Improvement of Molecular Diagnosis Using Domain-Level Single-Nucleotide Variants by Eliminating Unexpected Secondary Structures.

Abstract

Identification of single-nucleotide variants (SNVs) is of great significance in molecular diagnosis. The problem that should not be ignored in the identification process is that the unexpected secondary structure of the target nucleic acid may greatly affect the detection accuracy. Herein, we proposed a conditional domain-level SNV diagnosis strategy, in which the subsequent SNV detection can only be carried out after eliminating the unexpected secondary structure of target DNA. Specifically, the target DNA is assembled into a rigid double strand, which makes folding the target DNA difficult and the unexpected secondary structure is eliminated. Based on this double-stranded structure, specially designed probes are used to detect double-stranded properties and report abundant domain-level oligonucleotide information to improve the effective information in the detection results and complete domain-level SNV diagnosis. If the unexpected secondary structure is not eliminated, the detector will first detect it and feed back to us, ensuring the accuracy of the subsequent detection results. With the occurrence (or not) of SNV and the change of the SNV site, in the proof-of-concept experiment, we successfully identified the four homologous sequences to be tested related to BRAF gene.