Chapter 2 Prerequisites

2.1 Learning Objectives

This chapter will cover the prerequisites for this course, including:

Installing Docker
Installing R Studio
Downloading data files

2.2 Docker

For the purpose of this course, we will be using Docker to run pVACseq and pVACfuse. Docker is a tool that is used to automate the deployment of applications in lightweight containers so that applications can work efficiently in different environments in isolation. We provide versioned Docker containers for all pVACtools releases via Docker Hub using the griffithlab/pvactools image name.

In order to use Docker, you will to download the Docker Desktop software. Please ensure you select the correct install package for your operating system.

2.3 Terminal

We will be running Docker from the command line on your preferred terminal using the Docker command line interface (CLI). The Docker CLI is already included with Docker Desktop. Most operating systems already come with a Terminal application. If yours doesn’t, you will need to first install one.

2.4 R Studio and R package dependencies

In order to use pVACview, you will need to download R. Please refer here for downloading R (version 3.5 and above required). You may also take the additional step of downloading R studio if you are not familiar with launching R Shiny from the command line.

Additionally, there are a number of packages you will need to install in your R/R studio:

install.packages("shiny", dependencies=TRUE)
install.packages("ggplot2", dependencies=TRUE)
install.packages("DT", dependencies=TRUE)
install.packages("reshape2", dependencies=TRUE)
install.packages("jsonlite", dependencies=TRUE)
install.packages("tibble", dependencies=TRUE)
install.packages("tidyr", dependencies=TRUE)
install.packages("plyr", dependencies=TRUE)
install.packages("dplyr", dependencies=TRUE)
install.packages("shinydashboard", dependencies=TRUE)
install.packages("shinydashboardPlus", dependencies=TRUE)
install.packages("fresh", dependencies=TRUE)
install.packages("shinycssloaders", dependencies=TRUE)
install.packages("RCurl", dependencies=TRUE)
install.packages("curl", dependencies=TRUE)
install.packages("string", dependencies=TRUE)
install.packages("shinycssloaders", dependencies=TRUE)

2.5 Data

For this course, we have put together a set of input data generated from the breast cancer cell line HCC1395 and a matched normal lymphoblastoid cell line HCC1395BL. Data from this cell line is commonly used as test data in bioinformatics applications. For more information on these lines and the generation of test data, please refer to the data section of our precision medicine bioinformatics course.

The input data consists of the following files:

For pVACseq:

annotated.expression.vcf.gz: A somatic (tumor-normal) VCF and its tbi index file. The VCF has been annotated with VEP and has coverage and expression information added. It has also been annotated with custom VEP plugins that provide wild type and mutant versions of the full length protein sequences predicted to arise from each transcript annotated with each variant.
phased.vcf.gz: A phased tumor-germline VCF and its tbi index file to provide information about in-phase proximal variants that might alter the predicted peptide sequence around a somatic mutation of interest.
optitype_normal_result.tsv: A OptiType file with HLA allele typing predictions.

For more detailed information on how the variant input file is created, please refer to the input file preparation section of the pVACtools docs.

For pVACfuse:

agfusion_results: An AGFusion output directory with annotated fusion calls.
star-fusion.fusion_predictions.tsv: A STARFusion prediction file with fusion read support and expression information.

General:

Homo_sapiens.GRCh38.pep.all.fa.gz: A reference proteome peptide FASTA to use for determining whether there are any reference matches of neoantigen candidates.

To download this data, please run the following commands:

wget https://raw.githubusercontent.com/griffithlab/pVACtools_Intro_Course/main/HCC1395_inputs.zip
unzip HCC1395_inputs.zip

This course will not cover the required pre-processing steps for the pVACtools input data but extensive instructions on how to prepare your own data for use with pVACtools can be found at pvactools.org.