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Reliable Multiplex Sequencing with Rare Index Mis-Assignment on DNB-Based NGS Platform

By Qiaoling Li, Xia Zhao, Wenwei Zhang, Lin Wang, Jingjing Wang, Dongyang Xu, Zhiying Mei, Qiang Liu, Shiyi Du, Zhanqing Li, Xinming Liang, Xiaman Wang, Hanmin Wei, Pengjuan Liu, Jing Zou, Hanjie Shen, Ao Chen, Snezana Drmanac, Jia Sophie Liu, Li Li, Hui Jiang, Yongwei Zhang, Jian Wang, Huanming Yang, Xun Xu, Radoje Drmanac, Yuan Jiang

Posted 11 Jun 2018
bioRxiv DOI: 10.1101/343137 (published DOI: 10.1186/s12864-019-5569-5)

Accurate next generation sequencing (NGS) is critical for understanding genetic predisposition to human disease and thus aiding clinical diagnosis and personalized precision medicine. Recent breakthroughs in massively parallel sequencing, especially when coupled with sample multiplexing, have driven sequencing cost down and made clinical genetic tests broadly affordable. However, intractable index mis-assignment (commonly exceeds 1%) has been reported on some widely used sequencing platforms. Burdensome unique dual indexing is now used to reduce this problem. Here, we investigated this quality issue on BGI sequencers using three major library preparation methods: whole genome sequencing (WGS) with PCR, PCR-free WGS, and two-step targeted PCR. BGI sequencers utilize a unique DNA nanoball (DNB) technology that is based on rolling circle replication (RCR) for array preparation; this linear amplification is PCR free and can avoid error accumulation. We demonstrate here that single index mis-assignment from free indexed oligos on these sequencers occurs at a rate of only one in 36 million reads, suggesting virtually no index hopping during DNB creation and arraying, as expected for the RCR process. Furthermore, the DNB-based NGS applications have achieved an unprecedentedly low sample-to-sample mis-assignment rate of 0.0001% to 0.0004% using only single indexing. Therefore, single indexing with DNB sequencing technology provides a simple but effective method for sensitive research and clinical genetic assays that require the detection of low abundance sequences in a large number of samples.

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