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Towards inferring nanopore sequencing ionic currents from nucleotide chemical structures.

Hongxu Ding1,2, Ioannis Anastopoulos3,4, Andrew D Bailey3,4

  • 1Department of Biomolecular Engineering, UC Santa Cruz, Santa Cruz, CA, USA. hding16@ucsc.edu.

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We developed a deep learning model to predict ionic currents from DNA kmer chemical structures. This framework accurately detects nucleotide modifications like 5-methylcytosine in nanopore sequencing data.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Nanopore sequencing relies on characteristic ionic currents of nucleotide kmers for readouts.
  • Predicting these ionic currents from chemical structures is crucial for data analysis.
  • Existing methods may lack the ability to generalize chemical information across different nucleotides.

Purpose of the Study:

  • To develop a deep learning framework for predicting kmer characteristic ionic currents from chemical structures.
  • To assess the framework's ability to generalize chemical information, specifically the 5-methyl group.
  • To explore the potential for de novo detection of nucleotide modifications using this approach.

Main Methods:

  • Utilized a graph convolutional network (GCN)-based deep learning framework.
  • Input: Chemical structures of nucleotide kmers.
  • Output: Predicted characteristic ionic currents.

Main Results:

  • The framework successfully predicted characteristic ionic currents from chemical structures.
  • Demonstrated generalization of 5-methyl group chemical information from thymine to cytosine.
  • Accurately predicted ionic currents for 5-methylcytosine-containing DNA 6mers.

Conclusions:

  • The GCN-based deep learning framework is effective for predicting kmer ionic currents.
  • The model shows promise for identifying nucleotide modifications, such as 5-methylcytosine, in a de novo manner.
  • This approach advances the analysis of nanopore sequencing data and the detection of epigenetic modifications.