Module 0: Introductions and Environment Setup

Lecture

Environment Setup

This tutorial assumes use of a Linux computer with an ‘x86_64’ architecture. The rest of the tutorial should be conducted in a linux Terminal session. In other words you must already be logged into the Amazon EC2 instance as described in the previous section.

Before proceeding you must define a global working directory by setting the environment variable: ‘RNA_HOME’ Log into a server and SET THIS BEFORE RUNNING EVERYTHING.

Create a working directory and set the ‘RNA_HOME’ environment variable

mkdir -p ~/workspace/rnaseq/

export RNA_HOME=~/workspace/rnaseq

Make sure whatever the working dir is, that it is set and is valid

echo $RNA_HOME

Since all the environment variables we set up for the RNA-seq workshop start with ‘RNA’ we can easily view them all by combined use of the env and grep commands as shown below. The env command shows all environment variables currently defined and the grep command identifies string matches.

env | grep RNA

You can place the RNA_HOME variable (and other environment variables) in your .bashrc and then logout and login again to avoid having to worry about it. A .bashrc file with these variables has already been created for you.

In order to view the contents of this file, you can type:

less ~/.bashrc

To exit the file, type q.

Environment variables used throughout this tutorial:

export RNA_HOME=~/workspace/rnaseq
export RNA_DATA_DIR=$RNA_HOME/data
export RNA_DATA_TRIM_DIR=$RNA_DATA_DIR/trimmed
export RNA_REFS_DIR=$RNA_HOME/refs
export RNA_REF_INDEX=$RNA_REFS_DIR/chr22_with_ERCC92
export RNA_REF_FASTA=$RNA_REF_INDEX.fa
export RNA_REF_GTF=$RNA_REF_INDEX.gtf
export RNA_ALIGN_DIR=$RNA_HOME/alignments/hisat2

We will be using picard tools throughout this workshop. To follow along, you will need to set an environment variable pointing to your picard installation.

export PICARD=/home/ubuntu/bin/picard.jar

For simplicity, we are going to download a preconfigured .bashrc file to use.

cd ~
wget http://genomedata.org/rnaseq-tutorial/bashrc_copy
mv bashrc_copy ~/.bashrc
source ~/.bashrc

Now if you run the following command, you should see the RNA environment variables present.

env | grep RNA

Alternatively, you could have add these enivroment variables manually if they were not part of your .bashrc. First, you can open your .bashrc file with nano by simply typing:

nano ~/.bashrc

You can now see the contents of this file. Then, you want to add the above environment variables to the bottom of the file. You can do this by copying and pasting. Once you have the variables in the file, you’ll want to type ctrl + o to save the file, then enter to confirm you want the same filename, then ctrl + x to exit nano.

Again, check all the RNA related environment variables to make sure things look right.

env | grep RNA

Note that if you are doing this course on the Google Cloud Platform instead of AWS, you should instead use this .bashrc file: http://genomedata.org/rnaseq-tutorial/bashrc_copy_gcp.sh

Tool Installation

Note

First, make sure your environment is set up correctly.

Tools needed for this analysis are: samtools, bam-readcount, HISAT2, stringtie, gffcompare, htseq-count, gtf_to_fasta (TopHat), kallisto, FastQC, Fastp, MultiQC, Picard, Regtools, RSeqQC, bedops, gtfToGenePred, genePredToBed, how_are_we_stranded_here, CellRanger, R, BioConductor, ballgown, and other R packages. In the following installation example, the installs are local and will work whether you have root (i.e. admin) access or not. However, if root is available some binaries can/will be copied to system-wide locations (e.g., ~/bin/).

Set up tool installation location:

cd $RNA_HOME
mkdir student_tools
cd student_tools

SAMtools

Installation type: build C++ binary from source code using make. Citation: PMID: 19505943.

The following tool is installed by downloading a compressed archive using wget, decompressing it using bunzip2, unpacking the archive using tar, and building the source code using make to run compiler commands in the “Makefile” provided with the tool. When make is run without options, it attempts the “default goal” in the make file which is the first “target” defined. In this case the first “target” is :all. Once the build is complete, we test that it worked by attempting to execute the samtools binary. Remember that the ./ in ./samtools tells the commandline that you want to execute the samtools binary in the current directory. We do this because there may be other samtools binaries in our PATH. Try which samtools to see the samtools binary that appears first in our PATH and therefore will be the one used when we specify samtools without specifying a particular location of the binary.

cd $RNA_HOME/student_tools/
wget https://github.com/samtools/samtools/releases/download/1.18/samtools-1.18.tar.bz2
bunzip2 samtools-1.18.tar.bz2
tar -xvf samtools-1.18.tar
cd samtools-1.18
make
./samtools

bam-readcount

Installation type: build C++ binary from source code using cmake and make. Citation: PMID: 34341766.

Installation of the bam-readcount tool involves “cloning” the source code with a code version control system called git. The code is then compiled using cmake and make. cmake is an application for managing the build process of software using a compiler-independent method. It is used in conjunction with native build environments such as make (cmake ref). Note that bam-readcount relies on another tool, samtools, as a dependency. An environment variable is used to specify the path to the samtools install.

cd $RNA_HOME/student_tools/
export SAMTOOLS_ROOT=$RNA_HOME/student_tools/samtools-1.18
git clone https://github.com/genome/bam-readcount 
cd bam-readcount
mkdir build
cd build
cmake ..
make
./bin/bam-readcount

HISAT2

Installation type: download a precompiled binary. Citation: PMID: 31375807.

The hisat2 aligner is installed below by simply downloading an archive of binaries using wget, unpacking them with unzip, and testing the tool to make sure it executes without error on the current system. This approach relies on understanding the architecture of your system and downloading the correct precompiled binary. The uname -m command lists the current system architecture.

uname -m
cd $RNA_HOME/student_tools/
curl -s https://cloud.biohpc.swmed.edu/index.php/s/oTtGWbWjaxsQ2Ho/download > hisat2-2.2.1-Linux_x86_64.zip
unzip hisat2-2.2.1-Linux_x86_64.zip
cd hisat2-2.2.1
./hisat2 -h

StringTie

Installation type: download a precompiled binary. Citation: PMID: 25690850.

The stringtie reference guided transcript assembly and abundance estimation tool is installed below by simply downloading an archive with wget, unpacking the archive with tar, and executing stringtie to confirm it runs without error on our system.

cd $RNA_HOME/student_tools/
wget http://ccb.jhu.edu/software/stringtie/dl/stringtie-2.2.1.tar.gz
tar -xzvf stringtie-2.2.1.tar.gz
cd stringtie-2.2.1
make release
./stringtie -h

gffcompare

Installation type: download a precompiled binary. Citation: PMID: 25690850.

The gffcompare tool for comparing transcript annotations is installed below by simply downloading an archive with wget, unpacking it with tar, and executing gffcompare to ensure it runs without error on our system.

cd $RNA_HOME/student_tools/
wget http://ccb.jhu.edu/software/stringtie/dl/gffcompare-0.12.6.Linux_x86_64.tar.gz
tar -xzvf gffcompare-0.12.6.Linux_x86_64.tar.gz
cd gffcompare-0.12.6.Linux_x86_64/
./gffcompare

htseq-count

Installation type: pip install. Citation: PMID: 25260700.

The htseq-count read counting tools is a python package that can be installed using pip.

pip3 install --user HTSeq

TopHat

Installation type: dowload a precompiled binary. Citation: PMID: 19289445.

Note, this tool is currently only installed for the gtf_to_fasta tool used in kallisto section.

cd $RNA_HOME/student_tools/
wget http://genomedata.org/rnaseq-tutorial/tophat-2.1.1.Linux_x86_64.tar.gz
tar -zxvf tophat-2.1.1.Linux_x86_64.tar.gz
cd tophat-2.1.1.Linux_x86_64/
./gtf_to_fasta

kallisto

Installation type: download a precompiled binary. Citation: PMID: 27043002.

The kallisto alignment free expression estimation tool is installed below simply by downloading an archive with wget, unpacking the archive with tar, and testing the binary to ensure it runs on our system.

cd $RNA_HOME/student_tools/
wget https://github.com/pachterlab/kallisto/releases/download/v0.44.0/kallisto_linux-v0.44.0.tar.gz
tar -zxvf kallisto_linux-v0.44.0.tar.gz
cd kallisto_linux-v0.44.0/
./kallisto

FastQC

Installation type: download precompiled binary. Citation: s-andrews/FastQC.

cd $RNA_HOME/student_tools/
wget https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip
unzip fastqc_v0.12.1.zip
cd FastQC/
chmod 755 fastqc
./fastqc --help

Fastp

Installation type: download precompiled binary. Citation: PMID: 30423086

cd $RNA_HOME/student_tools/
mkdir fastp
cd fastp
wget http://opengene.org/fastp/fastp
chmod a+x ./fastp
./fastp

MultiQC

Installation type: use pip. Citation: PMID: 27312411.

Multiqc, a tool for assembling QC reports is a python package that can be installed using the python package manager pip.

pip3 install multiqc
export PATH=/home/ubuntu/.local/bin:$PATH
multiqc --help

Picard

Installation type: download java jar file. Citation: broadinstitute/picard.

Picard is a rich tool kit for BAM file manipulation that is installed below simply by downloading a jar file. The jar file is tested using Java, a dependency that must also be installed (it should already be present in many systems).

cd $RNA_HOME/student_tools/
wget https://github.com/broadinstitute/picard/releases/download/2.26.4/picard.jar -O picard.jar
java -jar $RNA_HOME/student_tools/picard.jar

RegTools

Installation type: compile from source code using cmake and make. Citation: bioRXiv: 10.1101/436634v2.

cd $RNA_HOME/student_tools/
git clone https://github.com/griffithlab/regtools
cd regtools/
mkdir build
cd build/
cmake ..
make
./regtools

RSeQC

Installation type: use pip. Citation: PMID: 22743226.

pip3 install RSeQC
read_GC.py

bedops

Installation type: download precompiled binary. Citation: PMID: 22576172.

cd $RNA_HOME/student_tools/
mkdir bedops_linux_x86_64-v2.4.41
cd bedops_linux_x86_64-v2.4.41
wget -c https://github.com/bedops/bedops/releases/download/v2.4.41/bedops_linux_x86_64-v2.4.41.tar.bz2
tar -jxvf bedops_linux_x86_64-v2.4.41.tar.bz2
./bin/bedops

gtfToGenePred

Installation type: download precompiled binary.

cd $RNA_HOME/student_tools/
mkdir gtfToGenePred
cd gtfToGenePred
wget -c http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/gtfToGenePred
chmod a+x gtfToGenePred
./gtfToGenePred

genePredToBed

Installation type: download precompiled binary.

cd $RNA_HOME/student_tools/
mkdir genePredToBed
cd genePredToBed
wget -c http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/genePredToBed
chmod a+x genePredToBed
./genePredToBed

how_are_we_stranded_here

pip3 install git+https://github.com/kcotto/how_are_we_stranded_here.git
check_strandedness

Install Cell Ranger

Must register to get download link, modify command below to match downloaded tar

cd $RNA_HOME/student_tools/
wget `download_link`
tar -xzvf cellranger-7.2.0.tar.gz

Install R

#sudo apt-get remove r-base-core

#wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | sudo gpg --dearmor -o /usr/share/keyrings/r-project.gpg
#echo "deb [signed-by=/usr/share/keyrings/r-project.gpg] https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/" | sudo tee -a /etc/apt/sources.list.d/r-project.list
#sudo apt update
#sudo apt install --no-install-recommends r-base

Note, if X11 libraries are not available you may need to use --with-x=no during config, on a regular linux system you would not use this option. Also, linking the R-patched bin directory into your PATH may cause weird things to happen, such as man pages or git log to not display. This can be circumvented by directly linking the R* executables (R, RScript, RCmd, etc.) into a PATH directory.

R Libraries

Installation type: add new base R libraries to an R installation.

For this tutorial we require:

launch R (enter R at linux command prompt) and type the following at an R command prompt. NOTE: This has been pre-installed for you, so these commands can be skipped.

#R
#install.packages(c("devtools","dplyr","gplots","ggplot2"),repos="http://cran.us.r-project.org")
#quit(save="no")

Bioconductor

Installation type: add bioconductor libraries to an R installation. Citation: PMID: 15461798.

For this tutorial we require:

launch R (enter R at linux command prompt) and type the following at an R command prompt. If prompted, type “a” to update all old packages. NOTE: This has been pre-installed for you, so these commands can be skipped.

#R
#source("http://bioconductor.org/biocLite.R")
#biocLite(c("genefilter","ballgown","edgeR","GenomicRanges","rhdf5","biomaRt"))
#quit(save="no")

Sleuth

Installation type: R package installation from a git repository. Citation: PMID: 28581496.

#R
#install.packages("devtools")
#devtools::install_github("pachterlab/sleuth")
#quit(save="no")

PRACTICAL EXERCISE 1 - Software Installation

Assignment: Install bedtools on your own. Make sure you install it in your tools folder. Download, unpack, compile, and test the bedtools software. Citation: PMID: 20110278.

cd $RNA_HOME/student_tools/

Hint: google “bedtools” to find the source code
Hint: there is a README file that will give you hints on how to install
Hint: If your install has worked you should be able to run bedtools as follows:

$RNA_HOME/student_tools/bedtools2/bin/bedtools

Questions

What happens when you run bedtools without any options?
Where can you find detailed documentation on how to use bedtools?
How many general categories of analysis can you perform with bedtools? What are they?

Solution: When you are ready you can check your approach against the Solutions

Add locally installed tools to your PATH [OPTIONAL]

To use the locally installed version of each tool without having to specify complete paths, you could add the install directory of each tool to your ‘$PATH’ variable and set some other environment variables:

PATH=$RNA_HOME/student_tools/genePredToBed:$RNA_HOME/student_tools/gtfToGenePred:$RNA_HOME/student_tools/bedops_linux_x86_64-v2.4.41/bin:$RNA_HOME/student_tools/samtools-1.18:$RNA_HOME/student_tools/bam-readcount/bin:$RNA_HOME/student_tools/hisat2-2.2.1:$RNA_HOME/student_tools/stringtie-2.2.1:$RNA_HOME/student_tools/gffcompare-0.12.6.Linux_x86_64:$RNA_HOME/student_tools/tophat-2.1.1.Linux_x86_64:$RNA_HOME/student_tools/kallisto_linux-v0.44.0:$RNA_HOME/student_tools/FastQC:$RNA_HOME/student_tools/fastp:$RNA_HOME/student_tools/regtools/build:/home/ubuntu/bin/bedtools2/bin:/home/ubuntu/.local/bin:$PATH

echo $PATH

export PICARD=$RNA_HOME/student_tools/picard.jar

You can make these changes permanent by adding the above lines to your .bashrc file use a text editor to open your bashrc file. For example:

vi ~/.bashrc

Vi instructions

Using your cursor, navigate down to the “export PATH” commands at the end of the file.
Delete the line starting with PATH using the vi command “dd”.
Press the “i” key to enter insert mode. Go to an empty line with you cursor and copy paste the new RNA_HOME and PATH commands into the file
Press the “esc” key to exit insert mode.
Press the “:” key to enter command mode.
Type “wq” to save and quit vi

If you would like to learn more about how to use vi, try this tutorial/game: VIM Adventures

NOTE: If you are worried your .bashrc is messed up you can redownload as follows:

cd ~
wget http://genomedata.org/rnaseq-tutorial/bashrc_copy
mv bashrc_copy ~/.bashrc
source ~/.bashrc

Installing tools from official ubuntu packages [OPTIONAL]

Some useful tools are available as official ubuntu packages. These can be installed using the linux package management system apt. Most bioinformatic tools (especially the latest versions) are not available as official packages. Nevertheless, here is how you would update your apt library, upgrade existing packages, and install an Ubuntu tool called tree.

#sudo apt-get update
#sudo apt-get upgrade
#sudo apt-get install tree
#tree

Installing tools by Docker image

Some tools have complex dependencies that are difficult to reproduce across systems or make work in the same environment with tools that require different versions of the same dependencies. Container systems such as Docker and Singularity allow you to isolate a tool’s environment giving you almost complete control over dependency issues. For this reason, many tool developers have started to distribute their tools as docker images. Many of these are placed in container image repositories such as DockerHub. Here is an example tool installation using docker.

Install samtools:

docker pull biocontainers/samtools:v1.9-4-deb_cv1
docker run -t biocontainers/samtools:v1.9-4-deb_cv1 samtools --help

Install pvactools for personalized cancer vaccine designs:

#docker pull griffithlab/pvactools:latest
#docker run -t griffithlab/pvactools:latest pvacseq --help

Installing tools by Docker image (using Singularity)

Some systems do not allow docker to be run for various reasons. Sometimes singularity is used instead. The equivalent to the above but using singularity looks like the following:

#singularity pull docker://griffithlab/pvactools:latest
#singularity run docker://griffithlab/pvactools:latest pvacseq -h

Note that if you encounter errors with /tmp space usage or would like to control where singularity stores its temp files, you can set the environment variables:

#export SINGULARITY_CACHEDIR=/media/workspace/.singularity
#export TMPDIR=/media/workspace/temp