Skip to content

Latest commit

 

History

History
85 lines (53 loc) · 5.74 KB

20180212_MiSeq_SOP_commands.md

File metadata and controls

85 lines (53 loc) · 5.74 KB
Raw

Mothur commands used 20180205

This section will cover

  1. Removing chimeric sequences
  2. Removing unwanted taxa from the dataset
  3. Calculate the error rate using the Mock community
  4. Determine OTUs from the tutorial data at thr 97% sequence similarity level.

Searching for chimeric sequences with chimera.uchime

Note that we use uchime here, but the current mothur standard is Vsearch and is recommended for chimera checking. We use uchime since the biolinux version does not have Vsearch.

chimera.uchime(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.fasta, count=stability.trim.contigs.good.unique.good.filter.unique.precluster.count_table)

Removing chimeric sequences, the INCORRECT way

remove.seqs(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.fasta, accnos=stability.trim.contigs.good.unique.good.filter.unique.precluster.uchime.accnos)

summary.seqs(fasta=current, count=current)

Removing chimeric sequences, the CORRECT way

remove.seqs(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.fasta, accnos=stability.trim.contigs.good.unique.good.filter.unique.precluster.uchime.accnos, count=stability.trim.contigs.good.unique.good.filter.unique.precluster.count_table)

Why is this the correct way?

summary.seqs(fasta=current, count=current)

Classifying seqs to detect undesired lineages

classify.seqs(fasta=current, count=current, taxonomy=trainset9_032012.pds.tax, reference=trainset9_032012.pds.fasta, cutoff=80)

Removing undesired lineages

remove.lineage(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.fasta, count=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.count_table, taxonomy=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pds.wang.taxonomy, taxon=Chloropast-Mitochondria-unknown-Archaea-Eukaryota)

Summary.seq file to create an overview of the clean dataset

summary.tax(taxonomy=current, count=current)

Summary.tax to create an overview of the taxonomic classifications present in the clean dataset

summary.tax(taxonomy=current, count=current)

At this point we have a clean dataset which can be used for generating OTUs and doing diversity studies. No matter what toolor pipeline you have used al of the above filtering/removal /screening steps have to be performed. Of course this is debatable.

##Assessing error rates In this section we use a mock community consisting of 32 unaligned fasta sequences, to analyze the error rate that we have based on the clean-up that we have performed.

extracting the mock community dataset from the complete dataset

get.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, groups=Mock)

Calculating the seq error rate using the mock community fasta sequences

seq.error(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.fasta, count=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.count_table, reference=HMP_MOCK.v35.fasta, aligned=F)

Calculating distances in the mock community sequences

dist.seqs(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.fasta, cutoff=0.03)

Clustering sequences based on distances to generate OTUs

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, cutoff=0.03)

Make shared command

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)

Rarefaction command

rarefaction.single(shared=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.an.unique_list.shared) This generates a file which you can us to check how the diversity is distributed in the dataset.

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)

Why is the cutoff used here?

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)