Gene Ontology

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Gene Ontology (GO) is an international bioinformatics initiative to standardize part of the vocabulary of the life sciences . Result is the same ontology - database , which is now used worldwide by many biological databases and is constantly evolving. Further efforts are the assignment of GO terms (annotation) to individual genes and their proteins and the provision of appropriate software for using the ontology.

The majority of the institutions participating in GO are American and are supported by governments and a company ( AstraZeneca ). GO is primarily in English and species-neutral and is freely available. It is part of a larger project, the Open Biomedical Ontologies .

Database and terms

GO is a biomedical ontology that covers three areas: “Cellular Component”, “Biological Process” and “Molecular Function”. Each term consists of a name, a number and associated data. The ontology has the topology of a directed acyclic graph .


id: GO:0000016
name: lactase activity
namespace: molecular_function
def: "Catalysis of the reaction: lactose + H2O = D-glucose + D-galactose." [EC:]
synonym: "lactase-phlorizin hydrolase activity" BROAD [EC:]
synonym: "lactose galactohydrolase activity" EXACT [EC:]
xref: EC:
xref: MetaCyc:LACTASE-RXN
xref: Reactome:20536
is_a: GO:0004553 ! hydrolase activity, hydrolyzing O-glycosyl compounds

Data Source:


The gene ontology, like other ontologies, is an attempt to present biological knowledge in a clear manner. Such a representation, even if it claims to be optimal, would have many uses in addition to a standardization of the language, including in the publishing and library sectors. In addition, the structured representation enables use in software that uses biological and clinical knowledge to answer questions and analyze experimental data ( logical reasoning , data mining ).

The most important tools for looking through the GO entries are the ontology editor OBO-edit and the browser AmiGO, which is available as a website. In addition to the presentation of the ontology, OBO-edit provides tools for querying and filtering the ontology information.

For the analysis of experiments that result in a large number of values ​​that are assigned to individual genes, different data mining objectives with different algorithms, together with the given gene ontology, can lead to nontrivial conclusions from the experiment. For example, cluster analysis algorithms are used to determine which biological processes are mainly changed by certain environmental toxins in cells by analyzing the results of corresponding microarray experiments using the GO annotations of all genes in the organism concerned.

See also

Web links

Individual evidence

  1. ^ GO Consortium Contributors List. In: Accessed January 8, 2018 .
  2. The GO Consortium: gene_ontology.1_2.obo (OBO 1.2 flat file) March 16, 2009. Accessed March 16, 2009.
  3. M. Ashburner , CA Ball et al. a .: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. In: Nature genetics . Volume 25, Number 1, May 2000, pp. 25-29. doi: 10.1038 / 75556 . PMID 10802651 . PMC 3037419 (free full text).
  4. ^ GO Consortium: The Gene Ontology in 2010: extensions and refinements. In: Nucleic Acids Research . 38, 2009, pp. D331-D335, doi: 10.1093 / nar / gkp1018 .
  5. J. Day-Richter, MA Harris et al. a .: OBO-Edit – an ontology editor for biologists. In: Bioinformatics. Volume 23, Number 16, August 2007, pp. 2198-2200, ISSN  1367-4811 . doi: 10.1093 / bioinformatics / btm112 . PMID 17545183 .
  6. P. Pavlidis, J. Qin et al. a .: Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex. In: Neurochemical research. Volume 29, Number 6, June 2004, pp. 1213-1222, ISSN  0364-3190 . PMID 15176478 .