GOlien tool: fast and scalable GO term annotation of proteins using a Shannon-entropy k-mer model

Authors

Laczko, L., Pek, D., Kovacs, B. L.

Abstract

Summary: Automated protein function annotation remains challenging as sequence databases outpace curated labels and homology-based transfer fails for proteins lacking close relatives. We present GOlien, a scalable, composition-based method that annotates protein sequences using a Shannon-entropy k-mer model. Built from the CAFA3-based training split and evaluated on the held-out validation split, GOlien achieves micro-averaged of 0.7317 (Biological Process), 0.7610 (Cellular Component) and 0.8295 (Molecular Function). Our tool offers a practical, complementary alternative to existing pipelines and extending annotation coverage for proteins. Availability and Implementation: The command line tool to submit FASTA files and retrieve predicted GO term annotations with the corresponding documentation is available at: https://github.com/probalytiq/golien-tool

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 12 Sep 2026.

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