博文
用Mothur分析多个DNA序列的OTU或ST
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Mothur的主页：
http://www.mothur.org/wiki/Main_Page
Mothur是一个架构非常好的生物信息学软件，把大量的工具和模块整合到了一起，并且将输入和输出标准化，非常简单易学。在高通量测序数据处理中特别有用。Mothur可用于距离的计算、多样性计算，非常适合微生物生态学和种群结构的研究。
正如发表mothur软件（AEM，2009， 75：7537）时，作者在摘要中描述的那样：mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.
本文介绍OTU或ST的确定使用方法。
使用Mothur软件进行多个DNA序列OTU（operational taxonomical unit）的确定或ST(sequence type)的确定.
主要用到unique.seqs、dist.seqs、bin.seqs、get.oturep命令。
使用方法详见附件。
附注：operational taxonomic units (OTU)
operational taxonomic units (OTUs)在微生物的免培养分析中经常用到，通过提取样品的总基因组DNA，利用16S rRNA或ITS的通用引物进行PCR扩增，通过测序以后就可以分析样品中的微生物多样性，那怎么区分这些不同的序列呢，这个时候就需要引入operational taxonomic units，一般情况下，如果序列之间，比如不同的 16S rRNA序列的相似性低于98%就可以把它定义为一个OTU，每个OTU对应于一个不同的16S rRNA序列，也就是每个OTU对应于一个不同的细菌（微生物）种。通过OTU分析，就可以知道样品中的微生物多样性和不同微生物的丰度。
bin.seqs - identify the OTU that each sequence belongs to
更多 的命令见下面（内容来转网络：http://blog.sina.com.cn/s/blog_83f77c94010161h7.html）
1：Home page: http://www.mothur.org/
2:Analysis pipeline:
2-1: chimera.uchime - identify potentially chimeric sequences (去除嵌合体)
command:chimera.uchime(fasta=out.fasta, reference=silva.gold.align)
output：out.uchime.chimera; out.uchime.accnos
2-2: unique.seqs - identify the unique sequences in a collection and generate a names file
command: unique.seqs(fasta=out.fasta) output:out.unique.fasta; out.names
2-3: align.seqs - align sequences against a reference alignment
alignment databases: http://www.mothur.org/wiki/Alignment_database
command: align.seqs(fasta=out.unique.fasta, reference=silva.eukarya.fasta, processors=2)
output: out.unique.align; ###.unique. align.report; ### .unique.flip.accnos
2-4: screen.seqs - remove sequences that don't satisfy criteria
command:screen.seqs(fasta=out.unique.align, maxambig=2, minlength=100, maxlength=400,name=out.names)
summary.seqs(fasta=out.unique.good.align,name=out.good.names)
output: out.unique.good.align; out.unique.bad.accnos; out.good.names
2-5: filter.seqs - filter positions out of an alignment
command: filter.seqs(fasta=out.unique.good.align,trump=.,vertical=T)
output: out.unique.good.filter.fasta;out.filter
2-6: dist.seqs - generate a pairwise distance matrix
command: dist.seqs(fasta=out.unique.good.filter.fasta, output=lt, processors=2)
output: out.unique.good.filter.phylip.dist
2-7:OTU-based Analyses:
cluster(phylip=out.unique.good.filter.phylip.dist, cutoff=0.10,name=out.names)
output:###.an.shared, ###.an.B.rabund; ###.an.list
2-8: rarefaction.single- generate intra-sample rarefaction curves(稀疏曲线)
command:rarefaction.single(list=out.unique.good.filter.phylip.an.list)
R_command:data <- read.table("out.unique.good.filter.phylip.an.rarefaction", header=TRUE)；
attach(data)；
plot(numsampled, unique, type="b", xlab="fungi sequences sampled", ylab="Observed OTUs", col="blue")
2-9：Phylotype analysis
classify.seqs(fasta=out.fasta, template=nogap.eukarya.fasta, taxonomy=silva.eukarya.silva.tax, iters=1000, cutoff=60)
output: ###.silva.taxonomy；###.silva.tax.summary
classify.otu(taxonomy=out.silva.taxonomy, list=out.unique.good.filter.phylip.an.list,name=out.names)
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https://blog.sciencenet.cn/blog-3533-635218.html
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