Towards inferring nanopore sequencing ionic currents from nucleotide chemical structures

ABSTRACT: 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. Nanopore sequencing allows users to identify nucleotide sequence from ionic currents. Here, the authors use deep learning to facilitate the de novo identification of modified nucleotides, particularly methylated cytosine and guanine, from the measured ionic currents without the need for controls.

SUBMITTER: Ding H 

PROVIDER: S-EPMC8586022 | biostudies-literature |

REPOSITORIES: biostudies-literature