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

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ID: 307223
2021
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Abstract
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 achieve state-of-the-art performance on prediction of promoters, splice sites and transcription factor binding sites, after easy fine-tuning using small task-specific labeled data. Further, DNABERT enables direct visualization of nucleotide-level importance and semantic relationship within input sequences for better interpretability and accurate identification of conserved sequence motifs and functional genetic variant candidates. Finally, we demonstrate that pre-trained DNABERT with human genome can even be readily applied to other organisms with exceptional performance. We anticipate that the pre-trained DNABERT model can be fined tuned to many other sequence analyses tasks. Availability and implementation The source code, pretrained and finetuned model for DNABERT are available at GitHub (https://github.com/jerryji1993/DNABERT). Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W3127238141 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yanrong Ji, Zhihan Zhou, Han Liu, Ramana V. Davuluri
Journal BMC Bioinformatics
Year 2021
DOI
10.1093/bioinformatics/btab083
URL
https://doi.org/10.1093/bioinformatics/btab083
Keywords Keywords not found

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