双联测序技术已广泛应用于循环肿瘤脱氧核糖核酸（DNA）中低频突变的检测，但如何确定测序深度和其他实验参数以确保低频突变的稳定检测仍然是一个迫切需要解决的问题。双联测序的突变检测规则不仅限制了突变模板的数量，还限制了每个正向链和反向链的突变模板对应的突变支持读数的数量。为了解决这个问题，我们提出了一种用于稳定检测双联测序中低频突变的深度估计模型（DELFMUT），它利用零截尾负二项分布模拟了模板和读数之间的恒等对应关系和数量关系，而不考虑由碱基组成的序列。DELFMUT的结果通过实际双联测序数据进行了验证。在已知突变频率和突变检测规则的情况下，DELFMUT可以推荐DNA输入和测序深度的组合，以保证突变的稳定检测，它在指导双联测序技术的实验参数设置方面具有很大的应用价值。© 2023作者。牛津大学出版社版权所有。如需获取权限，请发送电子邮件至：journals.permissions@oup.com。

Duplex sequencing technology has been widely used in the detection of low-frequency mutations in circulating tumor deoxyribonucleic acid (DNA), but how to determine the sequencing depth and other experimental parameters to ensure the stable detection of low-frequency mutations is still an urgent problem to be solved. The mutation detection rules of duplex sequencing constrain not only the number of mutated templates but also the number of mutation-supportive reads corresponding to each forward and reverse strand of the mutated templates. To tackle this problem, we proposed a Depth Estimation model for stable detection of Low-Frequency MUTations in duplex sequencing (DELFMUT), which models the identity correspondence and quantitative relationships between templates and reads using the zero-truncated negative binomial distribution without considering the sequences composed of bases. The results of DELFMUT were verified by real duplex sequencing data. In the case of known mutation frequency and mutation detection rule, DELFMUT can recommend the combinations of DNA input and sequencing depth to guarantee the stable detection of mutations, and it has a great application value in guiding the experimental parameter setting of duplex sequencing technology.© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.