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使用mothur软件分析16s做系统进化树的一般流程

更新时间:2023-07-01 06:55:10 阅读：评论：0

mothur分析tibet_dth过程

align序列: *已手动分组为ACD16,tibet_01,tibet_02..等9组

align.qs(candidate=th283.fasta, template=core_t_aligned.imputed.fasta,processors=2

查看比对情况:

summary.qs(fasta=th283.align *见ACD16-16异常

清除"bad" alignment:

screen.qs(fasta=th283.align, ups, start=2318, end=5823, maxambig=1 *该命令除去了ACD16-16,Tibet01-69

summary.qs(d.align

柜子英文filter掉序列内的gaps:

filter.qs(d.align, trump=.,vertical=T #vertical删除边gap,trump删除内gap

产生距离矩阵：使用Lt格式输出

dist.qs(d.filter.fasta, output=lt,processors=2 #output供选格式为lt,square和column(phylip软件只识别square)

基于otu处理:

cluster序列: *使用默认的聚集算法AN

cluster(phylip=th283.dist, cutoff=0.01

brs

make.shared(list=th283.an.list,d.groups)

rarefaction.single

##The rarefaction.single command will generate intra-sample rarefaction curves using a re-sampling without replacement approach. Rarefaction curves provide a way of comparing the richness obrved in different samples. Roughly speaking you get the number of OTUs, on average, that you would have been expected to have obrved if you hadn't sampled as many individuals. Although a formula exists to generate a rarefaction curve (e the example calculation), mothur us a randomization procedure. It can also help you to asss your sampling intensity. If a rarefaction curve becomes parallel to the x-axis, you can be reasonably confident that you have done a good jo

b of sampling and can trust the obrved level of richness. Otherwi, you need to keep sampling. Rarefaction is actually a better measure of diversity than it is of richness

rarefacton.shared()

###The rarefaction.shared command will generate inter-sample rarefaction curves using a re-sampling without replacement approach. The traditional way that ecologists u rarefaction is not to randomize the sampling order within a sample, rather between samples. For instance, if we wanted to know the number of OTUs in the human colon, we might sample from various sites within the colon, and quence a bunch of 16S rRNA genes. By determining the number of OTUs in each sample and comparing the composition of tho samples it is possible to determine how well you have sampled the biodiversity within the individual. mothur has the ability to generate data for inter-sample rarefaction curves for the number of obrved species

collect.single()

##collect.single generates collector's curves using calculators, that describe the richness, diversity, and other features of individual samples. Collector's c

e_mail

urves describe how richness or diversity change as you sample additional individuals. If a collector's curve becomes parallel to the x-axis, you can be reasonably confident that you have done a good job of sampling and can trust the last value in the curve. Otherwi, you need to keep sampling

collect.shared()

goldrush

###The collect.shared command generates collector's curves for calculators, which describe the similarity between communities or their shared richness. Collector's curves describe how richness or diversity change as you sample additional individuals. If a collector's curve becomes parallel to the x-axis, you can be reasonably confident that you have done a good job of sampling and can trust the last value in the curve

summary.single()

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###The summary.single command will produce a summary file that has the calculator value for each line in the OTU data and for all possible comparisons between the different groups in the group file. This can be uful if you aren't interested in generating collector's or rarefaction curves for your multi-sample data analysis. It would be worth your while, however, to look at the collector's curves for the calculators you are interested in to determine how nsitive the values are to sampling. If the

values are not nsitive to sampling, then you can trust the values. Otherwi, you need to keep sampling

summary.shared()

##The summary.shared command will produce a summary file that has the calculator value for each line in the OTU data and for all possible comparisons between the different groups in the group file. This can be uful if you aren't interested in generating collector's or rarefaction curves for your multi-sample data analysis. It would be worth your while, however, to look at the collector's curves for the calculators you are interested in to determine how nsitive the values are to sampling. If the values are not nsitive to sampling, then you can trust the values. Otherwi, you need to keep sampling

venn

heatmap.bin() 比较有意思的命令 scale=log2,log10,linear sorted=none,topotu,topgroup

#The heatmap.bin command generates a heat map from data provided in either a *.list or a *.shared file. Each row in the heatmap reprents a different OTU and the color of the OTU in each group sca

led between black and red according to the relative abundance of that OTU within the group. The command will generate a SVG file that can be further modified in a program like Gimp or Adobe Illustrator. Options are available to scale the relative abundance for each OTU using different approaches

heatmap.sim()

#The heatmap.sim command will generate a heatmap indicating the pairwi similarity between multiple samples using a variety of calculators comparing community membership and structure. As an example, we will u the example from the Sogin data analysis example

#mothur > make.shared(list=thac.an.list, d.groups, label=unique-0.03)

#collect.single(,freq=1 分别计算歌库的collect..(chao,ace etal

#rarefaction.single(freq=0.15) 单样本的取样深度曲线

#mothur > summary.single() 汇总各库的多样性指数

多样本分析

##summary.shared(shared=....an.shared) 比较库间的指数的共享otu数目

chinehandsomeguy##venn(label=0.03, calc=sharedsobs-sharedchao 绘制venn图

写入代表性的cutoff距离值:

分析单样本alpha多样性指数:

nen

rarefaction.single(

collect.single(calc=chao-npshannon *得到0.01,0.03,0.05下的chao和shannon

重复cad组的操作，计算余下的tibet_01,等八组多样性指数:

summary.single(