A practical guide to amplicon and metagenomic analysis of microbiome data
By Yong-xin Liu, Yuan Qin, Tong Chen, Meiping Lu, Xubo Qian, Xiaoxuan Guo, Yang Bai. Protein & Cell, 2020
706 citations recorded by the source corpus.
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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/a10a1887bdf21b9db07f092a60a0ad82257ed4fa
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In Molecular Biology
Citations in the corpus
- ggClusterNet: An R package for microbiome network analysis and modularity‐based multiple network layouts (2022, background): “In the past two decades, the rapid development of high ‐ throughput sequencing technology has contributed to progress in microbial community studies as well as related bioinformatics tools [1].”