Author:
Shen Hanjie,Liu Pengjuan,Li Zhanqing,Chen Fang,Jiang Hui,Shi Shiming,Xi Yang,Li Qiaoling,Wang Xiaojue,Zhao Jing,Liang Xinming,Xie Yinlong,Wang Lin,Tian Wenlan,Berntsen Tam,Alexeev Andrei,Luo Yinling,Gong Meihua,Li Jiguang,Xu Chongjun,Barua Nina,Drmanac Snezana,Dai Sijie,Mi Zilan,Ren Han,Lin Zhe,Chen Ao,Zhang Wenwei,Mu Feng,Xu Xun,Zhao Xia,Jiang Yuan,Drmanac Radoje
Abstract
AbstractBackgroundSystematic errors can be introduced from DNA amplification during massively parallel sequencing (MPS) library preparation and sequencing array formation. Polymerase chain reaction (PCR)-free genomic library preparation methods were previously shown to improve whole genome sequencing (WGS) quality on the Illumina platform, especially in calling insertions and deletions (InDels). We hypothesized that substantial InDel errors continue to be introduced by the remaining PCR step of DNA cluster generation. In addition to library preparation and sequencing, data analysis methods are also important for the accuracy of the output data.In recent years, several machine learning variant calling pipelines have emerged, which can correct the systematic errors from MPS and improve the data performance of variant calling.ResultsHere, PCR-free libraries were sequenced on the PCR-free DNBSEQ™ arrays from MGI Tech Co., Ltd. (referred to as MGI) to accomplish the first true PCR-free WGS which the whole process is truly not only PCR-free during library preparation but also PCR-free during sequencing. We demonstrated that PCR-based WGS libraries have significantly (about 5 times) more InDel errors than PCR-free libraries.Furthermore, PCR-free WGS libraries sequenced on the PCR-free DNBSEQ™ platform have up to 55% less InDel errors compared to the NovaSeq platform, confirming that DNA clusters contain PCR-generated errors.In addition, low coverage bias and less than 1% read duplication rate was reproducibly obtained in DNBSEQ™ PCR-free using either ultrasonic or enzymatic DNA fragmentation MGI kits combined with MGISEQ-2000. Meanwhile, variant calling performance (single-nucleotide polymorphisms (SNPs) F-score>99.94%, InDels F-score>99.6%) exceeded widely accepted standards using machine learning (ML) methods (DeepVariant or DNAscope).ConclusionsEnabled by the new PCR-free library preparation kits, ultra high-thoughput PCR-free sequencers and ML-based variant calling, true PCR-free DNBSEQ™ WGS provides a powerful solution for improving WGS accuracy while reducing cost and analysis time, thus facilitating future precision medicine, cohort studies, and large population genome projects.
Publisher
Cold Spring Harbor Laboratory