Answers are grounded in this paper and cite their sources.
Detection of de novo , low-frequency mutations is essential for characterizing cancer genomes and heterogeneous cell populations. However, the screening capacity of current ultrasensitive NGS methods is inadequate owing to either low-efficiency read utilization or severe amplification bias. Here, we present o2n-seq, an ultrasensitive and high-efficiency NGS library preparation method for discovering de novo , low-frequency mutations. O2n-seq reduces the error rate of NGS to 10 −5 –10 −8 . The efficiency of its data usage is about 10–30 times higher than that of barcode-based strategies. For detecting mutations with allele frequency (AF) 1% in 4.6 Mb-sized genome, the sensitivity and specificity of o2n-seq reach to 99% and 98.64%, respectively. For mutations with AF around 0.07% in phix174 , o2n-seq detects all the mutations with 100% specificity. Moreover, we successfully apply o2n-seq to screen de novo , low-frequency mutations in human tumours. O2n-seq will aid to characterize the landscape of somatic mutations in research and clinical settings.
No takes yet. Share an insight, caveat, or question.
Wang et al. (2017) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: