Objectives

In the following example we will run mothur, according to the SOP, on a set of bacterial samples that have had their 16S rRNA gene sequences on a MiSeq.

Setup

First, we need to get our sequences onto flux. Most likely, you had your samples sequenced at the UM Med School and they put your sequences on an MBox. Unless they have changed their setup, you will need to download these sequences to a local machine and then use Globus (recommended) or Cyberduck (fine for only a few samples) to transfer these files onto your FLUX account.

IMPORTANT: You should keep a safe version of these files in a long-term storage location like a flux nfs drive. Then use Globus to transfer a copy to the Scratch drive for processing.

Mothur batch script

Here is a link to an example mothur batch file, following the SOP. Please refer to the mothur wiki for an in depth explanation of the commands.

You can copy it to your current flux location with:

wget https://github.com/DenefLab/flux-tools/raw/gh-pages/scripts/mothur.batch

You will definitely need to edit the following lines of the file:

Change your input, output, and tempdefault locations. Tempdefault signifies a location that mothur will search for file that are not in the input directory. This is a good place to leave your databases for alignment/taxonomy.

set.dir(input=/scratch/lsa_fluxm/michberr/HABS)
set.dir(output=/scratch/lsa_fluxm/michberr/HABS)
set.dir(tempdefault=/scratch/lsa_fluxm/vdenef/Databases/ssu_rRNA/Mothur/)

Change the file argument to a file that contains all your sample names and links to the forward and reverse reads. See the example stability.file in the SOP. Set the processors argument to the number of processors you will request in your PBS script.

make.contigs(file=habs.files, processors=30)

Change the reference argument to the name of the file you want to align to

align.seqs(fasta=current, reference=silva.seed_v119.pcr.v4.unique.align)

Change the reference and taxonomy arguments to the files you are using for your analysis

classify.seqs(fasta=current, count=current, reference=silva.nr_v119.pcr.v4.align, taxonomy=silva.nr_v119.tax, cutoff=60)

Change the groups argument to the names of the mock groups included in your run. They should be separated by a ‘-’

remove.groups(count=current, fasta=current, taxonomy=current, groups=Mock1-Mock2)

You might want to edit several other lines of this file based on the needs of your analysis.

Mothur PBS script

Here is a link to an example PBS script to execute the mothur batch file. You can copy it to your FLUX location the same way as the batch file.

wget https://github.com/DenefLab/flux-tools/raw/gh-pages/scripts/mothur.pbs

You will definitely need to edit the -M line:

#PBS -M your-email

If you are not in the Denef lab, you will need to edit the -A line:

#PBS -A your-account

You might want to edit the -l line with a different memory or walltime allocation based on the size of your dataset. Usually, for ~350 samples of moderate diversity, it takes less than 24 hours on 30 processors.

Run the job

To begin running mothur we need to load the mothur module.

module load med
module load mothur

Now it’s time to execute!

qsub mothur.pbs

We can check our job with:

qstat -u your-uniq-name

Most likely, your script will fail the first few times you try to run it. Usually the error is related to an incorrect path or filename or setting an impossible allocation in your PBS script. Read your output/error file (the file ending in .o and your jobid) to debug.

cat mothur-output-file

Special mothur considerations

Some mothur commands are designed to run very efficiently on a large number of processors. This is where running your job on FLUX will save you a LOT of time over a local machine. However, the OTU picking stage can only run on a single processor and this is usually the most time consuming part of the process. The more diversity you have, the longer this will take. This is good to keep in mind as you are weighing processor and time allocation. Adding 40 processors to your job will only help to a point. You should still add sufficient time for all of your OTUs to be calculated.

The most frustrating issue with mothur I have encountered is when you end up with many strange unclassified OTUs that you know should really be classified. This took us in the Denef lab almost a year to figure out. It turns out that a few extra files are generated when you run the classification step. You need to delete these files in between mothur runs, or move your reference and taxonomy files to a new location, or you will get strange and wrong results. These are the problemmatic files: