Vir2vec: A Viral Genome-Wide Viral Embedding.

ABSTRACT: Genomic language models (gLMs) have recently emerged as powerful numerical surrogates for DNA, but existing architectures are largely focused on human DNA or trained on limited viral references, and no dedicated benchmark currently exists for viral genome understanding. Here we introduce vir2vec, a 422-million-parameter, decoder-only gLM obtained by continual pretraining of Mistral-DNA on a rigorously curated pan-viral corpus of 565,747 complete genomes spanning 295 viral species. vir2vec operates on byte-pair-encoded DNA subwords and exposes fixed-length genome-level embeddings that are reused across tasks. Additionally, we present vGUE, a unified benchmark for viral representation learning. In vGUE, we precompute vir2vec embeddings and feed them to simple classifiers (logistic regression

SUBMITTER: Rancati S 

PROVIDER: S-EPMC12713144 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature