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遗传  2019, Vol. 41 Issue (9): 845-862    DOI: 10.16288/j.yczz.19-222
生物学   综述 |遗传学 本期目录 | 过刊浏览 |
微生物组数据分析方法与应用
刘永鑫1,2,秦媛1,2,3,郭晓璇1,2,白洋1,2,3()
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
Methods and applications for microbiome data analysis
Liu Yongxin1,2,Qin Yuan1,2,3,Guo Xiaoxuan1,2,Bai Yang1,2,3()
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
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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    
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 wordsmicrobiome    data analysis    amplicon    metagenome    pipeline
收稿日期: 2019-07-30      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)
第一作者简介: 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
通讯作者简介: 白洋     E-mail: ybai@genetics.ac.cn
Corresponding author: Bai Yang     E-mail: ybai@genetics.ac.cn
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引用本文:

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.
Liu Yongxin,Qin Yuan,Guo Xiaoxuan,Bai Yang. Methods and applications for microbiome data analysis. Hereditas(Beijing), 2019, 41(9): 845-862.

链接本文:

http://www.chinagene.cn/CN/10.16288/j.yczz.19-222      或      http://www.chinagene.cn/CN/Y2019/V41/I9/845

图1  微生物组研究方法概述 A:微生物组常用的研究层面和对应方法。微生物组按研究层面主要分为微生物培养、DNA和mRNA等3个层面;按研究技术主要包括培养组学(culturome)、扩增子(amplicon)、宏基因组(metagenome)、宏病毒组(metavirome) 和宏转录组(metatranscriptome)等测序技术[1,12]。B:微生物组研究的基本步骤。基于测序技术为基础的微生物组研究,主要分为样本制备、测序、数据处理和统计分析4个阶段。C:微生物组数据分析的基本步骤、常用环境和思想。组学数据分析主要分3步,图中箭头上描述了实现分析的常用语言环境Shell和/或R;图中箭头下展示各步分析的目的,即通过降维和可视化的基本思想,实现将大数据转化为可读图表。
图2  近10年来微生物组领域的重要软件和算法 图中橙色为Patrick D. Schloss教授开发的分析流程mothur,绿色为Rob Knight教授主持开发的QIIME系列分析流程,蓝色显示Robert Edgar独立研究员编写的相关软件和算法。
表1 扩增子分析常用软件和数据库 Table 1 Software and databases for amplicon analysis
名称 链接 简介 参考文献
QIIME http://qiime.org/ 扩增子分析流程,功能最全、体积大、扩展性强、依赖关系多、仅限Linux或Mac系统 [19]
QIIME 2 https://qiime2.org/
https://github.com/YongxinLiu/
QIIME2ChineseManual
新一代扩增子分析流程,分析过程封装为压缩格式,支持分析过程全记录的可重现分析,开发并整合许多新算法处理大数据更快,可扩展性强和中文帮助文档 [20]
USEARCH http://www.drive5.com/usearch/ 比对工具,现发展为拥有200多个命令的扩增子分析流程,体积小巧、跨平台、计算速度快,但64位版收费,提供中文帮助文档(https://github.com/YongxinLiu/UsearchChineseManual) [25]
mothur https://www.mothur.org/ 最早的扩增子分析流程,体积小巧、跨平台 [16]
VSEARCH https://github.com/torognes/vsearch 扩增子分析流程,实现了USEARCH大部分的功能,喜欢USEARCH分析流程风格的替代软件,支持在QIIME 2中使用 [25]
Qiita https://qiita.ucsd.edu/ 在线扩增子分析平台,可存储数据 [35]
MGnify https://www.ebi.ac.uk/metagenomics/ 在线扩增子和宏基因组分析平台,可存储数据 [36]
gcMeta https://gcmeta.wdcm.org/ 中国科学院微生物所开发的在线扩增子和宏基因组分析平台 [37]
Greengenes https://greengenes.secondgenome.com/ 16S rRNA基因数据库,QIIME推荐数据库,但13年发表后无更新,功能注释软件PICRUSt和BugBase依赖此数据库 [38]
SILVA https://www.arb-silva.de/ rRNA基因数据库,包括真核、细菌和古菌三域的大小亚基序列,更新快、序列全,适用于物种分类和嵌合体检测 [39]
RDP https://rdp.cme.msu.edu/ 核糖体16S/28S数据库,适合物种注释,同时有在线分析流程 [40]
UNITE https://unite.ut.ee/ 真核生物ITS数据库,常用于真菌ITS扩增子测序分析中嵌合体检测和物种分类 [41]
vegan https://cran.r-project.org/package=vegan 微生物生态学领域的排序方法、多样性分析和可视化的R包,更有可视化增加的ggvegan版本https://github.com/gavinsimpson/ggvegan [31]
phyloseq https://joey711.github.io/phyloseq 扩增子分析R包,提供多样性分析、差异比较和进化树的可视化功能,同时提供网页版shiny-phyloseq [32,34]
microbiome http://bioconductor.org/packages/
microbiome/
扩增子分析辅助R包,提供核心OTU/ASV计算、相关分析等函数 [33]
PICRUSt https://github.com/picrust/picrust 基于Greengenes 16S rRNA基因预测宏基因组基因功能信息。现发布第2版实现对任意16S序列功能预测且数据库增大10倍 [42]
Tax4Fun http://tax4fun.gobics.de/ 基于SILVA 16S OTU表预测功能组成,第2版更新数据库和方法(https://sourceforge.net/projects/tax4fun2/) [43]
FAPROTAX http://www.loucalab.com/archive/
FAPROTAX/
原核分类学功能注释,获得元素循环相关文献挖掘的物种功能注释,适合于农业、环境相关研究菌种功能描述 [44]
BugBase https://bugbase.cs.umn.edu/ 物种水平微生物表型预测,如革兰氏阳/阴性、厌氧/需氧等 [45]
FUNGuild http://www.stbates.org/guilds/app.php 真菌的物种功能分类注释 [46]
表1  扩增子分析常用软件和数据库
表2 宏基因组分析常用软件和数据库 Table 2 Metagenome analysis software and databases
名称 链接 简介 参考文献
MultiQC https://multiqc.info/ 多样本质控和分析结果汇总 [66]
Trimmomatic http://www.usadellab.org/cms/index.php?
page=trimmomatic
Java编写的质量控制软件,实现快速去除低质量、接头和引物序列。被质控流程KneadData流程整合为默认质控软件。 [67]
Bowtie 2 http://bowtie-bio.sourceforge.net/bowtie2 序列比对工具,短读长序列快速比对至参考序列,结果为SAM/BAM格式 [68]
MetaPhlAn2 https://bitbucket.org/biobakery/
metaphlan2/
物种组成定量流程,包括人工整理的上万物种中的上百万个标记基因数据库,结果可直接用于LEfSe分析 [47]
HUMAnN2 https://bitbucket.org/biobakery/humann2 功能组成定量流程,默认基于UniRef数据库注释序列,获得基因家族、通路丰度和覆盖度的功能组成表 [49]
UniRef https://www.uniprot.org/uniref/ 非冗余蛋白序列数据库,用于宏基因组分析中序列或基因的功能注释 [69]
Kraken 2 https://ccb.jhu.edu/software/kraken2/ 物种分类软件,基于K-mer方式匹配NCBI 非冗余数据库实现超高速物种注释,内存要求高 [48]
MEGAHIT https://github.com/voutcn/megahit 宏基因组拼接软件,内存消耗低,计算速度快、嵌合体率较低、N50偏低 [70]
metaSPAdes http://cab.spbu.ru/software/spades/ 宏基因组拼接软件,内存消耗大,计算时间长,但有更长的N50,也存在拼接错误和嵌合体比例升高的风险 [50]
MetaQUAST http://quast.sourceforge.net/metaquast 拼接结果评估,输出拼接指标和可视化图形的PDF和交互式网页版报告 [71]
Prokka http://www.vicbioinformatics.com/
software.prokka.shtml
原核基因组注释流程,主要用于基因组、宏基因组中的编码基因预测,生成提交NCBI所需要的注释文件 [51]
GeneMarkS-2 http://exon.gatech.edu/GeneMark/
genemarks2.cgi
基因组注释网页工具,用户无需服务器和安装软件,浏览器中实现宏基因组中基因预测 [52]
CD-HIT http://weizhongli-lab.org/cd-hit/ 序列去冗余,实现核酸、蛋白构建非冗余基因集 [53]
Salmon https://combine-lab.github.io/salmon/ 非比对基因定量,基于K-mer方式超快速实现序列分配,无中间文件生成,直接获得计数型结果 [72]
DIAMOND https://github.com/bbuchfink/diamond 比BLAST更快的蛋白比对工具 [73]
eggNOG http://eggnogdb.embl.de/app/emapper#/
app/downloads
同源组蛋白数据库 [74]
GhostKOALA https://www.kegg.jp/ghostkoala/ 在线KEGG注释工具,可为基因序列分配KO编号 [75]
CAZy http://www.cazy.org/ 蛋白功能注释:碳水化合物基因数据库 [54]
CARD https://card.mcmaster.ca 蛋白功能注释:抗生素抗性基因综合数据库 [55]
Resfams http://www.dantaslab.org/resfams 蛋白功能注释:抗生素抗性基因数据库 [76]
VFDB http://www.mgc.ac.cn/VFs/ 蛋白功能注释:毒力因子数据库 [56]
MetaBAT 2 https://bitbucket.org/berkeleylab/metabat/ 主流分箱工具 [57]
MaxBin 2 https://sourceforge.net/projects/maxbin2/ 主流分箱工具 [58]
CONCOCT https://github.com/BinPro/CONCOCT 主流分箱工具 [59]
metaWRAP https://github.com/bxlab/metaWRAP 分箱流程,依赖140余款工具,可实现conda快速安装,默认对3种主流分箱结果提纯,提供多种可视化方案 [60]
DAS_Tool https://github.com/cmks/DAS_Tool 分箱流程,对5种主流分箱工具结果提纯 [61]
Athena https://github.com/elimoss/metagenomics_workflows/ 基于10×建库宏基因组测序的组装软件 [63]
OPERA-MS https://github.com/CSB5/OPERA-MS 基于Illumina、Nanopore和PacBio的二、三测序数据混合组装软件 [64]
MAGpy https://github.com/WatsonLab/MAGpy 分箱结果下游比较基因组分析流程 [65]
OrthoFinder https://github.com/davidemms/
OrthoFinder
同源基因鉴定,基于多个细菌基因组中的蛋白组鉴定单拷贝同源基因和构建多基因进化树 [77]
Microbiome helper https://github.com/LangilleLab/
microbiome_helper
微生物组分析中常用格式转换工具集,方便分析和流程搭建 [78]
表2  宏基因组分析常用软件和数据库
表3 微生物组下游通用分析工具 Table 3 Downstream software for microbiome analysis
名称 链接 简介 参考文献
edgeR http://bioconductor.org/packages/edgeR/ 数字基因表达数据的经验分析R包,常用于基于计数型数据和负二项分布模型进行差异统计 [79]
DESeq2 http://bioconductor.org/packages/DESeq2/ 基于负二项分布的差异基因表达分析R包,与edgeR包类似 [80]
limma http://bioconductor.org/packages/limma/ 基于线性模型分析芯片数据R包,可用于微生物组数据差异比较 [81]
lme4 https://github.com/lme4/lme4/ 拟合线性和广义线性混合效应模型,可结合已知影响因素数据校正的差异比较 [82]
STAMP http://kiwi.cs.dal.ca/Software/STAMP 图型界面的微生物组统计与可视化软件,跨平台,Windows中安装方便,但不支持中文,Linux/Mac中安装困难 [83].
LEfSe https://bitbucket.org/biobakery/biobakery/wiki/lefse 微生物组生物标记挖掘工具,支持Linux命令行、网页界面、多组比对,结果可视化为柱状图和进化分枝图 [84]
GraPhlAn https://bitbucket.org/nsegata/graphlan 进化分枝图可视化工具 [85]
MicrobiomeAnalyst http://www.microbiomeanalyst.
ca/
在线微生物组特征表分析平台,支持几十种常用分析和可视化,可导出网页版分析报告 [86]
igraph https://igraph.org/r/ 网络图可视化平台,可在R语言中可实现网络图可视化、布局和细节调整 [96]
Cytoscape https://cytoscape.org/ 网络分析和可视化图型界面分析平台,功能强大,跨平台,扩展插件丰富 [92]
Gephi https://gephi.org/ 网络分析和可视化软件,样式比较美观 [97]
MAFFT 7 https://mafft.cbrc.jp/alignment/
software/
多序列对齐软件,序列对齐速度快 [101]
MUSCLE https://www.drive5.com/muscle/ 多序列对齐软件,序列对齐速度快 [102]
IQ-TREE http://www.iqtree.org/
http://iqtree.cibiv.univie.ac.at/
进化树构建,在运行速度上有较明显的优势,跨平台,速度快,提供在线版 [104,105]
iTOL https://itol.embl.de/ 进化树可视化、编辑和美化工具,功能全面,支持结果生成分享链接 [107]
randomForest https://cran.r-project.org/web/
packages/randomForest/
实现随机森林分类和回归分析的R包 [114]
表3  微生物组下游通用分析工具
表4 部分提供统计分析代码的实验室 Table 4 Labs that provide statistical analysis codes
研究单位 课题组 链接 参考文献
美国密歇根大学 Patrick D. Schloss http://www.schlosslab.org [123]
美国斯坦福大学 Susan Holmes http://statweb.stanford.edu/~susan [124]
德国马普植物育种研究所 Paul Schulze-Lefert https://github.com/garridoo [125]
美国北卡罗来纳大学教堂山分校 Jeffery L. Dangl https://github.com/surh/pbi
https://github.com/isaisg/
[126,127]
EMBL-EBI Robert D. Finn https://github.com/Finn-Lab [128]
比利时鲁汶大学 Jeroen Raes https://github.com/raeslab [129]
美国贝勒医学院 Christopher J. Stewart https://github.com/StewartLab [130]
美国俄勒冈大学 James F. Meadow https://github.com/jfmeadow [131]
中国科学院遗传与发育生物学研究所 Yang Bai https://github.com/microbiota [132,133]
表4  部分提供统计分析代码的实验室
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