Abstract

In the present study, we demonstrate a tunneling nanogap technique to identify individual RNA nucleotides, which can be used as a mechanism to read the nucleobases for direct sequencing of RNA in a solid-state nanopore. The tunneling nanogap is composed of two electrodes separated by a distance of < 3 nanometers and functionalized with a recognition molecule. When a chemical entity is captured in the gap, it generates electron tunneling currents, a process we call recognition tunneling (RT). Using RT nanogaps created in a scanning tunneling microscope (STM), we acquired the electron tunneling signals for the canonical and two modified RNA nucleotides. To call the individual RNA nucleotides from the RT data, we adopted a machine learning algorithm, support vector machine (SVM), for the data analysis. Through the SVM, we were able to identify the individual RNA nucleotides and distinguish them from their DNA counterparts with reasonably high accuracy. Since each RNA nucleoside contains a hydroxyl group at the 2'-position of its sugar ring in an RNA strand, it allows for the formation of a tunneling junction at a larger nanogap compared to the DNA nucleoside in a DNA strand, which lacks the 2' hydroxyl group. It also proves advantageous for the manufacture of RT devices. This study is a proof-of-principle demonstration for the development of an RT nanopore device for directly sequencing single RNA molecules, including those bearing modifications.

Original languageEnglish (US)
JournalACS Nano
DOIs
StateAccepted/In press - Apr 15 2018

Fingerprint

machine learning
nucleotides
Nucleotides
RNA
Learning systems
Identification (control systems)
DNA
Electron tunneling
Nanopores
nucleosides
sequencing
deoxyribonucleic acid
Nucleosides
Hydroxyl Radical
electron tunneling
Support vector machines
strands
Bearings (structural)
Molecules
Sugars

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)
  • Physics and Astronomy(all)

Cite this

Recognition Tunneling of Canonical and Modified RNA Nucleotides for Their Identification with the Aid of Machine Learning. / Im, Jong One; Sen, Suman; Lindsay, Stuart; Zhang, Peiming.

In: ACS Nano, 15.04.2018.

Research output: Contribution to journalArticle

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