相关实验视频
Updated: Sep 14, 2025

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Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
Published on: August 29, 2014
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纳米孔全长16S rRNA基因测序增加了细菌生物标志物发现中的物种分辨率
Pablo Aja-Macaya1,2, Kelly Conde-Pérez1, Noelia Trigo-Tasende1,3
1Grupo de Investigación en Microbiología, Instituto de Investigación Biomédica de A Coruña (INIBIC), Complejo Hospitalario Universitario de A Coruña (CHUAC), Sergas, Universidade de A Coruña (UDC), As Xubias, 15006, A Coruña, Galicia, Spain.
Scientific reports
|July 21, 2025
概括
牛津的纳米孔
科学领域:
- 微生物组研究的研究.
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 16S rRNA基因V3V4区域的短读测序通常会产生属级细菌识别.
- 第三代测序,就像牛津纳米孔技术 (ONT) R10.4.1一样,可以实现全长的16SrRNA基因 (V1-V9) 测序,以实现物种级别的分辨率.
- 准确的细菌生物标志物发现对于早期发现疾病至关重要,例如结肠直肠癌.
研究的目的:
- 为了比较短读 (Illumina) 和全长 (ONT) 16S rRNA基因测序用于细菌生物标志物发现.
- 评估ONT基调模型和数据库对分类识别和结直肠癌生物标志物发现的影响.
- 评估基于ONT的物种级别识别对增强微生物组分析的有用性.
主要方法:
- 从123名受试者的便样本使用Illumina-V3V4 (DADA2,QIIME2) 和ONT-V1V9 (Emu) 测序进行了分析.
- 在多个多拉多基调模型 (fast, hac, sup) 和数据库 (SILVA, Emu Default) 中进行了ONT测序.
- 机器学习模型被用来根据已识别的细菌物种预测结直肠癌.
主要成果:
- 使用R10.4.1化学的ONT测序实现了精确的物种级别识别,在生物标志物特异性方面表现优于Illumina.
- ONT确定了一些关键的结直肠癌生物标志物,包括Parvimonas micra,Fusobacterium nucleatum和Bacteroides fragilis.
- 机器学习模型预测了结直肠癌,使用14种物种的AUC为0.87,使用4种关键物种的AUC为0.82.
结论:
- 通过ONT R10.4.1化学的全长16SrRNA测序显著提高了物种级细菌识别.
- 与短读方法相比,这种方法有助于发现更精确的与疾病相关的生物标志物.
- ONT技术提高了分类学准确性,为诊断和研究提供了改进的微生物组分析.
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