Using FreeBayes and deepSNV to call variants in mixed populations

In this tutorial we will use two different programs to identify variants in mixed genomic samples where DNA from many individuals was pooled and sequenced together.

1. FreeBayes can be used to call variants in genomes of any ploidy, pooled samples, or mixed populations. It can be used on single samples.
2. deepSNV can be used to call single-nucleotide variants and single-base deletions in ultra-deep sequencing data sets. It compares variation between two samples.

Install FreeBayes

This tutorial assumes that you have created the paths $WORK/src and $HOME/local/bin and added $HOME/local/bin to your $PATH. FreeBayes uses a git repository and requires the cmake build system to compile. You can install it with these commands:

...

This won't work on Lonestar! (You aren't an admin.) However, the make command already created the executables inside of the bin directory in the source tree, so we can find and move them to our standard $HOME/local/bin directory with the last command.

Install deepSNV

The newest version of R on lonestar is currently 2.14, but deep SNV deepSNV requires R version 2.15.

You can install your own version of R 2.15 on TACC using the instructions below, but this takes a while to compile, so you can also just add this a location to your path by adding this line to your ~/.profile_user file. We $PATH where we have installed R in this location2.15:

export PATH="/corral-repl/utexas/BioITeam/ngs_course/local/bin:$PATH"

...

login1$ R
...
> source("http://bioconductor.org/biocLite.R")
> biocLite("deepSNV")

Example 1: Mixed E. coli population

A mixed population of E. coli from an evolution experiment was sequenced at several different time points (PMID: 19838166 , PMID:19776167). At generation 0 it consisted of a clone (cells grown from a colony with essentially no genetic variation), then additional samples were taken at after 20K and 40K generations after which mutations arose and swept through the (asexual) population(roughly 10 and 20 years) of culture in the laboratory. By these later times new mutations had arisen. Some had taken over (swept to fixation in) the population and were present in 100% of the individuals. Some mutations were only present in certain subpopulations and thus they were are intermediate frequencies (like 25%) in the sample.

Data

The data files for this example are in the path:

...

We renamed the FASTA sequence from "gi|254160123|ref|NC_012967.1|" to "NC_012967" by changing the first line of the NC_012967.1.fasta sequence using a text editor. It's just easier to deal with the shorter name.

Map Reads

Choose an appropriate program and map the reads. As for other variant callers, convert the mapped reads to BAM format, then sort and index the BAM file.

Additional exercises

• Determine the approximate depth of mapped read coverage for each sequencing data set.
• Try using different mappers or changing the default alignment settings to find more variants.

Run FreeBayes

FreeBayes can be used to treat the sample as a mixture of pooled samples. (In our case it is actually a mixture of >1 million bacteria, but we have nowhere near that level of coverage, so we give an arbitrary mixed ploidy of 100, which means we use a statistical model that predicts variants only with frequencies of 1%, 2%, 3%, ... 98%, 99%, 100%). This command runs pretty fast, so you can do it in interactive mode.

login1$ freebayes --min-alternate-total 3 --ploidy 100 --pooled --vcf SRR032374.vcf \
        --fasta-reference NC_012967.1.fasta SRR032374.sorted.bam

Nice! We now have a familiar VCF file.

Additional exercises

• Write a script or use a linux command to filter the output files to only contain variants that are predicted to have frequencies > 0.10 and < 0.90 or scores > 100.
• What does the --min-alternate-total option mean. Experiment with using other options and examine how they affect speed versus what is predicted. Beware that some choices of options can cause crashes.
• Compare the variants predicted in samples SRR032374 and SRR032376. You could view them in IGV.

Run deepSNV

deepSNV runs more slowly, so we will only look at a small region of the genome initially in interactive mode. (Why is it slower? Probably in part due to using a more sophisticated statistical model and in part because it is implemented in R instead of pure C.)

...

• deepSNV website
• deepSNV paper ... but UT library does not have access to this journal...
• News article about deepSNV
• deepSNV R module vignette

login1$ R
...
> library("deepSNV")
> regions <- data.frame(chr="NC_012967", start = 1, stop=100000)
> result = deepSNV(test = "SRR032376.sorted.bam", control = "SRR030252.sorted.bam", regions=regions)
> sig_result <- summary(result, sig.level=0.05, adjust.method="BH")
> pdf("SRR032374.pdf")
> plot(mix)
> dev.off()
> write.csv(sig_result, "SRR032374")

The output is as a CSV (comma-separated) table, that can be loaded in R, Excel, or the like.

You have created a nice little plot SRR032374.pdf that you can copy back to your computer for viewing that compares allele frequencies between the samples.

Additional exercises

• Create an R script to run from the command line to execute these commands, and try running the entire E. coli genome as a job on TACC. You might need to know that you can run an R script (script.r) from the command using one of these command lines:

  Code Block
  Rscript script.r R --vanilla < script.r

• Repeat for SRR032374. Compare the variants predicted in samples SRR032374 and SRR032376.