Analysis of mutational genotyping using correctable decoding sequencing with superior specificity. Issue 2 (20th December 2022)

• Record Type: Journal Article
• Authors: Cheng, Chu; Fei, Zhongjie; Xiao, Pengfeng; Huang, Huan; Zhou, Guohua; Lu, Zuhong
• Is Part Of: Analyst. Volume 148:Issue 2(2023)
• Page Start: 402
• Page End: 411
• Publication Date: 2022-12-20
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/d2an01805e
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : A correctable decoding sequencing based on dual-mononucleotide sequencing-by-synthesis is proposed. Its error rate is lower than that of Sanger sequencing, making it promising as the most accurate high-throughput DNA sequencing approach. Abstract : The ability to accurately identify SNPs or low-abundance mutations is important for early clinical diagnosis of diseases, but the existing high-throughput sequencing platforms are limited in terms of their accuracy. Here, we propose a correctable decoding sequencing strategy that may be used for high-throughput sequencing platforms. This strategy is based on adding a mixture of two types of mononucleotides, natural nucleotide and cyclic reversible termination (CRT), for cyclic sequencing. Using the synthetic characteristic of CRTs, about 75% of the calls are unambiguous for a single sequencing run, and the remaining ambiguous sequence can be accurately deduced by two parallel sequencing runs. We demonstrate the feasibility of this strategy, and its cycle efficiency can reach approximately 99.3%. This strategy is proved to be effective for correcting errors and identifying whether the sequencing information is correct or not. And its conservative theoretical error rate was determined to be 0.0009%, which is lower than that of Sanger sequencing. In addition, we establish that the information of only a single sequencing run can be used to detect samples with known mutation sites. We apply this strategy to accurately identify a mutation site in mitochondrial DNA from human cells.