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Permits functional annotation, management, and data mining of novel sequence data. Blast2GO is based on the utilization of common controlled vocabulary schemas, the gene ontology (GO). It takes in consideration similarity, the extension of the homology, the database of choice, the GO hierarchy, and the quality of the original annotations. This tool is suitable for plant genomics research. It generates functional annotation and assesses the functional meaning of their experimental results.
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Provides tool to perform functional enrichment analysis and mine additional information. g:Profiler is a web server that allows to characterize and manipulate gene lists of high-throughput genomics. This tool analyses flat or ranked gene lists for enriched features, converts gene identifiers of different classes, maps genes to orthologous genes in related species, finds similarly expressed genes from public microarray and maps human single nucleotide polymorphisms (SNP) to gene names, chromosomal locations and variant consequence terms from Sequence Ontology (SO).
EASE / Expression Analysis Systematic Explorer
Allows biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE and other high-throughput genomic data. EASE automates the process of biological theme determination for lists of genes. It allows investigators to systematically mine the mass of functional information associated with data generated by microarray, proteomics or SAGE studies. This tool can be a self-extracting distribution which is ready to be used as soon as it is unzipped.
A tool for biological interpretation of 'omic' data – including data from gene expression microarrays. GoMiner leverages the Gene Ontology (GO) to identify the biological processes, functions and components represented in these lists. Instead of analyzing microarray results with a gene-by-gene approach, GoMiner classifies the genes into biologically coherent categories and assesses these categories. The insights gained through GoMiner can generate hypotheses to guide additional research.
Provides a series of programs allowing the functional investigation of groups of genes, based on the Gene Ontology resource. GOToolBox allows 1) the identification of statistically relevant over- or under-represented terms in a gene dataset, 2) the clustering of functionally related genes within a set and 3) the retrieval of genes sharing annotations with a query gene. The user can also constrain the GO annotations to a slim hierarchy or to a given level of the ontology, in order to facilitate the interpretation of the results.
JProGO / Java Tool for the Functional Analysis of Prokaryotic Microarray Data using the Gene Ontology
Enables a comparative exploratory analysis of the microarray data using the strengths of the selected statistical methods. JProGO is an integrative program suite which allows the easy and intuitive functional interpretation of mass gene expression data. It offers an integrative platform for the majority of statistical methods commonly used for the determination of the significant Gene Ontology (GO) nodes.
Enables scientists to employ annotation information, clustering algorithms and visualization tools in their array data analysis and interpretation strategy. goCluster is a solution that implements a statistical analysis procedure yielding gene lists that are subsequently searched for non-random enrichment of related gene ontology (GO) annotation terms from all three categories (biological process, molecular function and cellular component). The package provides four clustering algorithms and GeneOntology terms as prototype annotation data.
An R/Bioconductor package for identification and visualisation of gene expression profiles that best classify sample groups according to any known experimental factor. In contrast to most GO term summarisation approaches, GOexpress integrates prior biological knowledge and gene expression data from individual sample replicates to rank molecular pathways based on the capacity of functionally related groups of genes to classify multiple sample groups. Notably, the use of multiple genes for GO-based classification improves the robustness and biological relevance of the resulting interpretations and predictions.
An easy-to-use graphical exploration tool with built-in statistical features that allow a rapid assessment of the biological functions represented in large gene sets. GoSurfer takes one or two list(s) of gene identifiers (Affymetrix probe set ID) as input and retrieves all the Gene Ontology (GO) terms associated with the input genes. GoSurfer visualises these GO terms in a hierarchical tree format. With GoSurfer, users can perform statistical tests to search for the GO terms that are enriched in the annotations of the input genes. These GO terms can be highlighted on the GO tree. Users can manipulate the GO tree in various ways and interactively query the genes associated with any GO term. The user-generated graphics can be saved as graphics files, and all the GO information related to the input genes can be exported as text files.
Performs Gene Ontology (GO)-based functional enrichment analysis. EasyGO is a web-app that for crop and farm animal species, including Affymetrix GeneChips for 12 plants and 3 farm animals, together with Arabidopsis and rice (indica and japonica) gene names. Analysis results are displayed in an easy-to-read style. It compares a query list with a previously computed background composed of all known genes for a species or all probe sets on a GeneChip. EasyGO is expected to be suitable for the needs of the agronomical research community.
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