Expanding the Molecular Alphabet of DNA-Based Data Storage Systems with Neural Network Nanopore Readout Processing

S. Kasra Tabatabaei, Bach Pham, Chao Pan, Jingqian Liu, Shubham Chandak, Spencer A. Shorkey, Alvaro G. Hernandez, Aleksei Aksimentiev, Min Chen, Charles M. Schroeder, Olgica Milenkovic

Research output: Contribution to journalArticlepeer-review

Abstract

DNA is a promising next-generation data storage medium, but challenges remain with synthesis costs and recording latency. Here, we describe a prototype of a DNA data storage system that uses an extended molecular alphabet combining natural and chemically modified nucleotides. Our results show that MspA nanopores can discriminate different combinations and ordered sequences of natural and chemically modified nucleotides in custom-designed oligomers. We further demonstrate single-molecule sequencing of the extended alphabet using a neural network architecture that classifies raw current signals generated by Oxford Nanopore sequencers with an average accuracy exceeding 60% (39× larger than random guessing). Molecular dynamics simulations show that the majority of modified nucleotides lead to only minor perturbations of the DNA double helix. Overall, the extended molecular alphabet may potentially offer a nearly 2-fold increase in storage density and potentially the same order of reduction in the recording latency, thereby enabling new implementations of molecular recorders.

Original languageEnglish (US)
Pages (from-to)1905-1914
Number of pages10
JournalNano letters
Volume22
Issue number5
DOIs
StatePublished - Mar 9 2022

Keywords

  • DNA Data Storage
  • Nanopores
  • Neural Networks
  • Single-Molecule
  • Unnatural Nucleotides

ASJC Scopus subject areas

  • Bioengineering
  • Chemistry(all)
  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanical Engineering

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