Investigating the performance of foundation models on human 3'UTR sequences.

ABSTRACT: Foundation models, such as DNABERT and Nucleotide Transformer, have recently shaped a new direction in DNA research. Trained in an unsupervised manner on a vast quantity of genomic data, they can be used for a variety of downstream tasks, such as promoter prediction, DNA methylation prediction, gene network prediction, or functional variant prioritization. However, these models are often trained and evaluated on entire genomes, neglecting genome partitioning into different functional regions. In our study, we investigate the efficacy of various unsupervised approaches, including genome-wide and 3' untranslated region (3'UTR)-specific foundation models on human 3'UTR regions. To this end, we train a set of popular transformer architectures on a 3'UTR-specific dataset comprising 3 783 714 3'

SUBMITTER: Vilov S 

PROVIDER: S-EPMC12445641 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature