Authors: Hongxu Ding, Ioannis Anastopoulos, Andrew D. Bailey, Joshua Stuart, Benedict Paten
Published: 2021-11-11
DOI: 10.1038/s41467-021-26929-x
Source: Full article
AbstractThe characteristic ionic currents of nucleotide kmers are commonly used in analyzing nanopore sequencing readouts. We present a graph convolutional network-based deep learning framework for predicting kmer characteristic ionic currents from corresponding chemical structures. We show such a framework can generalize the chemical information of the 5-methyl group from thymine to cytosine by correctly predicting 5-methylcytosine-containing DNA 6mers, thus shedding light on the de novo detection of nucleotide modifications.