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短读和长读全基因组测序用于SARS-CoV-2变种识别

Mengfei Peng^1,2, Morgan L Davis¹, Meghan L Bentz¹

¹Division of Core Laboratory Services and Response, Office of Laboratory Systems and Response, Centers for Disease Control & Prevention, Atlanta, GA 30329, USA.

|April 26, 2025

概括

对SARS-CoV-2的基因组监测需要有效的测序. 这项研究对Illumina NovaSeq,牛津纳米孔和PacBio Sequel II进行了基准测试,发现NovaSeq优越,但所有平台都有能力进行质量控制.

科学领域:

基因组学就是基因组学.
病毒学病毒学
生物信息学是一种生物信息学.

背景情况:

对SARS-CoV-2的基因组监测对于跟踪变种和指导公共卫生干预至关重要.
多种测序技术,包括短读 (Illumina NovaSeq) 和长读 (Oxford Nanopore MinION,PacBio Sequel II),用于全基因组测序.

研究的目的:

为了对Illumina NovaSeq,牛津纳米孔MinION和PacBio Sequel II平台的性能进行基准和比较,用于SARS-CoV-2变种识别和血统分配.
评估生物信息学工具对不同平台数据质量和分析结果的测序影响.

主要方法:

这是一项跨平台的研究,利用成熟的生物信息学工具来分析由Illumina NovaSeq,Oxford Nanopore MinION和PacBio Sequel II.生成的SARS-CoV-2全基因组测序数据.
测序指标的评估,包括读取产量,基数,覆盖深度,基因组完整性,映射稳定性和谱系分配准确性.

主要成果:

Illumina NovaSeq在读数量,覆盖深度,基因组完整性和一致的谱系分配方面表现出卓越的性能.
长读平台 (MinION,Sequel II) 的收益率和覆盖率较低,最初限制了变种识别和谱系分配.
实施严格的质量控制措施使得在所有三种测序平台上实现了一致的SARS-CoV-2血统分配.

结论:

虽然NovaSeq提供了优势,但优化质量控制可以在短读和长读平台上实现可靠的SARS-CoV-2变种和谱系分析.
预计长期阅读测序技术的进步将改善数据质量和基因组覆盖率,增强基因组监测能力.
结合短读和长读数据的混合方法有可能改善SARS-CoV-2基因组监测,并可以为其他RNA病毒的策略提供信息.

关键词:

这就是SARS-CoV-2病毒.基因组覆盖范围的覆盖范围基因组监测对基因组进行监测.血统谱系血统谱系血统谱系测序深度的测序测序深度的测序变种变种的变种进行全基因组测序.

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