The Gene Ontology knowledgebase in 2023

Abstract

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and non-coding 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 Gene Ontology – a computational knowledge structure describing 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, as well as guidance on how users can best make use of the data we provide. We conclude with future directions for the project.

Identification of LMAN1- and SURF4-Dependent Secretory Cargoes

“…Affected proteins include known SURF4 cargoes such as APOB, APOA1, and APOA2, , several putative cargoes identified in other mass spectrometry based studies, , as well as a number of potentially novel SURF4 cargoes (Figure B and Table S8). We conducted Gene Ontology (GO) enrichment analysis , using PANTHER to assess the biological processes affected by potential SURF4 cargoes. Analyzing secreted proteins sensitive to SURF4 deletion ( i.e.…”

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“…Using AmiGO2, 32,33 we extracted all gene ontology (GO) terms annotated to ADORA1 or MTNR1A. We filtered for terms specific to biological processes in mammals.…”

MOAL: Multi-Omic Analysis at Lab. A simplified methodology workflow to make reproducible omic bioanalysis

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