16s rRNA早期的分析策略，如FISH（fluorescent in situ hybridization）、DDGE（denaturing gradient gel electrophoresis）、PCR cloning、T-RFLP（terminal restriction fragment length polymorphism）。随着NGS（next generation sequencing）测序技术的发展，在此主要讨论NGS技术在16s rRNA分析中的应用。 16s rRNA NGS数据分析的主要工具有： 16s rRNA NGS数据的分析主要有3个大步骤： 1）pretreatment of raw sequence data：including de-multiplexing of barcoded sequences, quality filtering, denoise, chimera checking, and data normalization; 2）microbial diversity analysis：construction of operational taxonomic unit (OTU)table, including picking OTUs, picking representative sequences, aligning representative sequences, taxonomic assignment of representative sequences, and building of a phylogenetic tree of the OTUs; 3）advanced data analysis and visualization：including the alpha- and beta-diversity analyses,clustering and coordinates analysis, and data visualization； （1）De-multiplexing and quality filtering 16s经常是pooling测序，为此需要将下机数据根据barcode序列信息将数据拆分到各样品中。QIIME中的 “ split_libraries.py ” 和“ split_libraries_fastq.py ”实现数据拆分和数据过滤的双重目的。Mothur利用“ Trim.seqs ”。不过QIIME和Mothur都不能直接处理sff文件（454下机产生的数据格式），不过可各自利用“process_sff.py”和Sffinfo将sff格式转换为FASTA和QUAL文件。 数据过滤考虑的参数有：minimum average quality score allowed in a read、maximum number of ambiguous bases allowed、minimum and maximum sequence length、maximum length of homopolymer allowed、maximum mismatches inprimer or barcode allowed、whether to truncate reverse primer, and so on. （2）Denoise and chimera checking 16s建库的pcr过程、测序过程均会导致序列出现错误，在分析过程过程中需要有效排除这种错误。测序误差矫正常用的工具有 Denoiser（implemented in QIIME）、AmpliconNoise、Acacia、Pre.cluster（implemented in Mothur） 。嵌合体查找的工具有 ChimeraSlayer、UCHIME、Persus、DECIPHER ， ChimeraSlayer、UCHIME、Persus 在mothur中均可调用。 在这些工具中，存在有待于优化的问题 （these different methods often disagree with one another on the list of identified chimeras，probably because of their different mechanisms or algorithms. More efforts are required to evaluate these methods and coordinate their inconsistencies in chimera identification.） 在分析中有个关于古细菌序列的情况需要注意： a very small proportion of archaeal sequences may be generated for 16S rRNA gene amplicon datasets amplified with bacteria-specific primers. These unexpected sequences should be identified after denoising and chimera removal, and are advised to be discarded before subsequent data normalization. （3）Data normalization 测序深度不理想和不均匀的话会对alpha多样性及beta多样性均有影响。Uneven sequencing depth can affect diversity estimates in a single sample (i.e.,alpha diversity), as well as comparisons across different samples (i.e., beta diversity), thus data normalization is required. 对于此问题有两种处理策略，分别是relative abundance and random sampling (i.e., rarefaction)，in addition, z-score亦用于normalization的过程中。但不同的方法均会有缺点。 （4）Picking OTUs and representative sequences OTU的界定主要根据序列的一致性进行，（The OTUs are picked based on sequence identity, and various identity cutoffs of 16S rRNA gene have been used for different taxonomic ranks. For example, identity cutoffs recommended by MEGAN are 99 % for species, 97 % for genus,95 % for family, and 90 % for order level, respectively）。OTU界定时选择的工具与算法对后期分析有很大影响（The OTU picking strategy and algorithms have significant effects in the downstream data interpretation. ）根据分析过程中是否使用数据库，OTU界定的策略可分为de novo、closed reference和open reference。在 OTU界定中有很多聚类的方法，There are many clustering or alignment tools available for OTU picking, such as Uclust, cd-hit, BLAST, mothur, usearch, and prefix/suffix. These tools are implemented in QIIME. Among them, the mothur method contains three clustering algorithms to pick de novo OTUs, namely, nearest neighbor, furthest neighbor, or average neighbor. 当序列聚类好后，代表了一个OTU，接下来就是从这个OTU找到代表序列，一种做法是a representative sequence can be a random, the longest, the most abundant(as default in QIIME), 另一种操作方法是the first sequence in an OTU cluster. 还有一种策略是the distance method in mothur identifies the sequence with the smallest maximum distance to the other sequences as the representative sequence. （5）Taxonomic assignment taxonomic assignment的策略有：（1）word match，如RDP classfier，（2）best hit，（3）Lowest Common Ancestor，如MEGAN、SINA Alignment Service. （6 ）Phylogenetic analysis Phylogenetic relationships一般可以用树来表示，phylogenetic relationships主要是通过序列比对来实现的，序列比对的工具有ClustalW, MUSCLE, Clustal Omega, Kalign, T-COFFEE, COBLAT和FastTree. 目前针对16s rRNA NGS数据的分析工具都可以实现，如MEGA，RAxML，MRBAYES，PhyML，TreeView，Clearcut，FitTree. 其中RAxMLand PhyML are the most widely used programs for maximum-likelihood phylogenetic analysis, probably because they are specifically designed and optimized for such purpose. （7）Alpha- and beta-diversity analyses alpha多样性有众多指标可以表示，在mothur中有Shannon, Berger-Parker,Simpson, Q statistic; observed richness, Chao1, ACE, and jackknife。而在QIIME中，有phylogenetic diversity (PD)-whole tree, chao1, and observed species. 还有一种物种丰度的比较方法：rarefaction curve. QIIME中主要用“single_rarefaction.py”、 “multiple_rarefaction.py”，在mothur中主要用“Rarefaction.single”和“Rarefaction.shared”. beta多样性计算主要反映不同样本之间的差异度，several distance metrics, such as Unifrac, Bray-Curtis, Euclidean,Jaccard index, Yue Clayton, and Morisita-Horn, have been often employed. beta多样性计算根据是否考虑OTU的相对丰度，可分为定量指数和定性指数。 （ 8）Statistical and network analysis 在Two-sample/group中，多考虑 t－test。 在其中需要注意， Particularly for independent two-sample t -test, independence and equal variances (which canbe tested by F-test, Levene ’ s test, etc.) of two populations arerequired. In the case of non-normal distribution of data sets,nonparametric two-sample tests robust to data non-normality,such as Wilcoxon signed-rank test, and Mann-Whitney U testare applicable for significance testing of difference betweengroup medians. 在Multiple-sample/group tests中，ANOVA。 （9）Clustering and classification clustering可以分析样品之间的亲疏关系。classfication的策略用来对样品进行类别判定。 （10）Ordination analysis 在样本的相似度和距离计算完后，可以利用 principal component analysis (PCA), principal coordinates analysis(PCoA, also known as metric multidimensional scaling), Nonmetric multidimensional scaling (NMDS), canonical correspondence analysis (CCA), linear discriminantanalysis (LDA), and redundancy analysis (RDA)等构建样本间的关系。 （11）Network-based modeling 与基因表达、代谢分子、蛋白等数据一起分析共表达网路或者共表达模式（co-occurrence and co-exclusion patterns） 参考文章： Ju F, Z hang T. 16s rRNA gene high throughput sequencing data mining of microbiota diversity and interactions, Appl Microbiol Biotechnol . 2015, 99(10):4119-4129
标签: QIIME
