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LFMD: detecting low-frequency mutations in high-depth genome sequencing data without molecular tags

By Rui Ye, Xuehan Zhuang, Jie Ruan, Yanwei Qi, Yitai An, Jiaming Xu, Timothy Mak, Xiao Liu, Xiuqing Zhang, Huanming Yang, Xun Xu, Larry Baum, Chao Nie, Pak C Sham

Posted 24 Apr 2019
bioRxiv DOI: 10.1101/617381

As next-generation sequencing (NGS) and liquid biopsy become more prevalent in research and in the clinic, there is an increasing need for better methods to reduce cost and improve sensitivity and specificity of low-frequency mutation detection (where the Alternative Allele Frequency, or AAF, is less than 1%). Here we propose a likelihood-based approach, called Low-Frequency Mutation Detector (LFMD), which combines the advantages of duplex sequencing (DS) and the bottleneck sequencing system (BotSeqS) to maximize the utilization of duplicate reads. Compared with the existing state-of-the-art methods, DS, Du Novo, UMI-tools, and Unified Consensus Maker, our method achieves higher sensitivity, higher specificity (< 4 * 10^-10 errors per base sequenced) and lower cost (reduced by ~70% at best) without involving additional experimental steps, customized adapters or molecular tags. LFMD is useful in areas where high precision is required, such as drug resistance prediction and cancer screening. As an example of LFMD's applications, mitochondrial heterogeneity analysis of 28 human brain samples across different stages of Alzheimer's Disease (AD) showed that the canonical oxidative damage related mutations, C:G>A:T, are significantly increased in the mid-stage group. This is consistent with the Mitochondrial Free Radical Theory of Aging, suggesting that AD may be linked to the aging of brain cells induced by oxidative damage.

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