DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning.

MOTIVATION: The Oxford Nanopore sequencing enables to directly detect methylation states of bases in DNA from reads without extra laboratory techniques. Novel computational methods are required to improve the accuracy and robustness of DNA methylation state prediction using Nanopore reads.

RESULTS: In this study, we develop DeepSignal, a deep learning method to detect DNA methylation states from Nanopore sequencing reads. Testing on Nanopore reads of Homo sapiens (H. sapiens), Escherichia coli (E. coli) and pUC19 shows that DeepSignal can achieve higher performance at both read level and genome level on detecting 6mA and 5mC methylation states comparing to previous HMM based methods. DeepSignal achieves similar performance cross different DNA methylation bases, different DNA methylation motifs, and both singleton and mixed DNA CpG. Moreover, DeepSignal requires much lower coverage than those required by HMM and statistics based methods. DeepSignal can achieve 90% above accuracy for detecting 5mC and 6mA using only 2x coverage of reads. Furthermore, for DNA CpG methylation state prediction, DeepSignal achieves 90% correlation with bisulfite sequencing using just 20x coverage of reads, which is much better than HMM based methods. Especially, DeepSignal can predict methylation states of 5% more DNA CpGs that previously cannot be predicted by bisulfite sequencing. DeepSignal can be a robust and accurate method for detecting methylation states of DNA bases.

AVAILABILITY: DeepSignal is publicly available at https://github.com/bioinfomaticsCSU/deepsignal.

SUPPLEMENTARY INFORMATION: Supplementary data are available at bioinformatics online.