/ecg/seqprg/scripts/init_meta1.shIf things have worked properly, you should have the directory meta1_work in your home directory, and it should contain several files.
cd meta1_work ls
To run the mothur program, type:
/ecg/seqprg/bin/mothurAll commands in mothur look like functions, and need to end with (), for example: help() or quit().
To characterize the taxonomic makeup, we first cluster sequences into OTUs (taxa, clades), use make.shared to count sequence abundance within those OTUs.
The lines below show commands that can be copied and pasted into the command line of mothur. Lines beginning with # are comments to explain the steps; you only need to copy lines that do not begin with #.
#Cluster sequences into OTU -- we are using the command cluster.split to do that as it allows us to cluster # sequences according a taxonomic level like order or family cluster.split(fasta=final.fasta, taxonomy=final.taxonomy, name=final.names, taxlevel=3, processors=4) # The make.shared creates a file that represent the number of times # that an OTU is observed in multiple samples make.shared(list=final.an.list, group=final.groups, label=0.03) # Since some samples might have better coverage, sub-sample to get a dataset # with the same number of sequences per sample sub.sample(shared=final.an.shared,size=400) # assign a consensus taxonomy to each OTU classify.otu(list=final.an.list, name=final.names, taxonomy=final.taxonomy, label=0.03, cutoff=80) # Calculate the relative abundance of taxa phylotype(taxonomy=final.taxonomy, name=final.names, label=1) # The make.shared creates a file that represent the number of times that an OTU is observed in multiple samples make.shared(list=final.tx.list, group=final.groups, label=1) # Since some samples might have better coverage, subsample to get a dataset # with the same number of sequences per sample sub.sample(shared=final.tx.shared, size=400) # assign a consensus taxonomy to each OTU classify.otu(list=final.tx.list, name=final.names, taxonomy=final.taxonomy, label=1)
#Here we want to build a phylogenetic tree sub.sample(fasta=final.fasta, name=final.names, group=final.groups, persample=T, size=400) dist.seqs(fasta=final.subsample.unique.fasta, output=lt, processors=2) #Build Tree clearcut(phylip=final.subsample.unique.phylip.dist)You can visualize the tree with TreeView (in /ecg/Applications/TreeView).
#Generates collector's curves collect.single(shared=final.an.0.03.subsample.shared, calc=chao-invsimpson, freq=100) rarefaction.single(freq=100) summary.single(calc=nseqs-coverage-sobs-invsimpson)
#Generate heatmaps and a venn diagram to compare samples heatmap.bin(scale=log2, numotu=50) heatmap.sim(calc=jclass-thetayc) venn(groups=26-23-28-44) #PCOA Analysis to Compare samples dist.shared(shared=final.an.0.03.subsample.shared, calc=thetayc-jclass) pcoa(phylip=final.an.0.03.subsample.thetayc.0.03.lt.dist) pcoa(phylip=final.an.0.03.subsample.jclass.0.03.lt.dist) #Non-metric multidimensional scaling nmds(phylip=final.an.0.03.subsample.thetayc.0.03.lt.dist) nmds(phylip=final.an.0.03.subsample.jclass.0.03.lt.dist)
These programs create .svg files, which you should be able to visualize with a web browser.