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Updated: Jun 15, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

长期阅读的安普利康的De novo聚类可以改善对微生物组数据的基因学洞察力

Yan Hui¹, Dennis Sandris Nielsen², Lukasz Krych²

¹Department of Preventive Medicine, School of Public Health and Nursing, Hangzhou Normal University, Hangzhou, China.

|June 11, 2025

概括

一个名为长幅共识分析 (LACA) 的新工作流改善了使用长读序列的微生物组分析. 拉卡增强了对安普利康测序数据的遗传学准确性和表型特征.

科学领域:

微生物学微生物学
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

像牛津纳米孔技术 (ONT) 和太平洋生物科学 (PacBio) 这样的长期阅读测序技术为安普利康分析提供了优势.
目前分析长安普利康的方法,如读取分类和独特分子标识符 (UMI) 校正,在遗传学和社区分析方面存在局限性,通常需要深度测序.

研究的目的:

开发和验证一个新的工作流程,长安普利康共识分析 (LACA),用于准确的长读安普利康测序数据分析.
改进从安普利康测序数据的植物遗传分辨率和微生物群全方位的表型特征.

主要方法:

在LACA工作流中,使用基于序列不相似性的多种*de novo*集群方法来纠正错误.
用各种测序平台 (ONT R9.4.1,R10.3,R10.4.1,Duplex和PacBio CCS) 对模拟和真实的16S和16-23S rRNA amplicon数据集进行了性能评估.
纠正序列的错误率控制在ONT R9.4.1/R10.3的1%以下,ONT R10.4.1的0.2%和ONT Duplex/PacBio CCS.0.1%的0.1%.

主要成果:

在不同测序技术中,LACA 实现了较低的平均错误率.
在LACA中基于集群的校正显示了与高精度PacBio CCS数据中的UMI校正相似的性能.
在噪音较大的ONT R10.3和R9.4.1数据中,LACA的表现优于UMI的校正,并保留了长期运行的分类学单位的家族遗传忠实性.

结论:

LACA提供了一种强大而准确的方法来分析长时间读取的片序列数据,克服现有方法的局限性.
工作流提高了微生物群落及其表型的特征,特别是在具有挑战性的数据集中.
拉卡为微生物组研究提供了一种有价值的工具,提高了遗传学准确性和社区分析.

关键词:

这就是Amplicon.这就是UniFrac的特点.德诺瓦斯 (Denoise) 是一个长阅读序列的测序.工作流的工作流.

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