A scalable approach to investigating sequence-to-function predictions from personal genomes

Sequence-to-function (S2F) models can evaluate arbitrary DNA sequences, yet they struggle to fully capture inter-individual variation in gene expression. We introduce SAGE-net, a scalable framework for training and evaluating S2F models using personal genomes. While personal genome training improves gene expression prediction accuracy for held-out individuals, performance gains arise primarily from identifying predictive variants rather than learning a cis-regulatory grammar that generalizes across loci. Scalable software will be critical to advancing S2F models for personal genomics.