微生物群
基因组
计算机科学
数据科学
可视化
生物
计算生物学
软件
数据可视化
领域(数学)
追踪
人类微生物组计划
人体微生物群
数据挖掘
生物信息学
基因
操作系统
生物化学
程序设计语言
纯数学
数学
作者
Yong‐Xin Liu,Yuan Qin,Tong Chen,Meiping Lu,Xubo Qian,Xiaoxuan Guo,Yang Bai
出处
期刊:Protein & Cell
[Springer Nature]
日期:2020-05-11
卷期号:12 (5): 315-330
被引量:389
标识
DOI:10.1007/s13238-020-00724-8
摘要
Advances in high-throughput sequencing (HTS) have fostered rapid developments in the field of microbiome research, and massive microbiome datasets are now being generated. However, the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field. Here, we systematically summarize the advantages and limitations of microbiome methods. Then, we recommend specific pipelines for amplicon and metagenomic analyses, and describe commonly-used software and databases, to help researchers select the appropriate tools. Furthermore, we introduce statistical and visualization methods suitable for microbiome analysis, including alpha- and beta-diversity, taxonomic composition, difference comparisons, correlation, networks, machine learning, evolution, source tracing, and common visualization styles to help researchers make informed choices. Finally, a step-by-step reproducible analysis guide is introduced. We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the biological significance behind the data.
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