微生物群
人体微生物群
人类微生物组计划
计算生物学
数据科学
基因组
计算机科学
生物
生物信息学
遗传学
基因
出处
期刊:Methods in molecular biology
日期:2023-01-01
卷期号:: 183-229
被引量:2
标识
DOI:10.1007/978-1-0716-2986-4_10
摘要
Since advances in next-generation sequencing (NGS) technique enabled to investigate uncultured microbiota and their genomes in unbiased manner, many microbiome researches have been reporting strong evidences for close links of microbiome to human health and disease. Bioinformatic and statistical analysis of NGS-based microbiome data are essential components in those microbiome researches to explore the complex composition of microbial community and understand the functions of community members in relation to host and environment. This chapter introduces bioinformatic analysis methods that generate taxonomy and functional feature count table along with phylogenetic tree from raw NGS microbiome data and then introduce statistical methods and machine learning approaches for analyzing the outputs of the bioinformatic analysis to infer the biodiversity of a microbial community and unravel host-microbiome association. Understanding the advantages and limitations of the analysis methods will help readers use the methods correctly in microbiome data analysis and may give a new opportunity to develop new analytic techniques for microbiome research.
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