Analysis of mutational genotyping using correctable decoding sequencing with superior specificity.

Area of Science:

• Genomics
• Molecular Biology
• Biotechnology

Background:

• Accurate identification of single-nucleotide polymorphisms (SNPs) and low-abundance mutations is crucial for early disease diagnosis.
• Current high-throughput sequencing platforms face limitations in accuracy, hindering precise clinical applications.

Purpose of the Study:

• To propose a correctable decoding sequencing strategy for high-throughput platforms.
• To enhance the accuracy of mutation detection and reduce sequencing errors.

Main Methods:

• A novel cyclic sequencing approach incorporating a mixture of natural nucleotides and cyclic reversible termination (CRT) nucleotides.
• Utilizing the synthetic properties of CRTs to resolve ambiguous sequence calls through parallel sequencing runs.

Main Results:

• Achieved approximately 99.3% cycle efficiency with a theoretical error rate of 0.0009%, surpassing Sanger sequencing.
• Demonstrated effective error correction and validation of sequencing information.
• Successfully identified a mutation site in mitochondrial DNA from human cells using a single sequencing run.

Conclusions:

• The proposed correctable decoding sequencing strategy significantly improves accuracy for high-throughput sequencing.
• This method offers a reliable approach for detecting low-abundance mutations and SNPs, aiding in early clinical diagnosis.
• The strategy shows potential for broad application in genetic research and diagnostics.