The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches.
Automates the process of biological-term classification and the enrichment analysis of gene clusters. ClusterProfiler supports three species, including humans, mice, and yeast. It offers a gene classification method, namely groupGO, to sort genes based on their projection at a specific level of the gene ontology (GO) corpus. This tool is able to calculate enrichment test for GO terms and KEGG pathways based on hypergeometric distribution.
An R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. Four information content (IC)- and a graph-based methods are implemented in the GOSemSim package, multiple species including human, rat, mouse, fly and yeast are also supported. The functions provided by the GOSemSim offer flexibility for applications, and can be easily integrated into high-throughput analysis pipelines.
A set of online tools for measuring the semantic similarities of Gene Ontology (GO) terms and the functional similarities of gene products, and for further discovering biomedical knowledge from the GO database. Visualization techniques are provided in these tools to allow users to inspect the locations of the GO terms within the GO graph and to visually determine the semantic similarity. A batch command interface is also provided for users to execute the tools to measure the semantic similarity of a group of GO terms or functional similarities of a group of genes. Web based APIs are also provided for advanced users.
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.
Measures disease similarity by integrating FunSim and SemSim. SemFunSim allows users to calculate disease similarity using disease-related gene sets in a weighted network of human gene function. SemSim is devised to compute disease similarity using the relationship between two diseases from Disease Ontology. This method assists in understanding associations between diseases and it provides an effective way to predict potential therapeutic chemicals (PTCs) for diseases.
Identifies gene clusters in eukaryotic genomes that utilizes functional categories defined in graph-based vocabularies such as the Gene Ontology (GO). C-Hunter is a clustering algorithm which incorporates knowledge of gene function derived from Gene Ontology with the organization of genes on chromosomes. The software provides output of the clusters and statistical test in human readable format as well as comma-separated format suitable for import into other applications.
Offers a support for biological data mining using the Medical Subject Headings (MeSH) terminology. Meshes includes functions for calculating similarities between genes and gene groups and support about 70 species. This program is also able to support enrichment analysis of whole expression profile or gene list and provides features of visualization to assist users in producing figures for further publication.