Gene Ontology
Gene Ontology (GO ) is an international bioinformatics initiative to unify a part of the vocabulary of the life sciences. Result of the same ontology database, which is now used worldwide by many biological databases, and constantly evolving. Further efforts are the assignment of GO terms ( annotation ) to individual genes and their proteins, as well as the provision of appropriate software for use of the ontology.
The participating institutions are GO in most U.S. American and by governments and companies ( AstraZeneca) support. GO is held primarily in English and species- neutral and at leisure. It is part of a larger project, the Open Biomedical Ontologies.
Database and Termini
GO is a biomedical ontology that covers three areas: "Cellular component ", " biological process " and "molecular function." Each term consists of a name, number and associated data. The ontology has the topology of a directed acyclic graph.
Example
Id: GO: 0000016 name: lactase activity namespace: molecular_function def: " Catalysis of the reaction: lactose H2O = D -glucose D -galactose. " [ EC: 3.2.1.108 ] synonym: " lactase - phlorizin hydrolase activity" BROAD [ EC: 3.2.1.108 ] synonym: "lactose galactohydrolase activity" EXACT [ EC: 3.2.1.108 ] xref: EC: 3.2.1.108 xref: MetaCyc: RXN - LACTASE xref: Reactome: 20536 is_a: GO: 0004553! hydrolase activity, hydrolyzing O- glycosyl compounds Data source:
Applications
The Gene Ontology, like other ontologies, an attempt to represent biological knowledge clearly. Such a representation, also if it lays claim to optimality, had next to a standardization of the language many applications, including in the publishing and librarianship. In addition, the structured representation use in software is possible to use as biological and clinical knowledge to answer questions and to analyze experimental data ( Reasoning, Data Mining).
The most important tools to look through the GO entries are the ontology editor OBO -edit and the browser Amigo, which is available as a web page. OBO -edit is in addition to the presentation of the ontology tools for querying and filtering of Ontologieinformation available.
For the analysis of experiments included as a result of a large amount of values that are each associated with the individual genes, various objectives of the data mining, each with different algorithms, together with the predetermined Gene Ontology result in non-trivial Conclusions from the experiment. Cluster analysis algorithms example, be used to determine which biological processes are mainly affected by certain environmental toxins into cells by analyzing results of related microarray experiments using the GO annotations of all genes of the organism concerned.