How far are we from personalized gene expression prediction using sequence-to-expression deep neural networks?

Abstract

Deep learning (DL) methods accurately predict various functional properties from genomic DNA, including gene expression, promising to serve as an important tool in interpreting the full spectrum of genetic variations in personal genomes. However, systematic out-of-sample benchmarking is needed to assess the gap in their utility as personalized DNA interpreters. Using paired Whole Genome Sequencing and gene expression data we evaluate DL sequence-to-expression models, identifying their critical failure to make correct predictions on a substantial number of genomic loci, highlighting the limits of the current model training paradigm.