双胞胎:从有限样本的元基因组数据中推断出致病性微生物特征
Qingren Meng1, Qian Zhou2, Shuo Shi3
1CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China; National Clinical Research Center for Infectious Diseases, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518100, China.
Science bulletin
|November 2, 2023
概括
在元基因组学中,因果推理由于混因素而具有挑战性. 虚拟双胞胎 (VTwins) 方法模仿双胞胎研究以匹配样本,有效消除混因素,并改善高维欧米数据中的因果关系检测.
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.
- 计算生物学 计算生物学
背景情况:
- 高维度的元基因组数据在推断因果关系方面存在挑战,因为有许多混因素.
- 现有的方法很难有效地从复杂的组成数据集中分离因果关系.
研究的目的:
- 开发一种新的方法,虚拟双胞胎 (VTwins),用于在高维度元基因组数据中进行强大的因果推断.
- 提高灵敏度和减少样本大小的要求,以识别与疾病相关的微生物和途径.
主要方法:
- VTwins方法将原始队列转换为"双胞胎"样本的配对队列.
- 样本与分类学配置文件相匹配,同时表现出不同的表型,有效地消除了混效应.
- 这种方法受到遗传研究中的双胞胎研究的启发.
主要成果:
- 与传统方法相比,VTwins在识别致病特征方面表现出更高的灵敏度.
- 该方法需要将样本大小减少10倍,以回忆与疾病相关的微生物或途径.
- 与其他16个软件包进行的基准测试验证了VTwins的功率和适用性.
结论:
- VTwins是一种简单有效的方法,用于处理高多样性,高维度的组合数据.
- 这种方法对矿业因果关系在元基因组和其他基因组数据中显示出显著的希望.
- VTwins是开放式的,可用于更广泛的研究应用.
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