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Towards inferring nanopore sequencing ionic currents from nucleotide chemical structures

Author

Listed:
  • Hongxu Ding

    (UC Santa Cruz
    UC Santa Cruz Genomics Institute)

  • Ioannis Anastopoulos

    (UC Santa Cruz
    UC Santa Cruz Genomics Institute)

  • Andrew D. Bailey

    (UC Santa Cruz
    UC Santa Cruz Genomics Institute)

  • Joshua Stuart

    (UC Santa Cruz
    UC Santa Cruz Genomics Institute)

  • Benedict Paten

    (UC Santa Cruz
    UC Santa Cruz Genomics Institute)

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.

Suggested Citation

  • Hongxu Ding & Ioannis Anastopoulos & Andrew D. Bailey & Joshua Stuart & Benedict Paten, 2021. "Towards inferring nanopore sequencing ionic currents from nucleotide chemical structures," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26929-x
    DOI: 10.1038/s41467-021-26929-x
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