The best practice for microbiome analysis using R.

Tao WEN; Guoqing NIU; Tong CHEN; Qirong SHEN; Jun YUAN; Yong-Xin LIU

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The best practice for microbiome analysis using R.

Author: Tao WEN ¹ ; Guoqing NIU ² ; Tong CHEN ³ ; Qirong SHEN ² ; Jun YUAN ² ; Yong-Xin LIU ¹
Author Information

1. Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China.
2. The Key Laboratory of Plant Immunity Jiangsu Provincial Key Lab for Organic Solid Waste Utilization Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing 210095, China.
3. National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China.
Publication Type:Journal Article
Keywords: R package; amplicon; data analysis; metagenome; microbiome; visualization
MeSH: Software; Microbiota; Sequence Analysis, DNA; Language
From: Protein & Cell 2023;14(10):713-725
CountryChina
Language:English
Abstract: With the gradual maturity of sequencing technology, many microbiome studies have published, driving the emergence and advance of related analysis tools. R language is the widely used platform for microbiome data analysis for powerful functions. However, tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data. How to choose suitable, efficient, convenient, and easy-to-learn tools from the numerous R packages has become a problem for many microbiome researchers. We have organized 324 common R packages for microbiome analysis and classified them according to application categories (diversity, difference, biomarker, correlation and network, functional prediction, and others), which could help researchers quickly find relevant R packages for microbiome analysis. Furthermore, we systematically sorted the integrated R packages (phyloseq, microbiome, MicrobiomeAnalystR, Animalcules, microeco, and amplicon) for microbiome analysis, and summarized the advantages and limitations, which will help researchers choose the appropriate tools. Finally, we thoroughly reviewed the R packages for microbiome analysis, summarized most of the common analysis content in the microbiome, and formed the most suitable pipeline for microbiome analysis. This paper is accompanied by hundreds of examples with 10,000 lines codes in GitHub, which can help beginners to learn, also help analysts compare and test different tools. This paper systematically sorts the application of R in microbiome, providing an important theoretical basis and practical reference for the development of better microbiome tools in the future. All the code is available at GitHub github.com/taowenmicro/EasyMicrobiomeR.