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1、QIIME (pronounced chime) stands for Quantitative Insights Into Microbial Ecology. QIIME is an open source software package for comparison and analysis of microbial communities, primarily based on high-throughput amplicon sequencing data (such as SSU rRNA小亚基核糖体rna http:/ generated on a variety of pla
2、tforms, but also supporting analysis of other types of data (such as shotgun metagenomic data metagenomic意思是宏基因组学，是对环境样品中微生物群体基因组进行的分析). QIIME takes users from their raw sequencing output through initial analyses such as OTU picking系统聚类, taxonomic assignment分类, and construction of phylogenetic trees
3、系统树 from representative sequences of OTUs, and through downstream statistical analysis, visualization, and production of publication-quality graphics. QIIME has been applied to适用于 single studies based on billions of sequences from thousands of samples. This tutorial explains how to use the QIIME (Qu
4、antitative Insights Into Microbial Ecology) Pipeline to process data from high-throughput 16S rRNA sequencing studies. If you have not already installed qiime, please see the section Installing Qiime first. The purpose of this pipeline流水线 is to provide a start-to-finish workflow, beginning with mult
5、iplexed sequence复合序列（多序列比对，整理分类和系统文件，比较样本，确定改变微生物群体形态的生物和环境因素） reads and finishing with taxonomic and phylogenetic profiles and comparisons of the samples in the study. With this information in hand, it is possible to determine biological and environmental factors that alter microbial community ecol
6、ogy in your experiment.As an example, we will use data from a study of the response of mouse gut microbial communities to fasting (Crawford et al., 2009). To make this tutorial run quickly on a personal computer, we will use a subset of the data generated from 5 animals kept on the control ad libitu
7、m fed diet, and 4 animals fasted for 24 hours before sacrifice. At the end of our tutorial, we will be able to compare the community structure of control vs. fasted animals. In particular, we will be able to compare taxonomic profiles for each sample type, differences in diversity metrics within the
8、 samples and between the groups, and perform comparative clustering analysis to look for overall differences in the samples.（给小鼠节食的例子）In this walkthrough, text like the following:print_qiime_config.pydenotes the command-line invocation命令行调用 of scripts. You can find full usage information for each sc
9、ript by passing the h option (help) and/or by reading the full description in the Documentation. Execute all tutorial commands from within the qiime_tutorial directory, which can be downloaded from here: QIIME Tutorial files.To process our data, we will perform the following analyses, each of which
10、is described in more detail below: Filter the DNA sequence reads for quality and assign multiplexed reads to starting samples by nucleotide barcode条码 . Pick Operational Taxonomic Units (OTUs操作分类单元) based on sequence similarity within the reads, and pick a representative sequence from each OTU. Assig
11、n the OTU to a taxonomic identity using reference databases. Align the OTU sequences and create a phylogenetic tree. Calculate diversity metrics for each sample and compare the types of communities, using the taxonomic and phylogenetic assignments. Generate UPGMA and PCoA plots to visually depict th
12、e differences between the samples, and dynamically work with these graphs to generate publication quality figures.筛选DNA序列获取质量，记录样品的核苷酸条码。基于读取文件的序列相似挑选操作分类单位，挑选每个OTU的代表序列。使用参考数据库指定OUT的分类一致性。对齐OTU序列，并创建一个系统进化树。计算每个样本的多样性指标和比较社区的类型，使用分类和系统法。类平均法和主坐标分析直观地描绘出样品之间的差异，并动态地使用这些曲线生成出版质量的图。Sequences (.fna)Thi
13、s is the 454-machine generated FASTA file格式文件. Using the Amplicon processing software on the 454 FLX standard, each region of the PTP plate will yield a fasta file of form 1.TCA.454Reads.fna, where “1” is replaced with the appropriate region number. For the purposes of this tutorial, we will use the
14、 fasta file Fasting_Example.fna.Quality Scores (.qual)This is the 454-machine generated quality score file, which contains a score for each base in each sequence included in the FASTA file. Like the fasta file mentioned above, the Amplicon processing software will generate one of these files for eac
15、h region of the PTP plate, named 1.TCA.454Reads.qual, etc. For the purposes of this tutorial, we will use the quality scores file Fasting_Example.qual.Mapping File (Tab-delimited .txt)The mapping file is generated by the user. This file contains all of the information about the samples necessary to
16、perform the data analysis. At a minimum, the mapping file should contain the name of each sample, the barcode sequence used for each sample, the linker/primer sequence used to amplify the sample, and a Description column. In general, you should also include in the mapping file any metadata元数据；诠释资料 that relates to the samples (for instance, health status or sampling site) and
