Towards inferring nanopore sequencing ionic currents from nucleotide chemical structures

Hongxu Ding (), Ioannis Anastopoulos, Andrew D. Bailey, Joshua Stuart () and Benedict Paten ()
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Hongxu Ding: UC Santa Cruz
Ioannis Anastopoulos: UC Santa Cruz
Andrew D. Bailey: UC Santa Cruz
Joshua Stuart: UC Santa Cruz
Benedict Paten: UC Santa Cruz

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26929-x

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DOI: 10.1038/s41467-021-26929-x

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