Barcode-free multiplex plasmid sequencing using Bayesian analysis and nanopore sequencing.

Masaaki Uematsu, Jeremy M Baskin
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Abstract

Plasmid construction is central to life science research, and sequence verification is arguably its costliest step. Long-read sequencing has emerged as a competitor to Sanger sequencing, with the principal benefit that whole plasmids can be sequenced in a single run. Nevertheless, the current cost of nanopore sequencing is still prohibitive for routine sequencing during plasmid construction. We develop a computational approach termed Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You (SAVEMONEY) that guides researchers to mix multiple plasmids and subsequently computationally de-mixes the resultant sequences. SAVEMONEY defines optimal mixtures in a pre-survey step, and following sequencing, executes a post-analysis workflow involving sequence classification, alignment, and consensus determination. By using Bayesian analysis with prior probability of expected plasmid construction error rate, high-confidence sequences can be obtained for each plasmid in the mixture. Plasmids differing by as little as two bases can be mixed for submission as a single sample for nanopore sequencing, and routine multiplexing of even six plasmids per 180 reads can still maintain high accuracy of consensus sequencing. SAVEMONEY should further democratize whole-plasmid sequencing by nanopore and related technologies, driving down the effective cost of whole-plasmid sequencing to lower than that of a single Sanger sequencing run.

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使用贝叶斯分析和纳米孔测序的无条形码多重质粒测序。
质粒构建是生命科学研究的核心,序列验证可以说是其成本最高的一步。长读测序已经成为桑格测序的竞争对手,其主要优点是可以在一次运行中对整个质粒进行测序。然而,目前纳米孔测序的成本对于质粒构建过程中的常规测序来说仍然是令人望而却步的。我们开发了一种称为“牛津纳米孔实验高效复用的简单算法”(SAVEMONEY)的计算方法,该方法指导研究人员混合多个质粒,然后对所得序列进行计算去混合。SAVEMONEY在预调查步骤中定义了最佳混合物,随后进行测序,执行涉及序列分类、比对和一致性确定的后分析工作流程。通过使用具有预期质粒构建错误率的先验概率的贝叶斯分析,可以获得混合物中每个质粒的高置信度序列。相差两个碱基的质粒可以混合作为单个样本进行纳米孔测序,即使是六个质粒的常规多路复用也可以保持一致测序的高精度。SAVEMONEY应该通过纳米孔和相关技术进一步民主化全质粒测序,将全质粒测序的有效成本降低到低于单次Sanger测序的成本。
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