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Updated: Jun 10, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis
Published on: July 12, 2022
A scalable approach to investigating sequence-to-function predictions from personal genomes
Anna E Spiro1, Xinming Tu1, Yilun Sheng1
1Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
None:
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.
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