Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data

Qian Liu, Li Fang, Guoliang Yu, Depeng Wang, Chuan-Le Xiao () and Kai Wang ()
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Qian Liu: Children’s Hospital of Philadelphia
Li Fang: Children’s Hospital of Philadelphia
Guoliang Yu: Sun Yat-sen University
Depeng Wang: Grandomics Biosciences
Chuan-Le Xiao: Sun Yat-sen University
Kai Wang: Children’s Hospital of Philadelphia

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: Abstract DNA base modifications, such as C5-methylcytosine (5mC) and N6-methyldeoxyadenosine (6mA), are important types of epigenetic regulations. Short-read bisulfite sequencing and long-read PacBio sequencing have inherent limitations to detect DNA modifications. Here, using raw electric signals of Oxford Nanopore long-read sequencing data, we design DeepMod, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) to detect DNA modifications. We sequence a human genome HX1 and a Chlamydomonas reinhardtii genome using Nanopore sequencing, and then evaluate DeepMod on three types of genomes (Escherichia coli, Chlamydomonas reinhardtii and human genomes). For 5mC detection, DeepMod achieves average precision up to 0.99 for both synthetically introduced and naturally occurring modifications. For 6mA detection, DeepMod achieves ~0.9 average precision on Escherichia coli data, and have improved performance than existing methods on Chlamydomonas reinhardtii data. In conclusion, DeepMod performs well for genome-scale detection of DNA modifications and will facilitate epigenetic analysis on diverse species.

Date: 2019
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DOI: 10.1038/s41467-019-10168-2

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