DeepKOALA: a scalable deep learning framework for KEGG Orthology assignment

The KEGG Orthology (KO) system links DNA and protein sequences to biological functions and pathways, providing a curated, fundamental, and consistent annotation framework across all domains of life. While accurate, traditional sequence alignment-based annotation methods are computationally expensive, which severely limits their application in large-scale datasets. To address this challenge, we introduce Deep KEGG Orthology and Links Annotation (DeepKOALA), a deep learning approach based on Gated Recurrent Units (GRU), which frames KO annotation as an open-set recognition task. This design reduces false positives arising from out-of-scope sequences and, together with a lightweight GRU backbone, enables high-throughput annotation. The GRU-based model was benchmarked against four other deep learning architectures and showed the best balance between speed and accuracy. We then trained a GRU-based model, DeepKOALA, and performed a cross-species evaluation against existing KO annotation tools. In this comparison, DeepKOALA achieved a F1 of 83.37%, which is comparable to existing alignment-based tools. Meanwhile, the speed of DeepKOALA was 36.5-fold faster than Blast KEGG Orthology and Links Annotation (BlastKOALA). We also provide a specialized fragment model for handling incomplete sequences and an optional multi-domain mode. Together, these features make DeepKOALA a scalable and efficient option for high-throughput function annotation.