背景:双重测序是鉴定以非常低的频率存在的序列变体的最准确的方法。它的力量来自于将原始DNA分子的两条链的多个后代汇集在一起,这允许将真正的核苷酸取代与PCR扩增和测序伪影区分开。这种策略是以成本为代价的-对同一分子进行多次测序会增加动态范围,但会显着降低覆盖率,使全基因组双链测序过于昂贵。此外,每个双重实验都会产生大量的单例读数,这些读数无法用于分析并被丢弃。
结果:在本文中,我们证明了这些读段中的很大一部分包含双链体标签内的PCR或测序错误。这种错误的校正允许将这些读段与它们各自的家族“重新结合”,从而增加该方法的输出并使其更具成本效益。
结论:我们在新版本的双工分析软件中将纠错策略与许多算法改进相结合,DuNovo2.0.它是用Python编写的,C,AWK,还有Bash.它是开源的,可以通过Galaxy随时获得,Bioconda,和Github:https://github.com/galaxyproject/dunovo。
BACKGROUND: Duplex sequencing is the most accurate approach for identification of sequence variants present at very low frequencies. Its power comes from pooling together multiple descendants of both strands of original DNA molecules, which allows distinguishing true nucleotide substitutions from PCR amplification and sequencing artifacts. This strategy comes at a cost-sequencing the same molecule multiple times increases dynamic range but significantly diminishes coverage, making whole genome duplex sequencing prohibitively expensive. Furthermore, every duplex experiment produces a substantial proportion of singleton reads that cannot be used in the analysis and are thrown away.
RESULTS: In this paper we demonstrate that a significant fraction of these reads contains PCR or sequencing errors within duplex tags. Correction of such errors allows \"reuniting\" these reads with their respective families increasing the output of the method and making it more cost effective.
CONCLUSIONS: We combine an error correction strategy with a number of algorithmic improvements in a new version of the duplex analysis software, Du Novo 2.0. It is written in Python, C, AWK, and Bash. It is open source and readily available through Galaxy, Bioconda, and Github: https://github.com/galaxyproject/dunovo.