Using the Gene Ontology Annotations in UniProt

he Gene Ontology (GO)(https://geneontology.org/) is a framework used to describe gene functions with respect to biological processes, molecular functions, and cellular components. GO is critical in providing a standardized vocabulary that enables consistent gene function annotation and comparison across species. This video introduces GO and the different GO annotation processes: the standard GO annotation, the network-based detailed GO-CAM (Gene Ontology Causal Activity Models), and the PAN-GO (Phylogenetic ANnotation using Gene Ontology) approach which integrates evolutionary and network information. Hands-on guidance will be provided on how to navigate GO data through key bioinformatics resources such as UniProt, QuickGO, and AmiGO, focusing on how to access, interpret, and visualize GO annotations. Finally, the course introduces the concept of GO annotation enrichment, explaining how enrichment analysis can help identify overrepresented biological processes or functions in experimental datasets. The video was recorded live during the course streamed on June 15, 2026. Target audience: PhD students, postdoctoral and other researchers in the life sciences from both academia and industry who are interested in protein function, and how these are tied to biological processes and pathways. Speakers (in order of appearance): Pascale Gaudet, Senior Project Manager, Swiss-Prot group. Patrick Masson, Senior Biocurator, Swiss-Prot group. Elisabeth Gasteiger, Senior User Experience and Support Manager, Swiss-Prot group. Marc Feuermann, Senior Biocurator, Swiss-Prot group. Any question about this talk? Contact UniProt (https://www.uniprot.org/contact) 00:00 Introduction 01:48 Become familiar with the Gene Ontology 19:37 GO Annotations: Standard GO Annotations and GO-Causal Activity Models 48:41 Locate GO data in QuickGO, AmiGO, and UniProt 57:40 GO annotations in genome and protein databases 1:23:55 Insights from Phylogenetic Annotations 1:57:28 The human functionome 2:06:41 Big data analysis using Gene Ontology: enrichment analysis 2:16:35 Resources