Next-Generation Sequencing (NGS) technologies and high-quality gene structure prediction have enabled the rapid generation of genome sequence data sets for any organism of interest. These advancements have changed the way we design research questions and analyze the data generated. The focus has now shifted to predicting gene function to facilitate improved candidate gene prioritization for phenotypes and traits of interest. Cannabis has a long history of being used for fiber production, as well as for both recreational and medicinal purposes. Two of the most known varieties are the hemp type ‘Finola’ and the drug type ‘Purple kush.’ They have varying degrees of the cannabinoid content with ‘Purple kush’ having comparatively more. Genes associated with cannabinoid synthesis have been successfully mapped, although little information on gene function is known for the rest of the genome. Gene Ontology Meta Annotator for Plants (GOMAP) is a computational pipeline that functionally assigns GO terms to plant protein-coding transcripts. GOMAP assigns GO terms by inference from existing annotations via both sequence and domain similarity and adds more recently developed ‘mixed-methods’ to annotate the protein sequences. For Cannabis, the pipeline produced a median of eleven GO annotations per gene. We are hopeful that our functional annotations of Cannabis genes will support and advance Cannabis research across diverse traits of interest. These datasets will be published on CyVerse and submitted to the GO database.
