Transformer model generated bacteriophage genomes are compositionally distinct from natural sequences.

ABSTRACT: Novel applications of language models in genomics promise to have a large impact on the field. The megaDNA model is the first publicly available generative model for creating synthetic viral genomes. To evaluate megaDNA's ability to recapitulate the nonrandom genome composition of viruses and assess whether synthetic genomes can be algorithmically detected, compositional metrics for 4969 natural bacteriophage genomes and 1002 de novo synthetic bacteriophage genomes were compared. Transformer-generated sequences had varied but realistic genome lengths, and 58% were classified as viral by geNomad. However, the sequences demonstrated consistent differences in various compositional metrics when compared to natural bacteriophage genomes by rank-sum tests and principal component analyses.

SUBMITTER: Ratcliff J 

PROVIDER: S-EPMC11409064 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature