The detection of low-frequency somatic mutations enables early diagnosis of disease; however, base-substitution errors that arise during genomic library preparation and high-throughput sequencing can lead to false diagnostic information. To discriminate true genomic alterations from technical errors, we developed spCas9-assisted true variant labeling sequencing (CARVE-seq), which detects low-frequency mutant alleles with high accuracy. CARVE-seq utilizes single-base discrimination during spCas9 cleavage reactions to exclude technical errors. Ten single nucleotide variants that recurrently occur in tumors were assayed by CARVE-seq using 20 ng reference samples, and 100% positive predictive value and specificity was observed, which proved the highly accurate performance of CARVE-seq.
Bibliographical noteFunding Information:
This work was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT; 2016M3A9B6948494); the Midcareer Researcher Program of the National Research Foundation (NRF) funded by the Korean government (MSIT; 2018R1A2A1A05079172); the National Research Foundation (NRF) funded by the Korean government (MSIT; 2021R1A2C2008490); the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI18C2282, HI14C1277).
© 2021 American Chemical Society.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Biochemistry, Genetics and Molecular Biology (miscellaneous)