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2019 , Vol. 41 >Issue 9: 845 - 862

DOI: https://doi.org/10.16288/j.yczz.19-222

综述

微生物组数据分析方法与应用

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  • 1. 中国科学院遗传与发育生物学研究所,植物基因组学国家重点实验室,北京 100101
    2. 中国科学院遗传与发育生物学研究所,中国科学院-英国约翰英纳斯中心植物和微生物科学联合研究中心,北京 100101
    3. 中国科学院大学现代农学院,北京 100049
刘永鑫,博士,工程师,研究方向：生物信息学、宏基因组学。E-mail: yxliu@genetics.ac.cn

收稿日期: 2019-07-30

  修回日期: 2019-08-21

  网络出版日期: 2019-09-02

基金资助

中国科学院前沿科学重点研究项目编号(QYZDB-SSW-SMC021);国家自然科学基金面上项目编号(31772400);中国科学院重点部署项目资助编号(KFZD-SW-219)

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Methods and applications for microbiome data analysis

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  • 1. State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, the Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing 100101, China
    2. CAS-JIC Centre of Excellence for Plant and Microbial Science, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
    3. College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100101, China

Received date: 2019-07-30

  Revised date: 2019-08-21

  Online published: 2019-09-02

Supported by

Supported by the Key Research Program of Frontier Sciences of the Chinese Academy of Science No(QYZDB-SSW-SMC021);the National Natural Science Foundation of China No(31772400);the Key Research Program of the Chinese Academy of Sciences No(KFZD-SW-219)

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摘要

高通量测序技术的发展衍生出一系列微生物组(microbiome)研究技术,如扩增子、宏基因组、宏转录组等,快速推动了微生物组领域的发展。微生物组数据分析涉及的基础知识、软件和数据库较多,对于同领域研究者开展学习和选择合适的分析方法具有一定困难。本文系统概述了微生物组数据分析的基本思想和基础知识,详细总结比较了扩增子和宏基因组分析中的常用软件和数据库,并对高通量数据下游分析中常用的几种方法,包括统计和可视化、网络分析、进化分析、机器学习和关联分析等,从可用性、软件选择以及应用等几个方面进行了概述。本文拟通过对当前微生物组主流分析方法的整理和总结,为同领域研究者更方便、灵活的开展数据分析,快速选择研究分析工具,高效挖掘数据背后的生物学意义提供参考,进一步推动微生物组研究在生物学领域的发展。

关键词： 微生物组; 数据分析; 扩增子; 宏基因组; 分析流程

本文引用格式

刘永鑫,秦媛,郭晓璇,白洋 . 微生物组数据分析方法与应用[J]. 遗传, 2019 , 41(9) : 845 -862 . DOI: 10.16288/j.yczz.19-222

Abstract

Development of high-throughput sequencing stimulates a series of microbiome technologies, such as amplicon sequencing, metagenome, metatranscriptome, which have rapidly promoted microbiome research. Microbiome data analysis involves a lot of basic knowledge, software and databases, and it is difficult for peers to learn and select proper methods. This review systematically outlines the basic ideas of microbiome data analysis and the basic knowledge required to conduct analysis. In addition, it summarizes the advantages and disadvantages of commonly used software and databases used in the comparison, visualization, network, evolution, machine learning and association analysis. This review aims to provide a convenient and flexible guide for selecting analytical tools and suitable databases for mining the biological significance of microbiome data.

Key words： microbiome; data analysis; amplicon; metagenome; pipeline

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