DNA language models are powerful predictors of genome-wide variant effects.

ABSTRACT: The expanding catalog of genome-wide association studies (GWAS) provides biological insights across a variety of species, but identifying the causal variants behind these associations remains a significant challenge. Experimental validation is both labor-intensive and costly, highlighting the need for accurate, scalable computational methods to predict the effects of genetic variants across the entire genome. Inspired by recent progress in natural language processing, unsupervised pretraining on large protein sequence databases has proven successful in extracting complex information related to proteins. These models showcase their ability to learn variant effects in coding regions using an unsupervised approach. Expanding on this idea, we here introduce the Genomic Pre-trained Network (GPN

SUBMITTER: Benegas G 

PROVIDER: S-EPMC10622914 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature