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PreprintCC BY-NC-NDbioRxiv

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

  • L. Laczko1
  • D. Pek
  • B. L. Kovacs
  1. 1University of Debrecen, Faculty of Health Sciences, One Health Institute, Department of Bioinformatics
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Abstract

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

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Cite

BibTeXRIS
BibTeX
@misc{laczko2026golien,
  title = {{GOlien tool: fast and scalable GO term annotation of proteins using a Shannon-entropy k-mer model}},
  author = {L. Laczko and D. Pek and B. L. Kovacs},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.04.749510},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.04.749510v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - GOlien tool: fast and scalable GO term annotation of proteins using a Shannon-entropy k-mer model
AU  - L. Laczko
AU  - D. Pek
AU  - B. L. Kovacs
PY  - 2026
DA  - 2026/09/04
PB  - bioRxiv
DO  - 10.64898/2026.09.04.749510
UR  - https://www.biorxiv.org/content/10.64898/2026.09.04.749510v1
AB  - 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
ER  -