DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome.

ABSTRACT:

Motivation

Deciphering the language of non-coding DNA is one of the fundamental problems in genome research. Gene regulatory code is highly complex due to the existence of polysemy and distant semantic relationship, which previous informatics methods often fail to capture especially in data-scarce scenarios.

Results

To address this challenge, we developed a novel pre-trained bidirectional encoder representation, named DNABERT, to capture global and transferrable understanding of genomic DNA sequences based on up and downstream nucleotide contexts. We compared DNABERT to the most widely used programs for genome-wide regulatory elements prediction and demonstrate its ease of use, accuracy and efficiency. We show that the single pre-trained transformers model can simultaneously

SUBMITTER: Ji Y 

PROVIDER: S-EPMC11025658 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature