Mothur commands used 20180226

Preparing for clustering and OTU picking

Removing the mock communtiy from the dataset

remove.groups(count=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.count_table,
 fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.fasta, 
 taxonomy=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pds.wang.pick.taxonomy,
 groups=Mock)

Build a distance matrix with the maximum distance set

dist.seqs(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.fasta,
 cutoff=0.20, processors=2)

Why is the cutoff used here? and how does mothur use that cutoff to build the distance matrix

Cluster sequences using average linkage clustering

cluster(column=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.dist,
  count=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.count_table,
  method=average, cutoff=0.20)

Note: in the newest MiSeq SOP this method is replaced by the optiClust algorithm.

Create an otu table, as well as relative abundance files for each sample

make.shared(list=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.an.unique_list.list,
 count=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.count_table,
 label=0.03)

Classifying the OTU sequences see in what taxon they fall.

classify.otu(list=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.an.unique_list.list,
 count=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.count_table,
 taxonomy=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pds.wang.pick.pick.taxonomy,
 label=0.03)

renaming output files so we can use them easily in the diversity analysis

system(cp stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.dist final.dist)

system(cp stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.fasta final.fasta)

system(cp stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.an_unique_list final.list)

system(cp stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.an_unique_list.0.03.cons.taxonomy final.0.03.taxonomy)

system(cp stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.an_unique_list.0.03.cons.tax.summary final.0.03.tax.summary)

system(cp stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.an_unique_list.shared final.shared

system(cp stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.count_table final.count_table)

Now we have the final datasets, that can be used for diversity analysis using otu

##Preparing for diversity analysis

identifying the sample with the least sequences

count.groups(shared=final.shared)

subsampling all datasets to downsample to the smallest sample size

It is important that you check if your samples match the size(2401) indicated here, if not that use the smallest sample size in your analysis.

subsample(shared=final.shared, size=2401)

calculating data to create rarefaction curves

rarefaction.single(shared=final.0.03.subsample.shared,
 calc=sobs, freq=100)

calculating alpha-diversity estimators using subsampling

summary.single(shared=final.shared,
 calc=nseqs-coverage-sobs-invsimpson,
 subsample=2401)