Species-aware DNA language models capture regulatory elements and their evolution.

ABSTRACT:

Background

The rise of large-scale multi-species genome sequencing projects promises to shed new light on how genomes encode gene regulatory instructions. To this end, new algorithms are needed that can leverage conservation to capture regulatory elements while accounting for their evolution.

Results

Here, we introduce species-aware DNA language models, which we trained on more than 800 species spanning over 500 million years of evolution. Investigating their ability to predict masked nucleotides from context, we show that DNA language models distinguish transcription factor and RNA-binding protein motifs from background non-coding sequence. Owing to their flexibility, DNA language models capture conserved regulatory elements over much further evolutionary distances than seq

SUBMITTER: Karollus A 

PROVIDER: S-EPMC10985990 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature