Eukan: a fully automated nuclear genome annotation pipeline for less studied and divergent eukaryotes

Here, we introduce a new annotation pipeline, called Eukan, designed to deliver reliably high-quality results across a broad range of eukaryotes. First, experimental evidence is automatically leveraged to refine predictions, specifically, RNA-Seq coverage to inform generalized Hidden Markov Model gene prediction and intron lengths to inform protein sequence alignments. Second, a consensus is created from an empirically optimized weighting of gene predictions from multiple sources. Third, Eukan runs a post-annotation routine to recover gene predictions missing from the consensus that otherwise have strong transcript support and appear to be protein-coding. We compare the results of Eukan with those of three popular freely available pipelines (Maker, Braker, and Gemoma) on 17 phylogenetically diverse haploid and diploid nuclear genomes. In addition to the commonly reported annotation accuracy statistics, we define a novel classification system of critical defects commonly observed in automated annotations. Furthermore, we demonstrate that each of the tested pipelines correctly identified the majority of the validated "gold standard" genes across the test set, but each pipeline uniquely generates a non-negligible portion of either fragmented, artificially fused, or missing genes. Despite that, Eukan performs consistently well where other pipelines encounter challenges, such as for compact protist genomes.