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G-SESAME / Gene Semantic Similarity Analysis and Measurement Tools
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.
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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.
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.
SML / Semantic Measures Library
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).
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.
GS2 / GO-based similarity of gene sets
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.
GOssTo / Gene Ontology semantic similarity Tool
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-DaGO-Fun / ADaptable Gene Ontology semantic similarity based Functional analysis
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.
SAPP / Semantic Annotation Platform with Provenance
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.
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.
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.
AIGO / Analysis and Intercomparison of Gene Ontology functional annotations
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.
SGFSC / Speeding Gene Functional Similarity Calculation
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.
A web tool focused on calculating Gene Ontology (GO)-based protein semantic similarity. ProteInOn features a stepwise query selection menu, which together with the possibility of selecting results as input for new queries, makes it flexible and customizable. It also incorporates data on protein interactions, allowing for comparative studies between protein similarity and interactions. The tool implements a preliminary weighting factor which increases the specificity of existing semantic similarity measures, and a score for measuring the representativeness of a GO term within a set of proteins.
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.
Integrates several complementary properties in a novel vector space model. IntelliGO provides a customizable and comprehensive method for quantifying gene similarity based on Gene Ontology (GO) annotations. The ability of the tool to express the biological cohesion of sets of genes compares favourably to four existing similarity measures. For inter-set comparison, it consistently discriminates between distinct sets of genes. It allows the influence of weights assigned to evidence codes to be checked.
GFSST / Gene Functional Similarity Search Tool
Identifies genes with related functions from annotated proteome databases. GFSST is a search engine that lets users design their search targets by gene functions. It provides functions not only for similar gene retrieval but also for gene search by one or more Gene Ontology (GO) terms. GFSST provides functions not only for similar gene retrieval but also for gene search by one or more GO terms. This represents a powerful approach for selecting similar genes and gene products from proteome databases according to their functions.
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.
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