Volume 12 Issue 5
May  2021
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Yong-Xin Liu, Yuan Qin, Tong Chen, Meiping Lu, Xubo Qian, Xiaoxuan Guo, Yang Bai. A practical guide to amplicon and metagenomic analysis of microbiome data[J]. Protein&Cell, 2021, 12(5): 315-330. doi: 10.1007/s13238-020-00724-8
Citation: Yong-Xin Liu, Yuan Qin, Tong Chen, Meiping Lu, Xubo Qian, Xiaoxuan Guo, Yang Bai. A practical guide to amplicon and metagenomic analysis of microbiome data[J]. Protein&Cell, 2021, 12(5): 315-330. doi: 10.1007/s13238-020-00724-8

A practical guide to amplicon and metagenomic analysis of microbiome data

doi: 10.1007/s13238-020-00724-8
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
  • 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.
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