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.
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.
A Java Toolkit dedicated to semantic measures computation and analysis. SML-Toolkit is composed of various tools related to semantic measures computation and analysis. Those tools are provided through a common command-line interface.
A generic and open source Java library dedicated to the computation and analysis of semantic measures. SML can be used to compute semantic similarities of concepts/terms defined in structured terminologies and ontologies. It can also be used to assess the semantic similarity of pairs of entities annotated by concepts, e.g. patient records annotated by groups of concepts, genes annotated by GO terms, PubMed articles annotated by MeSH descriptors. The library supports various ontology formats and specifications (e.g. OBO, RDF, OWL).
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.
Offers a way to measure similarities between cross-categories gene ontology (GO) terms. CroGo proposes a ranking-based method to evaluate links between two gene sets based on the gene co-functional network. The application first starts from two GO categories and a gene co-functional network for measuring the association between two genes based on the gene network. Then, it computes similarity between two gene sets for finally merging network-based and GO-based information to determine the similarity of the two terms cross categories.
A GO-based measure of gene set similarity that is computable in linear time in the size of the gene set. The measure quantifies the similarity of the GO annotations among a set of genes by averaging the contribution of each gene's GO terms and their ancestor terms with respect to the GO vocabulary graph. To study the performance of our method, we compared our measure with an established pair-based measure when run on gene sets with varying degrees of functional similarities. In addition to a significant speed improvement, our method produced comparable similarity scores to the established method.
A user-friendly software system for calculating semantic similarities between gene products according to the Gene Ontology. GOssTo is bundled with six semantic similarity measures, including both term- and graph-based measures, and has extension capabilities to allow the user to add new similarities. Importantly, for any measure, GOssTo can also calculate the Random Walk Contribution that has been shown to greatly improve the accuracy of similarity measures.
A portable software package integrating all known GO information content-based semantic similarity measures and relevant biological applications associated with these measures. A-DaGO-Fun has the advantage not only of handling datasets from the current high-throughput genome-wide applications, but also allowing users to choose the most relevant semantic similarity approach for their biological applications and to adapt a given module to their needs.
An efficient method for measuring semantic similarity of GO terms using their GO definitions, which is based on the Gloss Vector measure commonly used in natural language processing. The simDEF approach builds optimized definition vectors for all relevant GO terms, and expresses the similarity of a pair of proteins as the cosine of the angle between their definition vectors.
Annotates automatically genome sequences using standard tools. SAPP is a semantic framework for large scale comparative functional genomics studies. Annotation results and their provenance are stored in a Linked Data format, thus enabling the deployment of mining capabilities of the Semantic Web. This application supports periodic querying, comparison and linking of diverse annotation sources, resulting in up-to-date genome annotations.
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.
An R package for computing semantic similarity between genes using Gene Ontology annotation and for clustering genes based on the similarity measures. The methodology allows rapid identification of genes sharing the same Gene Ontology cluster.
Calculates semantic similarities between gene ontology (GO) terms based on GO directed acyclic graphs (DAGs) topology. GOGO can compute the semantic similarities between one or more pairs of GO terms, functional similarities between one or more pairs of genes, and pairwise functional similarities between a list of genes. It can also classify multiple genes based on the functional similarities between genes.
Allows to calculate the functional similarity of genes based on various information theoretic similarity concepts for GO terms. GOSim extends existing tools by providing additional lately developed functional similarity measures for genes. These can e.g. be used to cluster genes according to their biological function. Vice versa, they can also be used to evaluate the homogeneity of a given grouping of genes with respect to their GO annotation. GOSim hence provides the researcher with a flexible and powerful tool to combine knowledge stored in GO with experimental data. It can be seen as complementary to other tools that, for instance, search for significantly overrepresented GO terms within a given group of genes.
An R-based software package to compute the similarity between diseases and to measure the similarity between human genes in terms of diseases. DOSim incorporates a DO-based enrichment analysis function that can be used to explore the disease feature of an independent gene set. A multilayered enrichment analysis (GO and KEGG annotation) annotation function that helps users explore the biological meaning implied in a detected gene module is also part of the DOSim package.
Incorporates many different Gene Ontology (GO) similarity measures for exploring, analyzing and comparing GO terms and proteins within the context of GO. DaGO-Fun uses GO data and UniProt proteins with their GO annotations as provided by the Gene Ontology Annotation (GOA) project to precompute GO term information content (IC), enabling rapid response to user queries. The DaGO-Fun online tool presents the advantage of integrating all the relevant IC-based GO similarity measures, including topology- and annotation-based approaches to facilitate effective exploration of these measures, thus enabling users to choose the most relevant approach for their application. Furthermore, this tool includes several biological applications related to GO semantic similarity scores, including the retrieval of genes based on their GO annotations, the clustering of functionally related genes within a set, and term enrichment analysis.
Consists in a set of twelve metrics. AIGO serves for the analysis and the inter-comparison of gene ontology (GO) functional annotations. This tool allows users to compare publicly available functional annotations and to highlight their differences. It can assist users in the evaluation of the annotation quality thanks to a gold-standard. More, it can be used to monitor the evolution of functional annotations by comparing different releases over time, in order to detect major variations, or to identify potentially incorrect annotations.
An online tool that is bundled with seven typical measures. SGFSC is able to calculate two types of similarity: semantic similarity of Gene Ontology (GO) pairs and the functional similarity of genes or gene products using GO and GOA files. It takes into account the following options: (i) the similarity type to calculate; (ii) the method to use; and (iii) GO type to use. Currently, SGFSC can measure semantic similarity and functional similarity using the GO database and UniProt GO Annotation.