使用环境样本的高通量测序数据识别菌
Samuel Holden1,2, Sang Hu Kim3, Wen Chen4
1Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada. Samuel.holden@ubc.ca.
Methods in molecular biology (Clifton, N.J.)
|April 8, 2025
概括
本研究介绍了在环境样本数据的测序中识别和分类微生物的in silico方法,这对于准确的植物相关微生物研究至关重要. 这些质量控制步骤确保了下游分析的数据完整性,特别是在复杂的现场样本 (如菌) 中.
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.
- 植物病理学 植物病理学
背景情况:
- 环境样本包含多样化的微生物群落,这给有针对性的研究带来了挑战.
- 维护植物病原体的菌培养物,如菌,需要严格的实验室协议.
- 下一代测序 (NGS) 产生了大型数据集,通常包含非目标遗传物质.
研究的目的:
- 详细介绍在测序数据中识别和分类有机体的in silico方法.
- 为分析环境测序数据提供基本的质量控制 (QC) 步骤.
- 解决分析与植物相关的微生物群落,特别是生真菌的挑战.
主要方法:
- 测序数据的分析.
- 下一代测序读数的质量控制和过.
- 微生物遗传物质的生物信息分类.
主要成果:
- 开发和应用用于样本识别和分类的计算方法.
- 证明了在环境测序数据中过非目标读数的重要性.
- 突出了假设目标生物只在测序数据集中的潜在陷.
结论:
- 在环境样本中准确分析微生物群体时,in silico方法至关重要.
- 实施质量控制步骤可确保植物相关微生物研究的测序数据的可靠性.
- 这些方法是广泛适用的,但可能需要特定物种的调整.
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