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

Command:

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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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序列的相似性大于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

mothur > bin.seqs(list=98_sq_phylip_amazon.an.list, fasta=amazon.fasta)

这个list是来自2-7之后。

原有的软件：

Introducing DOTUR, a Computer Program for Defining Operational Taxonomic Units and Estimating Species Richness