Yuan Qin
Tong Chen
Meiping Lu
Xubo Qian
Xiaoxuan Guo
Yang Bai
1 State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China;
2 CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China;
3 CAS-JIC Centre of Excellence for Plant and Microbial Science, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China;
4 College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;
5 National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China;
6 Department of Rheumatology Immunology&Allergy, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310053, China
Funds: This work was supported by grants from the Strategic Priority Research Program of the Chinese Academy of Sciences (Precision Seed Design and Breeding, XDA24020104), the Key Research Program of Frontier Sciences of the Chinese Academy of Science (grant nos. QYZDB-SSW-SMC021), the National Natural Science Foundation of China (grant nos. 31772400).
Received Date: 2020-02-04
Rev Recd Date:2020-04-10
Abstract
Abstract
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 betadiversity, taxonomic composition, difference comparisons, correlation, networks, machine learning, evolution, source tracing, and common visualization styles to help researchers make informed choices. Finally, a stepby-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.Keywords: metagenome,
marker genes,
highthroughput sequencing,
pipeline,
reproducible analysis,
visualization
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