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.
Keywords: CRISPR/Cas9; accurate mutant DNA detection; disease diagnosis; labeling true variants; rare mutant allele; single nucleotide variant (SNV).