Discriminating Single Nucleotide Variations in Solid-State Nanopores by Evaluating the Combination Efficiency between DNA Polymerase and Its Substrate.

A single nucleotide variant present between two otherwise identical nucleic acids will have unexpected functional consequences frequently. Here, a neoteric single nucleotide variation (SNV) detection assay that integrates two complementary nanotechnology systems, nanoassembly technology and an ingenious nanopore biosensing platform, has been applied to this research. Specifically, we set up a detection system to reflect the binding efficiency of the polymerase and nanoprobe through the difference of nanopore signals and then explore the effect of base mutation at the binding site. In addition, machine learning based on support vector machines is used to automatically classify characteristic events mapped by nanopore signals. Our system reliably discriminates single nucleotide variants at binding sites, even possessing the recognition among transitions, transversions, and hypoxanthine (base I). Our results demonstrate the potential of solid-state nanopore detection for SNV and provide some ideas for expanding solid-state nanopore detection platforms.