Microbial community analysis using high-throughput sequencing technology: a beginner's guide for microbiologists

被引:37
|
作者
Jo, Jihoon [1 ]
Oh, Jooseong [1 ]
Park, Chungoo [1 ]
机构
[1] Chonnam Natl Univ, Sch Biol Sci & Technol, Gwangju 61186, South Korea
关键词
microbiome; environmental DNA; metagenomics; shotgun metagenomic sequencing; bioinformatics; DE-NOVO ASSEMBLER; PHYLOGENETIC CLASSIFICATION; SHOTGUN METAGENOMICS; GENOME SHOTGUN; DIVERSITY; BACTERIAL; MICROARRAY; INSIGHTS; REVEALS; ECOLOGY;
D O I
10.1007/s12275-020-9525-5
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
Microbial communities present in diverse environments from deep seas to human body niches play significant roles in the complex ecosystem and human health. Characterizing their structural and functional diversities is indispensable, and many approaches, such as microscopic observation, DNA fingerprinting, and PCR-based marker gene analysis, have been successfully applied to identify microorganisms. Since the revolutionary improvement of DNA sequencing technologies, direct and high-throughput analysis of genomic DNA from a whole environmental community without prior cultivation has become the mainstream approach, overcoming the constraints of the classical approaches. Here, we first briefly review the history of environmental DNA analysis applications with a focus on profiling the taxonomic composition and functional potentials of microbial communities. To this end, we aim to introduce the shotgun metagenomic sequencing (SMS) approach, which is used for the untargeted ("shotgun") sequencing of all ("meta") microbial genomes ("genomic") present in a sample. SMS data analyses are performed in silico using various software programs; however, in silico analysis is typically regarded as a burden on wet-lab experimental microbiologists. Therefore, in this review, we present microbiologists who are unfamiliar with in silico analyses with a basic and practical SMS data analysis protocol. This protocol covers all the bioinformatics processes of the SMS analysis in terms of data preprocessing, taxonomic profiling, functional annotation, and visualization.
引用
收藏
页码:176 / 192
页数:17
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