假定基因组污染对GTDB分类学的影响最小
Aaron J Mussig1, Pierre-Alain Chaumeil1, Maria Chuvochina1
1The University of Queensland, School of Chemistry and Molecular Biosciences, Australian Centre for Ecogenomics, St Lucia, QLD, Australia.
Microbial genomics
|May 29, 2024
概括
基因组分类数据库 (GTDB) 的分类尽管存在潜在的污染,但仍然稳健. 分析表明,即使在删除了受污染的基因组部分后,分类学赋值也基本上不受影响,这证实了GTDBDB的存在.
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:
- 基因组分类数据库 (GTDB) 是用于分类微生物基因组的广泛采用的资源.
- GTDB使用平均核酸标识 (ANI) 进行物种划分和更高种类的遗传学分析.
- 有关潜在的基因组污染对GTDB分类准确性的影响存在担忧.
研究的目的:
- 为了在基因组分类数据库 (GTDB) 中识别可能被污染的基因组,发布07-RS207.7.
- 评估GTDB对检测到的基因组污染的分类学分类的稳定性.
- 为了评估将被污染的基因组部分去除对它们分配的分类学的影响.
主要方法:
- 利用基因组UNClutterer (GUNC) 软件在GTDB发布07-RS207.7中识别疑似受污染的基因组.
- 通过删除最受污染的一半,创建了受污染的细菌基因组的"清洁"版本.
- 对"干净"基因组进行重新计算的平均核酸标识 (ANI) 和连接基因类.
主要成果:
- 在GTDB发布07-RS207中,GUNC确定了35,723个基因组 (11.25%) 已被污染.
- 在去除受污染的部分后,只有77个基因组 (0.22%) 与它们的原始GTDB分类不一致.
- GTDB中的分类学划分证明与GUNC检测到的污染水平保持一致.
结论:
- 基因组分类数据库 (GTDB) 的分类是可靠的,尽管存在一些假定受污染的基因组.
- 用于GTDB的分类学分配的方法能够抵御轻微污染.
- 进一步完善污染检测和清除策略可以增强GTDB资源.
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