Deep-learning prediction of gene expression from personal genomes.

ABSTRACT:

Background

Models that predict gene expression levels from DNA sequence struggle to predict differences between individuals when given their personal genome sequences. These models are generally trained on reference genome sequences, and thus have never observed examples of genetic variation at any locus during training, which may explain their lack of generalizability to personal genome sequences that do contain variation.

Results

We utilize fine-tuning with personal genomes and matched tissue-specific gene expression values to develop Variformer, a deep sequence-based neural network. Across held-out people, Variformer predicts expression with accuracy that approaches the cis-heritability of most genes and prioritizes genetic variants across the allele frequency spectrum th

SUBMITTER: Drusinsky S 

PROVIDER: S-EPMC12869966 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature