-specified DNA regulatory motif (expressed as a position weight matrix; PWM) and Gene Ontology terms.
The original method for predicting the roles of transcription factors (TFs starts with a PWM motif describing the DNA-binding affinity of the TF. GOMO uses the PWM to score the promoter region of each gene in the genome for its likelihood to be bound by the TF. The resulting ‘affinity’ scores are then used to test each term in the Gene Ontology for association with high-scoring genes. The algorithm was subsequently extended to leverage conserved signals using multiple, related species in a comparative approach, which greatly improves the resulting annotations.

• ontology or annotation editor
• statistical analysis

Gene Ontology Tree Machine (GOTM) is a web-based tool for the analysis and visualization of sets of interesting genes based on Gene Ontology hierarchies. This tool provides user friendly data navigation and visualization. It generates expandable tree for browsing the GO hierarchy, fixed tree as HTML output for archive and Bar charts at different annotation levels for publication. GOTM provides statistical analysis to indicate GO categories with relatively enriched gene numbers and suggest biological areas that warrant further study. Enriched GO categories can be visualized in Sub-trees or DAGs. Subset of genes can be retrieved by GO term or keyword searching. Detailed information for each gene can be retrieved directly from our a local database GeneKeyDB.

• statistical analysis

GENECODIS is a web-based tool for the functional analysis of gene lists. It integrates different sources of information to search for annotations that frequently co-occur in a set of genes and rank them by their statistical significance. It allows the analysis of annotations from different databases such as GO, KEGG or SwissProt.

• statistical analysis

Generalized Ontological Algorithmic Logical Invariants Extractor (GOALIE) is a tool for the construction of time-course dependent enrichments. Requires an ODBC connection to an instance of the GO database.

• statistical analysis

Genome Function INtegrated Discoverer (GFINDer) is a multi-database system providing large-scale lists of user-classified sequence identifiers with genome-scale biological information and functional profiles biologically characterizing the different gene classes in the list. GFINDer automatically retrieves updated annotations of several functional categories from different sources, identifies the categories enriched in each class of a user-classified gene list, and calculates statistical significance values for each category. Moreover, GFINDer enables to functionally classify genes according to mined functional categories and to statistically analyse the obtained classifications, aiding in better interpreting microarray experiment results.

• statistical analysis

The GO Slim Mapper (aka GO Term Mapper) maps the specific, granular GO terms used to annotate a list of budding yeast gene products to corresponding more general parent GO slim terms. Uses the SGD GO Slim sets.

• statistical analysis
• slimmer-type tool

GOArray is a Perl program which inputs a lists of genes annotated as "of interest" (GOI) or not, and determines if any associated GO terms have an overrepresentation of GOI. A permutation test is optionally used to assess confidence in the results. Output includes multiple visualizations and supplementary information and, for future reference, a summary of the statistical methods used.

• statistical analysis

GOdist is a Matlab program that analyzes Affymetrix microarray expression data implementing Kolmogorov-Smirnov (KS) continuous statistics approach. It also implements the discrete approach using Fisher exact test employing a two-tailed hypergeometric distribution. GOdist enables detection of both kinds of changes within specific GO terms represented on the array in relation to different populations: the global array population, the direct parents of the analyzed GO term and the global parent of it (e.g. biological process, molecular function or cellular component).

• statistical analysis

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.

• statistical analysis
• slimmer-type tool

GOstat is an easy to use web tool to determine statistically significant over- or under-represented GO categories within lists of genes. Data files are updating monthly.

• statistical analysis

GoSurfer uses Gene Ontology information in the analysis of gene sets obtained from genome-wide computations, microarray analysis or any other highly parallel method. It includes rigorous statistical testing, interactive graphics and automated updating of the annotation available for common gene identifiers (UniGene, LocusLink) or Affymetrix probe sets.

• statistical analysis

GOtcha provides a prediction of a set of GO terms that can be assosciated with a given query sequence. Each term is scored independently and the scores calibrated against reference searches to give an accurate percentage likelihood of correctness. These results can be displayed graphically.
The tool is currently web-based; contact David Martin for details of the standalone version.

• statistical analysis

GOToolBox is a series of web-based programs allowing the identification of statistically over- or under-represented terms in a gene dataset relative to a reference gene set; the clustering of functionally related genes within a set; and the retrieval of genes sharing annotations with a query gene. GO annotations can also be constrained to a slim hierarchy or a given level of the ontology and terms can be filtered on evidence codes. Updated monthly with GO and gene association files.

• statistical analysis
• slimmer-type tool

Databases of protein domains and functional sites have become vital resources for the prediction of protein functions. During the last decade, several signature- recognition methods have evolved to address different sequence analysis problems, resulting in rather different and, for the most part, independent databases. Diagnostically, these resources have different areas of optimum application owing to the different strengths and weaknesses of their underlying analysis methods. Thus, for best results, search strategies should ideally combine all of them. InterProScan is a perl-based program which combines these different protein signature recognition methods into one resource.

• ontology or annotation editor
• statistical analysis

L2L is a simple but powerful tool for discovering the hidden biological significance in microarray data. Through an easy-to-use web interface, L2L will mine a list of up- or down-regulated genes for Gene Ontology terms that are significantly enriched. L2L can also compare the list of genes to a database of hundreds of published microarray experiments, in order to identify common patterns of gene regulation. A downloadable command-line version can run customized and batch analyses.

• statistical analysis

MAPPFinder is an accessory program for GenMAPP. This program allows users to query any existing GenMAPP Expression Dataset Criterion against GO gene associations and GenMAPP MAPPs (microarray pathway profiles). The resulting analysis provides the user with results that can be viewed directly upon the Gene Ontology hierarchy and within GenMAPP, by selecting terms or MAPPs of interest.

• statistical analysis

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.

• statistical analysis

MultiExperiment Viewer (MeV) is a versatile microarray data analysis tool, incorporating sophisticated algorithms for clustering, visualization, classification, statistical analysis and biological theme discovery. Analyze gene expression or CGH microarray data and with MeV's many clustering, statistical analysis and graphical display tools. MeV generates informative and interrelated displays of expression and annotation data from single or multiple experiments.

• statistical analysis

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.

• statistical analysis

Probe Explorer is an open access web-based bioinformatics application designed to show the association between microarray oligonucleotide probes and transcripts in the genomic context, but flexible enough to serve as a simplified genome and transcriptome browser. Coordinates and sequences of the genomic entities (loci, exons, transcripts), including vector graphics outputs, are provided for fifteen metazoa organisms and two yeasts. Alignment tools are used to built the associations between Affymetrix microarrays probe sequences and the transcriptomes (for human, mouse, rat and yeasts). Search by keywords is available and user searches and alignments on the genomes can also be done using any DNA or protein sequence query.

• statistical analysis

Profiling of Complex Functionality (ProfCom) is a web-based tool for the functional interpretation of a gene list that was identified to be related by experiments. A trait which makes ProfCom a unique tool is an ability to profile enrichments of not only available Gene Ontology (GO) terms but also of complex function. A complex function is constructed as Boolean combination of available GO terms. The complex functions inferred by ProfCom are more specific in comparison to single terms and describe more accurately the functional role of genes.

• statistical analysis

SeqExpress is a comprehensive analysis and visualisation package for gene expression experiments. GO is used to assign functional enrichment scores to clusters, using a combination of specially developed techniques and general statistical methods. These results can be explored using the in built ontology browsing tool or through the generated web pages. SeqExpress also supports numerous data transformation, projection, visualisation, file export/import, searching, integration (with R), and clustering options.

• statistical analysis

SerbGO is a web-based tool intended to assist researchers determine which microarray tools for gene expression analysis which make use of the GO ontologies are best suited to their projects. SerbGO is a bidirectional application. The user can ask for some features by checking on the Query Form to get the appropriate tools for their interests. The user can also compare tools to check which features are implemented in each one.

• statistical analysis

SOURCE compiles information from several publicly accessible databases, including UniGene, dbEST, UniProt Knowledgebase, GeneMap99, RHdb, GeneCards and LocusLink. GO terms associated with LocusLink entries appear in SOURCE.

• statistical analysis

The Spotfire Gene Ontology Advantage Application integrates GO annotations with gene expression analysis in Spotfire DecisionSite for Functional Genomics. Researchers can select a subset of genes in DecisionSite visualizations and display their distribution in the Gene Ontology hierarchy. Similarly, selection of any process, function or cellular location in the Gene Ontology hierarchy automatically marks the corresponding genes in DecisionSite visualizations.

• statistical analysis

T-Profiler uses the t-test to score changes in the average activity of pre-defined groups of genes. The gene groups are defined based on Gene Ontology categorization, ChIP-chip experiments, upstream matches to a consensus transcription factor binding motif, and location on the same chromosome, respectively. A jack-knife procedure is used to make calculations more robust against outliers. T-profiler makes it possible to interpret microarray data in a way that is both intuitive and statistically rigorous, without the need to combine experiments or choose parameters.

• statistical analysis

The Ontologizer is a Java webstart application for GO term enrichment analysis that provides browsing and graph visualization capabilities. The Ontologizer allows users to analyze data with the standard Fisher exact test and also the parent-child method and topology methods.
The tool can be started directly from the web using Java webstart. For graph visualizations, users need to install the GraphViz library. The tool is freely available to all, and source code is available at SourceForge.

• statistical analysis

Tools for High-throughput Experiments Analysis (THEA) is an integrated information processing system dedicated to the analysis of post-genomic data. It allows automatic annotation of data issued from classification systems with selected biological information (including the Gene Ontology). Users can either manually search and browse through these annotations, or automatically generate meaningful generalizations according to statistical criteria (data mining).

• statistical analysis