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DeepKOALA is a high-performance deep learning-based tool for rapid protein
function annotation according to the KEGG Orthology (KO) system.
By framing KO assignment as an open-set recognition problem, it can effectively
distinguish between known and unknown functional sequences, thereby reducing
false-positive annotations.
Built on a Gated Recurrent Unit (GRU) architecture, the tool provides excellent
computational efficiency while ensuring high accuracy.
This DeepKOALA runs on CPU machines.
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| Enter FASTA Sequences |
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| or upload a sequence file |
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The file size of up to 300 MB with the limit of 500,000 sequences may be uploaded.
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Model
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High-precision annotation for only full-length sequences.
For full-length and fragmented sequences (e.g., choice for metagenome).
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| E-mail |
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Current database version
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Download DeepKOALA
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Reference
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Yu Z., Meng L., Nguyen C.H., Mamitsuka H., Kanehisa M., OgataH.
DeepKOALA: A Fast and Accurate Deep Learning Framework for KEGG Orthology Assignment.
bioRxiv 2026.01.07.698072; doi:10.64898/2026.01.07.698072
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