Accurate ab initio gene prediction in eukaryotes with Tiberius in multiple clades

Abstract

BackgroundEukaryotic genome annotation is currently bottlenecked by limitations in the generality, scalability or accuracy of computational methods and fewer than 20% of the genomes available at NCBI Datasets have associated gene annotations. Evidence-based pipelines such as BRAKER3 achieve high accuracy but require substantial extrinsic evidence and compute. Deep learning approaches have recently achieved large improvements in ab initio gene prediction accuracy. The end-to-end deep learning gene predictor Tiberius approaches the accuracy of evidence-based annotation on mammalian genomes without using any extrinsic evidence, but its published models were trained on mammals only, limiting its applicability across eukaryotes. ResultsWe extend Tiberius beyond mammals by training lineage-specific models for Mesan-giospermae, Fungi, Vertebrata, Insecta, Chlorophyta and Bacillariophyta, making the tool applicable to 92% of currently available eukaryotic assemblies. Across a benchmark of 33 species, Tiberius achieved higher exon-, transcript- and gene-level accuracy than the other evaluated ab initio methods, Helixer and ANNEVO, improving gene-level F1 score by 12-37 percentage points over Helixer and by 10-22 percentage points over ANNEVO, while also having the fastest runtimes overall. Compared with BRAKER3, which incorporates RNA-Seq and protein evidence, Tiberius approaches state-of-the-art accuracy in Mesangiospermae, Fungi, Bacillariophyta and Chlorophyta, while being on average 80 times faster when using a GPU. A reimplementation of the Tiberius backend reduced runtime by 31% compared to the previous implementation. Tiberius and its models are also available through a web server, which allows annotation of submitted assemblies without local resources. Furthermore, the Vertebrata model has been applied to annotate 2,948 vertebrate assemblies totalling nearly 6 trillion base pairs. ConclusionsTiberius is transferable well beyond Mammalia and reaches accuracy close to evidence-based annotation in several clades at a small fraction of the compute cost. This makes it a practical choice for highly accurate large-scale genome annotation, particularly when extrinsic evidence is unavailable or annotation throughput is limiting. Availability and implementationCode: https://github.com/Gaius-Augustus/Tiberius, web server: https://bioinf.uni-greifswald.de/tiberius.