The Gene Ontology knowledgebase in 2023

By Suzi A Aleksander, James P. Balhoff, S. Carbon, J. Cherry, H. Drabkin, Dustin Ebert, Marc Feuermann, Pascale Gaudet, N. Harris, David P. Hill and 135 more. Genetics, 2023

2,378 citations recorded by the source corpus.

Abstract The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO—a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations—evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)—mechanistic models of molecular “pathways” (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ceaf500410b7469090d01f38827223fb322c6d30

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Damiano Piovesan
2. Peer Bork
3. Ivica Letunic
4. Silvio C. E. Tosatto
5. Lucy J. Colwell
6. Pascal Benkert

In Molecular Biology

1. Ivica Letunic
2. Peer Bork
3. Aron Marchler-Bauer
4. Damiano Piovesan
5. Silvio C. E. Tosatto
6. Narmada Thanki
7. Françoise Thibaud-Nissen
8. Christian J. A. Sigrist

Citations in the corpus

• InterPro: the protein sequence classification resource in 2025 (2024, positive): “Each InterPro entry is assigned a unique accession number, name and short name and is annotated with a descriptive abstract and Gene Ontology (GO) terms ( 13 ) that can be assigned to all sequences matched by that entry ( 14 ).”
• The Reactome Pathway Knowledgebase 2024 (2023, background): “A recent survey ( 9 ) suggests that experimental evidence is available for ∼ 68% of these predicted gene products, or 13 500, leaving ∼ 2350 annotatable human proteins not yet in Reactome and ∼ 6300 ‘dark’ proteins ( 10 ) whose functions are not yet directly accessible for annotation.”