PhageTransformer - scalable and accurate host assignments for bacteriophages

Authors

Siemers, M., Lopez, J. L., Dutilh, B. E.

Abstract

Bacteriophages can only be understood through their interactions with bacterial hosts. As environmental sequencing efforts expanded, the number of available phage genome sequences has exploded, yet the vast majority of these sequences lack host information. Predicting the host of a newly observed phage is therefore a key challenge in virology. Several computational tools can predict phage-host relationships from genomic data, but they share notable limitations: (1) the number of different hosts that can be predicted remains relatively restricted; (2) tools tend to assign confident host predictions to non-viral input sequences; and (3) most tools have a trade-off between accuracy and speed. Here we present PhageTransformer (PT), a deep learning model for phage-host prediction that addresses these limitations. We benchmark PT against existing tools on 3,881 independent phage-host pairs from GenBank and public HiC data, and demonstrate that it achieves competitive or superior prediction accuracy at greatly reduced runtime.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 31 Aug 2026.

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