Bokulich Nicholas A, Ziemski Michal, Robeson Michael S, Kaehler Benjamin D
Laboratory of Food Systems Biotechnology, Institute of Food, Nutrition, and Health, ETH Zurich, Switzerland.
University of Arkansas for Medical Sciences, Department of Biomedical Informatics, Little Rock, AR, USA.
Comput Struct Biotechnol J. 2020 Dec 3;18:4048-4062. doi: 10.1016/j.csbj.2020.11.049. eCollection 2020.
Microbiomes are integral components of diverse ecosystems, and increasingly recognized for their roles in the health of humans, animals, plants, and other hosts. Given their complexity (both in composition and function), the effective study of microbiomes (microbiomics) relies on the development, optimization, and validation of computational methods for analyzing microbial datasets, such as from marker-gene (e.g., 16S rRNA gene) and metagenome data. This review describes best practices for benchmarking and implementing computational methods (and software) for studying microbiomes, with particular focus on unique characteristics of microbiomes and microbiomics data that should be taken into account when designing and testing microbiomics methods.
微生物群落是多样生态系统的重要组成部分,并且因其在人类、动物、植物及其他宿主健康方面的作用而日益受到认可。鉴于其复杂性(包括组成和功能方面),对微生物群落的有效研究(微生物组学)依赖于用于分析微生物数据集(例如来自标记基因,如16S rRNA基因和宏基因组数据)的计算方法的开发、优化和验证。本综述描述了用于研究微生物群落的计算方法(及软件)的基准测试和实施的最佳实践,特别关注在设计和测试微生物组学方法时应考虑的微生物群落和微生物组学数据的独特特征。