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

Table of Contents

Optional: Install breseq

We have already installed breseq in $BI/bin, so you should be ready to go! Skip this section.

Download breseq from Google code

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module load R

module load GCC
module load bowtie/2.1.0

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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.

breseq -j 12 -r lambda.gbk lambda_mixed_population.fastq > log.txt

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login1$ pwd

desktop1$ scp -r username@lonestar.tacc.utexas.edu:/the/directory/returned/by/pwd/output .

Inside of the output directory is a file called index.html. Open this in a web browser on your desktop and click around to take a look at the mutation predictions and summary information.

Optional Exercise: Running breseq in mixed population mode

The data set you are examining is actually of a mixed population of many different phage lambda genotypes descended from a clonal ancestor. You have run breseq in a mode where it is predicting 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.

As an optional exercise, you can use a somewhat experimental feature of breseq to run 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 classes of mutations like large structural variants.

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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 try it without this option.

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

If you want to know more about identifying polymorphisms in mixed population or pooled samples see: Genome variation in mixed samples (FreeBayes, deepSNV).

Example 2: E. coli data sets

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breseq includes a few utility commands that can be used on any BAM/FASTA set of files to draw an HTML read pileup or a plot of the coverage across a region.

breseq bam2aln NC_012967:237462-237462
breseq bam2cov NC_012967:2300000-2320000

You can It's easiest to run these commands from inside the main output directory (e.g., output_20K) of a breseq run. They use information in the data directory.

breseq bam2aln NC_012967:237462-237462
breseq bam2cov NC_012967:2300000-2320000

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.