Sequencing artifacts derived from a library preparation method using enzymatic fragmentation

DNA fragmentation is a fundamental step during library preparation in hybridization capture-based, short-read sequencing. Ultra-sonication has been used thus far to prepare DNA of an appropriate size, but this method is associated with a considerable loss of DNA sample. More recently, studies have employed library preparation methods that rely on enzymatic fragmentation with DNA endonucleases to minimize DNA loss, particularly in nano-quantity samples. Yet, despite their wide use, the effect of enzymatic fragmentation on the resultant sequences has not been carefully assessed. Here, we used pairwise comparisons of somatic variants of the same tumor DNA samples prepared using ultrasonic and enzymatic fragmentation methods. Our analysis revealed a substantially larger number of recurrent artifactual SNVs/indels in endonuclease-treated libraries as compared with those created through ultrasonication. These artifacts were marked by palindromic structure in the genomic context, positional bias in sequenced reads, and multi-nucleotide substitutions. Taking advantage of these distinctive features, we developed a filtering algorithm to distinguish genuine somatic mutations from artifactual noise with high specificity and sensitivity. Noise cancelling recovered the composition of the mutational signatures in the tumor samples. Thus, we provide an informatics algorithm as a solution to the sequencing errors produced as a consequence of endonuclease-mediated fragmentation, highlighted for the first time in this study.DNA片段化（DNA fragmentation）是基于杂交捕获的短读长测序（short-read sequencing）建库流程中的核心步骤。目前业界常规采用超声破碎法制备符合片段长度要求的DNA样品，但该方法会造成大量DNA样品损失。近年来，多项研究采用依赖DNA核酸内切酶的酶切片段化建库方法，以最大限度减少DNA样品损失，该方法尤其适用于纳升级微量样品。尽管此类酶切建库方法应用广泛，但其对最终测序序列的影响尚未得到系统细致的评估。本研究对采用超声破碎法与酶切片段化法制备的同一批肿瘤DNA样品的体细胞变异（somatic variants）进行了成对比较分析。结果显示，与超声破碎法制备的文库相比，经核酸内切酶处理的文库中，反复出现的人工伪影单核苷酸变异（SNVs）/插入缺失（indels）数量显著更多。这些人工伪影具有三大典型特征：基因组区域存在回文结构、测序读段存在位置偏好性，以及存在多核苷酸替换现象。基于这些独特的特征，我们开发了一款过滤算法，可实现高特异性、高灵敏度地区分真实体细胞突变与人工伪影噪声。通过噪声去除步骤，我们成功还原了肿瘤样本的突变特征（mutational signatures）组成。综上，本研究首次报道了核酸内切酶介导的片段化所引发的测序误差，并提供了一款信息学算法作为该问题的解决方案。