The rapid advancement of DNA sequencing technologies and computational algorithms has led to an unprecedented surge in genomic data, driven by several large-scale sequencing projects worldwide. Orthol...

Identifying orthologs and paralogs and characterizing function (and functional divergence) are core parts of modern genome annotation. This review will touch ...

Abstract. The eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) database is a phylogenomic resource for orthology inference, evol

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 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 further performed a cross-species evaluation of DeepKOALA in comparison with existing KO annotation tools, where it achieved an F1-score of 0.8653 and 37.5-fold acceleration compared with 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, efficient, and accurate solution for high-throughput function annotation. ### Competing Interest Statement The authors have declared no competing interest. JSPS/KAKENHI, 22H00384 HFSP, RGP011/2024

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 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 further performed a cross-species evaluation of DeepKOALA in comparison with existing KO annotation tools, where it achieved an F1-score of 0.8653 and 37.5-fold acceleration compared with 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, efficient, and accurate solution for high-throughput function annotation. ### Competing Interest Statement The authors have declared no competing interest. JSPS/KAKENHI, 22H00384 HFSP, RGP011/2024

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. Wh...

Abstract. The eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) database is a phylogenomic resource for orthology inference, evol

Abstract. The eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) database is a phylogenomic resource for orthology inference, evol

Abstract. The eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) database is a phylogenomic resource for orthology inference, evol

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