Predicting functional constraints across evolutionary timescales with phylogeny-informed genomic language models.

ABSTRACT: Genomic language models (gLMs) have emerged as a powerful approach for learning genome-wide functional constraints directly from DNA sequences. However, standard gLMs adapted from natural language processing often require extremely large model sizes and computational resources, yet still fall short of classical evolutionary models in predictive tasks. Here, we introduce GPN-Star (Genomic Pretrained Network with Species Tree and Alignment Representation), a biologically grounded gLM featuring a phylogeny-aware architecture that leverages whole-genome alignments and species trees to model evolutionary relationships explicitly. Trained on alignments spanning vertebrate, mammalian, and primate evolutionary timescales, GPN-Star achieves state-of-the-art performance across a wide range of varian

SUBMITTER: Ye C 

PROVIDER: S-EPMC12458161 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature