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 > 30-fold
- Less than ~1,000 mutations expected
- Detects SNVs and SVs from single-end reads (does not use paired-end distance information)
- Produces annotated HTML output
You can learn a great deal more about breseq by reading the Online Documentation.
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.
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 and cd into it.
If the copy worked correctly you should see the following 2 files:
File Name | Description | Sample |
|---|---|---|
| Single-end Illumina 36-bp reads | Evolved lambda bacteriophage mixed population genome sequencing |
| Reference Genome | Bacteriophage lambda |
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 or run on an idev node.
module load bowtie/2.1.0 #Breseq uses bowtie to map reads, so this module must be loaded before you run breseq breseq -j 12 -r lambda.gbk lambda_mixed_population.fastq > log.txt
A bunch of progress messages will stream by during the breseq run 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 -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:
| part | puprose |
|---|---|
| -j 12 | Use 12 processors (the max available on lonestar nodes) |
| -r lambda.gbk | Use the lambda.gbk file as the reference to identify specific mutations |
| lambda_mixed_population.fastq | breseq assumes any argument not preceded by a - option to be an input fastq file to be used for mapping |
| > log.txt | redirect the output the log.txt file |
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 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.
Navigate to the output directory in the finder and open the a file called index.html. This will open the results in a web browser 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
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 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
File Name | Description | Sample |
|---|---|---|
| Paired-end Illumina 36-bp reads | 0K generation evolved E. coli strain |
| Paired-end Illumina 36-bp reads | 2K generation evolved E. coli strain |
| Paired-end Illumina 36-bp reads | 5K generation evolved E. coli strain |
| Paired-end Illumina 36-bp reads | 10K generation evolved E. coli strain |
| Paired-end Illumina 36-bp reads | 15K generation evolved E. coli strain |
| Paired-end Illumina 36-bp reads | 20K generation evolved E. coli strain |
| Paired-end Illumina 36-bp reads | 40K generation evolved E. coli strain |
| Reference Genome | E. coli B str. REL606 |
Running breseq on TACC
breseq may take an hour to run on these sequences, so you should submit to the normal queue instead of the development queue on TACC and should give a run time of 3 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 (line) in the commands file to look something like this:
breseq -j 12 -r NC_012967.1.gbk -o output_<XX>K SRR030252_1.fastq SRR030252_2.fastq &> <XX>K.log.txt
Notice we've added some additional options:
| part | puprose |
|---|---|
| &> <XX>00K.log.txt | Redirect both the standard output and the standard error streams to a file called <XX>00k.log.txt. It is important that you replace the <XX> to send it to different files, but KEEP the &> as those are telling the command line to send the streams to that file. |
| -o output_<xx>00k | all of those output directories should be put in the specified directory, instead of the current directory. If we don't include this (and chande the <XX>), 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. |
checking command before submitting to queues
control-c to cancel the command and use ls to make sure the output file is created and use tail or cat to make sure that the program is running rather than crashing.
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?
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.
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: 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.
See if you can install breseq and get it running from the installation instructions.
Info for installing breseq on TACC
You do not need to install a compiler (GCC/ICC), bowtie2, or R on TACC as they are available via the module system.
module load R module load GCC module load bowtie/2.1.0
You could add these commands to your $HOME/.profile_user if you wanted them available by default.