GT-Plotting Documentation

Background

This tutorial plots the genotypic similarities and differences between individuals aligned with to reference genome extracted from a vcf file. A vcf.gz file is used as input, and it outputs .png files, depicting chromosome number, chromosome position, and genotype.

Note

This is run using Ubuntu 18.04 Bionic Beaver with R version 3.6.0. For different Ubuntu distributions, download and install the appropriate software/packages.

Code ran on Linux terminal is preceded by $, while code ran on RStudio is preceded by >.

Installing conda and Setting Up Channels

It is recommended to install miniconda3 and create a conda environment to install all necessary packages and dependencies without affecting the system. Miniconda is a free minimal installer for conda. It is a small, bootstrap version of Anaconda that includes only conda, Python, the packages they depend on, and a small number of other useful packages, including pip, zlib and a few others. Use the conda install command to install 720+ additional conda packages from the Anaconda repository.

Open a new terminal (Ctrl + Alt + T) and make sure that the system is up-to-date,

$ sudo apt-get update

Download the latest package from the ‘Miniconda Webpage and install it with,

$ curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
$ sh Miniconda3-latest-Linux-x86_64.sh

It may be possible that Python 2.7 was installed with miniconda3. In order to use a newer version of Python, install the preferred Python version and update conda to resolve any dependency failures,

$ conda install -c anaconda python=3.7
$ conda update --all

Setup the appropriate bioconda channels. Make sure to run the following commands exactly in this order,

$ conda config --add channels defaults
$ conda config --add channels bioconda
$ conda config --add channels conda-forge

Bioconda is now enabled, so you can install any packages and versions available on the bioconda channel, such as

$ conda install bwa bowtie bcftools=1.9

Creating a conda environment

Create a new conda environment with:

$ conda create --name <environment-name>

Substitute <environment-name> with the preferred name you want. As an example,

$ conda create --name R-Data-Science

Once the newly created environment has been installed, activate it with,

$ conda activate R-Data-Science

Installing R and RStudio

In order to install R and RStudio, it is recommended to run the installation with the use of conda. To install R, input the following,

$ conda install rstudio

By directly installing RStudio, conda will install all the necessary dependencies, including the appropriate version of R. Once the installation is complete, open RStudio in the terminal,

$ rstudio

RStudio will open a new window where R scripts can be run.

Installing and Loading the Necessary R Packages

In RStudio, create a new R script and begin developing it by going to File>New File>R Script. In order to open the genome vcf.gz files, unzip and tar are required. The system might have multiple directories containing unzip and tar.

> getOption("unzip")
> options(unzip = "/usr/bin/unzip")
>
> Sys.getenv("TAR")
> Sys.setenv(TAR = "/bin/tar")

First install and load devtools, which will help install the corresponding dependencies and GitHub libraries later on,

> install.packages(“devtools”)
> library(devtools)

Install the necessary dependencies using BiocManager,

> BiocManager::install(c("VariantAnnotation"))

If the above did not run, it may be needed to install first BiocManager and then run the above again,

> install.packages("BiocManager")

Install the libraries vcfR and GT-Plotting from their respective GitHub repositories,

> devtools::install_github("knausb/vcfR")
> devtools::install_github("AnzaGhaffar/GT-Plotting")

Load the installed dependencies and GitHub libraries,

> library(tidyr)
> library(ggplot2)
> library("VariantAnnotation")
> library("vcfR")
> library("GTPlotting")

Pointing towards the vcf File

RStudio will need to know where the vcf file is. Specify the path and filename,

> path <- "/path/to/directory/with/vcf/file/"
> setwd(path)
> vcffilename <- "name_of_vcf_file.vcf.gz"

The next three steps consist of running functions to plot the genotypes.

VcfToTable Function

VcfToTable takes as input a vcf file with extension .vcf or .vcf.gz and creates an object that consists of two data frames,

> vcf_testdata<-VcfToTable(vcffilename)

Then, the important features are extracted from the vcf file for the genotype plotting using the vcfdata data frame,

> vcf_testdata$vcfdata

Running the above will output the CHROM, POS, REF, ALT, QUAL, INDVL1, and INDVL2. A data frame is created by running,

> vcf_testdata$chromelen

The output looks like below with chromosome number and size,

    chromosome   size
NC_018051.1     16
NC_040279.1  38450
NC_040280.1  34390
NC_040281.1  54830
NC_040282.1 193987
NC_040283.1 125079
NC_040284.1  36664
NC_040285.1 104691
NC_040286.1  58685
NC_040287.1  83639
NC_040288.1 276550
NC_040289.1  52588

GTPlotting_Chromosome Function

This function plots the genotype of each chromosome. It takes three inputs the vcf data frame generated by the VcfToTable function, the chromosome length table generated by the VcfToTable function, and the name of the control sample should be same as in the vcf file,

> GTPlotting_Chromosome(vcf_testdata$vcfdata,vcf_testdata$chromelen,'Grinkan_CTRL')

“GTPlotting_Chromosome” output. (click to expand)

GTPlotting_Chromosome_Combined Function

This function plots the genotype of all the chromosomes. It takes two inputs the vcf data frame generated by the VcfToTable function and the chromosome length table generated by the VcfToTable function,

> GTPlotting_Chromosome_Combined(vcf_testdata$vcfdata,vcf_testdata$chromelen)

GTPlotting_Chromosome_Combined” output. (click to expand)