LFMD: a new likelihood-based method to detect low-frequency mutations without molecular tags

Abstract

As next generation sequencing (NGS) and liquid biopsy become more prevalent in clinical and research area, especially cancer diagnosis, targeted therapy guidance and disease surveillance, there is an increasing need for better methods to reduce cost and to improve sensitivity and specificity. Since the error rate of NGS is around 1%, it is difficult to identify mutations with frequency lower than 1% accurately and efficiently because of low Signal-to- Noise Ratio (SNR). Here we propose a likelihood-based approach, low-frequency mutation detector (LFMD), combining the advantages of duplex sequencing (DS) and bottleneck sequencing system (BotSeqS) to maximize utilization of duplicate sequenced reads. Compared with DS, the new method achieves higher sensitivity (improved ~16%), higher specificity and lower cost (reduced ~70%) without involving additional experimental steps, customized adapters and molecular tags. In addition, this method can also be used to improve sensitivity and specificity of other variant calling algorithms by replacing a step in traditional NGS analysis: removing polymerase chain reaction (PCR) duplication. Thus, LFMD can be a promising method used in genomic research and clinical fields.