Introduction

breseq is a tool developed by the Barrick lab intended for analyzing genome re-sequencing data for bacteria. It is primarily used to analyze laboratory evolution experiments with microbes. In these experiments, there is usually a high-quality reference genome for the ancestral strain, and one is interested in exhaustively finding all of the mutations that occurred during the evolution experiment. Then one might want to construct a phylogenetic tree of individuals samples from a single population or determine whether the same gene is mutated in many independent evolution experiments in an environment.

Input data / expectations:

• Haploid reference genome
• Relatively small (<20 Mb) reference genome
• Input FASTQ reads can be from any sequencing technology
• Average genomic coverage > 20-fold
• Less than ~1,000 mutations expected
• Detects SNVs and structural variants from single-end reads
• Produces annotated HTML output

You can learn a great deal more about breseq by reading the Online Documentation.

Install breseq

Download breseq from Google code

See if you can install breseq and get it running from the installation instructions.

module load R

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.

Data

The data files for this example are in the path:

/corral-repl/utexas/BioITeam/ngs_course/lambda_mixed_pop/data

Copy this directory to your $SCRATCH space. Name it something other than data. And cd into it.

Running breseq

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.

login1$ breseq -r lambda.gbk lambda_mixed_population.fastq > log.txt

A bunch of progress messages will stream by during the breseq run. They detail 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.

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.

breseq will also produce two directories called: data and output.

First, copy the output directory back to your desktop computer.

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 an 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.

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 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

Now we'll try running breseq on some Escherichia coli genomes from an evolution experiment. These files are larger. You don't want to run them in interactive mode. We'll submit them to the TACC queue all at once.

Data

The data files for this example are in the path:

/corral-repl/utexas/BioITeam/ngs_course/ecoli_clones/data

Running breseq on TACC

breseq may take an hour or two to run on these sequences, so you should submit to the serial queue instead of the development queue on TACC and should give a run time of 4 hours as a conservative estimate.

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'll want each command to look something like this:

login1$ breseq -r NC_012967.1.gbk -o output_00K SRR030252_1.fastq SRR030252_2.fastq

Notice the additional -o option which specifies that all of those output directories should be put in the specified directory, instead of the current directory. If we don't include this, then we will end up writing the output from all of the runs on top of one other. The program will undoubtedly get confused, possibly crash, and generally it will be a mess.

Hint: It is often a good idea to try running a command that you are about to submit to the TACC queue yourself, just to be sure you have all the options and paths correct. Otherwise you will have to wait until it starts running on TACC in order to find out that it it failed immediately, which can be frustrating. Try running the command above. You can use control-c to cancel the command after you're sure it started ok.

breseq -r NC_012967.1.gbk -o output_00K SRR030252_1.fastq SRR030252_2.fastq
breseq -r NC_012967.1.gbk -o output_02K SRR030253_1.fastq SRR030253_2.fastq
breseq -r NC_012967.1.gbk -o output_05K SRR030254_1.fastq SRR030254_2.fastq
breseq -r NC_012967.1.gbk -o output_10K SRR030255_1.fastq SRR030255_2.fastq
breseq -r NC_012967.1.gbk -o output_15K SRR030256_1.fastq SRR030256_2.fastq
breseq -r NC_012967.1.gbk -o output_20K SRR030257_1.fastq SRR030257_2.fastq
breseq -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.

launcher_creator.py -q serial -t 4:00:00 ... <your other options>
qsub launcher.sge

Examining breseq results

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

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

Click around in the results.

Optional: breseq utility commands

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 run these commands from inside the main output directory (e.g., output_20K) of a breseq run. They use information in the data directory.

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