Elucidation of DNA methylation on N 6-adenine with deep learning

Document Type

Article

Publication Date

8-1-2020

Abstract

Research on DNA methylation on N6-adenine (6mA) in eukaryotes has received much recent attention. Recent studies have generated a large amount of 6mA genomic data, yet the role of DNA 6mA in eukaryotes remains elusive, or even controversial. We argue that the sparsity of DNA 6mA in eukaryotes, the limitations of current biotechnologies for 6mA detection and the sophistication of the 6mA regulatory mechanism together pose great challenges for elucidation of DNA 6mA. To exploit existing 6mA genomic data and address this challenge, here we develop a deep-learning-based algorithm for predicting potential DNA 6mA sites de novo from sequence at single-nucleotide resolution, with application to three representative model organisms, Arabidopsis thaliana, Drosophila melanogaster and Escherichia coli. Extensive experiments demonstrate the accuracy of our algorithm and its superior performance compared with conventional k-mer-based approaches. Furthermore, our saliency maps-based context analysis protocol reveals interesting cis-regulatory patterns around the 6mA sites that are missed by conventional motif analysis. Our proposed analytical tools and findings will help to elucidate the regulatory mechanisms of 6mA and benefit the in-depth exploration of their functional effects. Finally, we offer a complete catalogue of potential 6mA sites based on in silico whole-genome prediction.

Identifier

85088866093 (Scopus)

Publication Title

Nature Machine Intelligence

External Full Text Location

https://doi.org/10.1038/s42256-020-0211-4

e-ISSN

25225839

First Page

466

Last Page

475

Issue

8

Volume

2

Grant

CIE160021

Fund Ref

National Science Foundation

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