Accurate human genome analysis with element avidity sequencing.

IF 3.3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Andrew Carroll, Alexey Kolesnikov, Daniel E Cook, Lucas Brambrink, Kelly N Wiseman, Sophie M Billings, Semyon Kruglyak, Bryan R Lajoie, Junhua Zhao, Shawn E Levy, Cory Y McLean, Kishwar Shafin, Maria Nattestad, Pi-Chuan Chang
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引用次数: 0

Abstract

Background: New sequencing technologies provide options for the scientific community to design studies and build clinical workflows. These options expand user choice, and can enable more accurate, scalable, or affordable workflows depending on the fit between scientist needs and platform capability. However, it is essential to understand the performance of these new technologies for different tasks, especially for capabilities that were not possible or tractable in prior technologies. We investigate the new sequencing technology avidity from Element Biosciences. to help the scientific community understand the performance of the options to generate sequencing data.

Results: We show that Element whole genome sequencing achieves higher mapping and variant calling accuracy compared to Illumina sequencing at the same coverage, with larger differences at lower coverages (20-30x). We quantify base error rates of Element reads, finding lower error rates, especially in homopolymer and tandem repeat regions. We use Element's ability to generate paired end sequencing with longer insert sizes than typical short-read sequencing. We show that longer insert sizes result in even higher accuracy, with long insert Element sequencing giving more accurate genome analyses at all coverages.

Conclusions: New options for sequencing technologies can analyze genomes comparably or better than prior standard methods.

精确的人类基因组分析与元素贪婪测序。
背景:新的测序技术为科学界设计研究和建立临床工作流程提供了选择。这些选项扩展了用户的选择,并且可以根据科学家需求和平台能力之间的契合度,实现更准确、可扩展或负担得起的工作流程。然而,理解这些新技术对于不同任务的性能是非常重要的,特别是对于在以前的技术中不可能或无法处理的功能。我们研究了来自Element Biosciences的新的测序技术。帮助科学界了解产生测序数据的选项的性能。结果:与Illumina测序相比,Element全基因组测序在相同覆盖率下获得了更高的定位和变异调用精度,在低覆盖率(20-30倍)下差异更大。我们量化了Element读取的碱基错误率,发现更低的错误率,特别是在均聚物和串联重复区域。我们使用Element的能力生成比典型短读测序更长的插入长度的成对末端测序。我们发现,更长的插入长度导致更高的准确性,长插入元件测序在所有覆盖范围内提供更准确的基因组分析。结论:测序技术的新选择可以与先前的标准方法相当或更好地分析基因组。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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