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Here is a rough outline of the workflow in breseq with proposed additions.

This tutorial was reformatted from the most recent version found here. Our thanks to the previous instructors.

Objectives:

• Use a very self contained/automated pipeline to identify mutations.
• Explain the types of mutations found in a complete manner before using methods better suited for higher order organisms.

Example 1: Bacteriophage lambda data set

First, we'll run breseq on a small data set to be sure that it is installed correctly, and to get a taste for what the output looks like. This sample is a mixed population of bacteriophage lambda that was co-evolved in lab with its E. coli hosts.

Environment

To set your profile up to run breseq, we need to add "module load bowtie/2.1.0" to your profile.

cdh  #move to your home directory
echo "module load bowtie/2.1.0" >> .profile  #this command updates your profile to automatically load the bowtie module

After you've completed these commands, exit lonestar and re log in to re run your profile.  

Data

The data files for this example are in the path:

$BI/ngs_course/lambda_mixed_pop/data

Copy this directory to a new directory called BDIB_breseq in your $SCRATCH space . Name it something other than data. And and cd into it.

cds
mkdir BDIB_breseq_lambda
cp $BI/ngs_course/lambda_mixed_pop/data/* BDIB_breseq_lambda
cd BDIB_breseq_lambda
ls 

If the copy worked correctly you should see  the following 2 files:

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Because this data set is relatively small (roughly 100x coverage of a 48,000 bp genome), a breseq run will take < 5 minutes. Submit this command to the TACC development queue or run on an idev node.

idev  #idev starts an "interactive development" mode which allows you to run computationally intensive tasks
 
breseq -j 12 -r lambda.gbk lambda_mixed_population.fastq > log.txt

A bunch of progress messages will stream by during the breseq run . They detail which would be lost on the compute node if not for the redirection to the log.txt file. The output text details several steps in a pipeline that combines the steps of mapping (using SSAHA2), variant calling, annotating mutations, etc. You can examine them by peeking in the log.txt file as your job runs using tail using tail -f. The -f option means to "follow" the file and keep giving you output from it as it gets bigger. You will need to wait for your job to start running before you can tail -f log.txt.

The command that we used contains several parts with the following explanations:

Looking at breseq predictions

breseq will produce a lot of directories beginning 01_sequence_conversion, 02_reference_alignment, ... Each of these contains intermediate files that can be deleted when the run completes, or explored if you are interested in the inner guts of what is going on. More importantly, breseq will also produce two directories called: data and output.First, which contain files used to create .html output files and .html output files respectively. The most interesting files are the .html files which can't be viewed directly on lonestar. Therefore we first need to copy the output directory back to your desktop computer.

you can then copy paste that information (in the correct position) into the scp command on the desktop's command line:

tar -czvf output.tar.gz output  # the czvf options in order mean Create, Zip, Verbose, Force
pwd

scp -r username@lonestar<username>@lonestar.tacc.utexas.edu:/the/directory/returned/by/pwd/output .

It would be even better practice to archive and gzip the output directory before copying it using tar -cvzf to archive. Then copying that file and using tar -xvzf to unarchive it.

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<the_directory_returned_by_pwd>/output.tar.gz .
tar -xvzf output.tar.gz  # the new "x" option at the front means eXtract 

Navigate to the output directory in the finder and open the a file called index.html. Open this This will open the results in a web browser on your desktop and click around to take a look at the mutation predictions and summary information.window that you can click through different mutations and other information and see the evidence supporting it. The summary page provides useful information about the percent of reads mapping to the genome as well as the overall coverage of the genome. The Mutation Predictions page is where most of the analysis time is spent in determining which mutations are important (and more rarely inaccurate).

Feel free to click around through the different mutations and examine their evidence when you have time, but first start the next breseq run so that it can be in the queue and completing while you look at the data. We will go over the different types of mutations and the evidence for them as a group towards the end of class today, but additional information on analyzing the output can be found at the following reference:

• Deatherage, D.E., Barrick, J.E.. (2014) Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Methods Mol. Biol. 1151:165-188. «PubMed»

Example 2: E. coli data sets

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The data files for this example are in the following path. Go ahead and copy them to a new folder in your $SCRATCH directory called BDIB_breseq_coli_clones:

$BI/ngs_course/ecoli_clones/data

cds
mkdir BDIB_breseq_coli_clones
cp -v $BI/ngs_course/ecoli_clones/data/* BDIB_breseq_coli_clones 

cd BDIB_breseq_coli_clones

Running breseq on TACC

breseq may take an hour or two to run on these sequences, so you should submit to the serialnormal queue instead of the development queue on TACC and should give a run time of 4 3 hours as a conservative estimate.Since  Since we have multiple data sets, this example will also give us an opportunity to run several commands as part of a single job on TACC, and use multiple cores on a single processor.You You'll want each command (line) in the commands file to look something like this:

login1$ breseqbreseq -j 12 -r NC_012967.1.gbk -o output_00K<XX>K SRR030252_1.fastq SRR030252_2.fastq &> <XX>K.log.txt

Notice the additional -o option which specifies that we've added some additional options:

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breseq -j 12 -r NC_012967.1.gbk -o output_00K SRR030252_1.fastq SRR030252_2.fastq &> 00K.log.txt
breseq -j 12 -r NC_012967.1.gbk -o output_02K SRR030253_1.fastq SRR030253_2.fastq &> 02K.log.txt
breseq -j 12 -r NC_012967.1.gbk -o output_05K SRR030254_1.fastq SRR030254_2.fastq &> 05K.log.txt
breseq -j 12 -r NC_012967.1.gbk -o output_10K SRR030255_1.fastq SRR030255_2.fastq &> 10K.log.txt
breseq -j 12 -r NC_012967.1.gbk -o output_15K SRR030256_1.fastq SRR030256_2.fastq &> 15K.log.txt
breseq -j 12 -r NC_012967.1.gbk -o output_20K SRR030257_1.fastq SRR030257_2.fastq &> 20K.log.txt
breseq -j 12 -r NC_012967.1.gbk -o output_40K SRR030258_1.fastq SRR030258_2.fastq 

Once you have your commands file ready, then you need to create your launcher.sge script.

&> 40K.log.txt

launcher_creator.py -q serialnormal -t 43:00:00 ... <your other options>-n launcher -a "UT-2015-05-18" -m "module load bowtie/2.1.0"
qsub launcher.sge

Examining breseq results

As before, copy the data back to your computer and examine the HTML output in a web browser.

Examining breseq results

Exercise: Can you figure out how to archive all of the output directories and copy only those files (and not all of the very large intermediate files) back to your machine? - without deleting any files?

tar -cvzf output.tgz output_*/output

tar -cvzf output.tgz output_*/output

tar -czvf output.tar.gz output_*/output  # the czvf options in order mean Create, Zip, Verbose, Force
pwd

scp -r <username>@lonestar.tacc.utexas.edu:<the_directory_returned_by_pwd>/output.tar.gz .
tar -xvzf output.tar.gz  # the new "x" option at the front means eXtract 

Click around in the results.

Optional: breseq utility commands

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Additionally, the files in the data directory can be loaded in IGV if you copy them back to your desktop.

Optional Exercise: Running breseq in mixed population mode

The phage lambda data set you examined is actually a mixed population of many different phage lambda genotypes descended from a clonal ancestor. You ran breseq in a mode where it predicted consensus mutations in what it thinks is one uniform haploid genome. Actually, some individuals in the population have certain mutations and others do not, so you might have noticed when you looked at some of the alignments that there was a mixture of bases at a position.

We will talk more about analyzing mixed population data to predict rare variants in a later lesson. However, if you're curious you can now experimental with running breseq in a mode where it estimates the frequencies of different mutations in the population. This process is most accurate for single nucleotide variants. Mutations at intermediate frequencies are not (yet) predicted for all classes of mutations like large structural variants.

login1$ breseq --polymorphism-prediction --polymorphism-no-indels -r lambda.gbk lambda_mixed_population.fastq

The option --polymorphism-prediction turns on these mixed population predictions. The option --polymorphism-no-indels turns off predictions of small insertions and deletions (which don't work as well for reasons too complicated to explain here). You're welcome to also try it without this option.

Copy the resulting output directory back to your computer and examine the HTML output in a web browser. Compare it to the output from before.

Optional: Install breseq

We have already installed breseq in $BI/bin for the purpose of this tutorial. You are welcome to continue using it for your own work, or use the installation options we present here to install or update to newer versions as needed.

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