The Gene Ontology (GO) resource, established in 1998, has become a widely used knowledgebase for gene functions. Over the past two years, the GO resource has undergone significant developments, including the introduction of new frameworks and tools for representing gene functions. The molecular function ontology has been refactored to better represent gene product activities, with a focus on transcription regulator activities. Quality assurance efforts have been increased to address potentially outdated or inaccurate annotations. New evidence codes for high-throughput experiments now allow users to filter out annotations from these sources. GO-CAM, a new framework for representing gene function, has been released, and users can now explore the growing repository of these models. The 'GO ribbon' widget has been introduced for visualizing GO annotations to a gene, which can be easily embedded in any web page. The GO knowledgebase includes over 7 million annotations to genes from over 3,200 species, with 750,000 supported by experimental data. Annotations are created by linking specific gene products to GO terms, with evidence based on peer-reviewed publications. The accuracy of the GO resource is continually refined through internal checks and feedback from the user community. The GO knowledgebase supports computational queries, such as identifying gene functions or genes involved in specific biological processes. It is used in GO enrichment analysis, which helps identify functions that are unusually common among a set of genes. The GO resource has been updated to include a new framework for representing gene functions, GO-CAM, which is more expressive than standard GO annotations. The GO resource now produces monthly releases with unique DOIs, enabling reproducible analyses. The GO resource continues to integrate with external ontology resources and maintains cross-references to widely-used external resources. The molecular function branch of GO has been refactored to better represent higher-level functions, with new terms and relations added. The transcription factor areas of GO have also been refactored to better capture different types of protein and DNA binding activities. The GO resource has also introduced subsets (slims) for providing an overview of gene functions, locations, or roles. The GO ribbon is a configurable tool for visualizing GO annotations, allowing users to explore gene functions. The GO resource has also introduced new evidence codes for high-throughput experiments, enabling users to filter out annotations from these sources. The GO resource continues to be an open, community project, with ongoing efforts to review annotations and improve the resource. The GO Consortium is increasing efforts to review annotations, especially those that may have been superseded by newer findings. The GO resource is now monthly released with persistent DOIs, and all published GO-based analyses are encouraged to cite these DOIs to ensure reproducibility. The GO-CAM framework is being used to create a growing set of curated biological models, and the analysis tool developer community is encouraged to explore these models.The Gene Ontology (GO) resource, established in 1998, has become a widely used knowledgebase for gene functions. Over the past two years, the GO resource has undergone significant developments, including the introduction of new frameworks and tools for representing gene functions. The molecular function ontology has been refactored to better represent gene product activities, with a focus on transcription regulator activities. Quality assurance efforts have been increased to address potentially outdated or inaccurate annotations. New evidence codes for high-throughput experiments now allow users to filter out annotations from these sources. GO-CAM, a new framework for representing gene function, has been released, and users can now explore the growing repository of these models. The 'GO ribbon' widget has been introduced for visualizing GO annotations to a gene, which can be easily embedded in any web page. The GO knowledgebase includes over 7 million annotations to genes from over 3,200 species, with 750,000 supported by experimental data. Annotations are created by linking specific gene products to GO terms, with evidence based on peer-reviewed publications. The accuracy of the GO resource is continually refined through internal checks and feedback from the user community. The GO knowledgebase supports computational queries, such as identifying gene functions or genes involved in specific biological processes. It is used in GO enrichment analysis, which helps identify functions that are unusually common among a set of genes. The GO resource has been updated to include a new framework for representing gene functions, GO-CAM, which is more expressive than standard GO annotations. The GO resource now produces monthly releases with unique DOIs, enabling reproducible analyses. The GO resource continues to integrate with external ontology resources and maintains cross-references to widely-used external resources. The molecular function branch of GO has been refactored to better represent higher-level functions, with new terms and relations added. The transcription factor areas of GO have also been refactored to better capture different types of protein and DNA binding activities. The GO resource has also introduced subsets (slims) for providing an overview of gene functions, locations, or roles. The GO ribbon is a configurable tool for visualizing GO annotations, allowing users to explore gene functions. The GO resource has also introduced new evidence codes for high-throughput experiments, enabling users to filter out annotations from these sources. The GO resource continues to be an open, community project, with ongoing efforts to review annotations and improve the resource. The GO Consortium is increasing efforts to review annotations, especially those that may have been superseded by newer findings. The GO resource is now monthly released with persistent DOIs, and all published GO-based analyses are encouraged to cite these DOIs to ensure reproducibility. The GO-CAM framework is being used to create a growing set of curated biological models, and the analysis tool developer community is encouraged to explore these models.