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Introduction to Inputs | Griffith Lab

RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

Introduction to Inputs

Module 1 - Key concepts

  • Review central dogma, RNA sequencing, RNAseq study design, library construction strategies, biological vs technical replicates, alignment strategies, etc.

Module 1 - Learning objectives

  • Introduction to the theory and practice of RNA sequencing (RNA-seq) analysis
  • Rationale for sequencing RNA
  • Challenges specific to RNA-seq
  • General goals and themes of RNA-seq analysis work flows
  • Common technical questions related to RNA-seq analysis
  • Getting help outside of this course
  • Introduction to the RNA-seq hands on tutorial

Lecture

Tool Installation | Griffith Lab

RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

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, flexbar, R, ballgown, fastqc and picard-tools. 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., /usr/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.

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/1.9/samtools-1.9.tar.bz2
bunzip2 samtools-1.9.tar.bz2
tar -xvf samtools-1.9.tar
cd samtools-1.9
make
./samtools

bam-readcount

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

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-1.9
git clone https://github.com/genome/bam-readcount.git
cd bam-readcount
cmake -Wno-dev $RNA_HOME/student_tools/bam-readcount
make
./bin/bam-readcount

HISAT2

Installation type: download a precompiled binary.

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/
wget ftp://ftp.ccb.jhu.edu/pub/infphilo/hisat2/downloads/hisat2-2.1.0-Linux_x86_64.zip
unzip hisat2-2.1.0-Linux_x86_64.zip
cd hisat2-2.1.0
./hisat2 -h

StringTie

Installation type: download a precompiled binary.

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-1.3.4d.Linux_x86_64.tar.gz
tar -xzvf stringtie-1.3.4d.Linux_x86_64.tar.gz
cd stringtie-1.3.4d.Linux_x86_64
./stringtie -h

gffcompare

Installation type: download a precompiled binary.

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-0.10.6.Linux_x86_64.tar.gz
tar -xzvf gffcompare-0.10.6.Linux_x86_64.tar.gz
cd gffcompare-0.10.6.Linux_x86_64
./gffcompare

htseq-count

Installation type: use python setup script.

The htseq-count read counting tools is installed below by downloading an archive with wget, unpacking the archive using tar and running a setup script written in Python. After setup, chmod is used to change permissions of the htseq-count file to be executable.

cd $RNA_HOME/student_tools/
wget https://github.com/simon-anders/htseq/archive/release_0.11.0.tar.gz
tar -zxvf release_0.11.0.tar.gz
cd htseq-release_0.11.0/
python setup.py install --user
chmod +x scripts/htseq-count
./scripts/htseq-count -h

TopHat

Installation type: dowload a precompiled binary.

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

cd $RNA_HOME/student_tools/
wget https://ccb.jhu.edu/software/tophat/downloads/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.

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.

cd $RNA_HOME/student_tools/
wget https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.11.8.zip --no-check-certificate
unzip fastqc_v0.11.8.zip
cd FastQC/
chmod 755 fastqc
./fastqc --help

MultiQC

Installation type: use pip.

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

pip install --user multiqc
multiqc --help

Picard

Installation type: download java jar file.

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/2.18.15/picard.jar -O picard.jar
java -jar $RNA_HOME/student_tools/picard.jar

Flexbar

Installation type: download precompiled binary.

cd $RNA_HOME/student_tools/
wget https://github.com/seqan/flexbar/releases/download/v3.4.0/flexbar-3.4.0-linux.tar.gz
tar -xzvf flexbar-3.4.0-linux.tar.gz
cd flexbar-3.4.0-linux/
export LD_LIBRARY_PATH=$RNA_HOME/student_tools/flexbar-3.4.0-linux:$LD_LIBRARY_PATH
./flexbar

Regtools

Installation type: compile from source code using cmake and make.

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.

pip3 install RSeQC
read_GC.py

bedops

Installation type: download precompiled binary.

cd $RNA_HOME/student_tools/
mkdir bedops_linux_x86_64-v2.4.35
cd bedops_linux_x86_64-v2.4.35
wget -c https://github.com/bedops/bedops/releases/download/v2.4.35/bedops_linux_x86_64-v2.4.35.tar.bz2
tar -jxvf bedops_linux_x86_64-v2.4.35.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

R

Installation type: compile source code using make.

This install takes a while, so check if you have R installed already by typing which R. It is already installed on the Cloud, but for completeness, here is how it was done. Please skip all R installation!

#sudo apt-get install r-base-dev
#export R_LIBS=
#cd $RNA_HOME/student_tools/
#wget https://stat.ethz.ch/R/daily/R-patched.tar.gz
#tar -xzvf R-patched.tar.gz
#cd R-patched
#./configure --prefix=$RNA_HOME/student_tools/R-patched/ --with-x=no
#make
#make install
#./bin/Rscript

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.

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

#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.

cd $RNA_HOME/student_tools/
$RNA_HOME/student_tools/bedtools2/bin/bedtools

Questions

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

PATH=$RNA_HOME/student_tools/genePredToBed:$RNA_HOME/student_tools/gtfToGenePred:$RNA_HOME/student_tools/bedops_linux_x86_64-v2.4.35/bin:$RNA_HOME/student_tools/samtools-1.9:$RNA_HOME/student_tools/bam-readcount/bin:$RNA_HOME/student_tools/hisat2-2.1.0:$RNA_HOME/student_tools/stringtie-1.3.4d.Linux_x86_64:$RNA_HOME/student_tools/gffcompare-0.10.6.Linux_x86_64:$RNA_HOME/student_tools/htseq-release_0.11.0/scripts:$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:$RNA_HOME/student_tools/flexbar-3.4.0-linux:$RNA_HOME/student_tools/regtools/build:/home/ubuntu/bin/bedtools2/bin:$PATH

export LD_LIBRARY_PATH=$RNA_HOME/student_tools/flexbar-3.4.0-linux:$LD_LIBRARY_PATH

echo $PATH

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

  1. Using your cursor, navigate down to the “export PATH” commands at the end of the file.
  2. Delete the line starting with PATH using the vi command “dd”.
  3. 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
  4. Press the “esc” key to exit insert mode.
  5. Press the “:” key to enter command mode.
  6. 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 -N https://raw.githubusercontent.com/griffithlab/rnabio.org/master/assets/setup/.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 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