No genome sequencing project is complete without structural and functional annotation. Gene models and functional predictions for these models can be obtained relatively easily using computational methods, but they are prone to errors. We describe herein the steps we use to manually curate gene models and functionally annotate them. Our approach is to examine each gene model carefully, and improve its structure if necessary, using a comprehensive set of experimental and computational data as evidence. Then, functional predictions are assigned to the gene models based on conserved protein domains and sequence similarities. We use stringent sequence similarity cutoffs and reviewed sequence-database records as external sources for our annotations. By methodically choosing which evidence to use for each annotation, we minimize the risk of adopting and assigning false predictions to the gene models.