Towards inferring nanopore sequencing ionic currents from nucleotide chemical structures

The 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.