This course focuses on basics of data processing and analysis of bulk short read RNA-Sequencing data. Whie we will mention single cell RNA-Seq and long read sequencing these topics will not be covered in depth. The first half of the class will establish he theoretical foundations of RNA-Seq analysis and build the necessary knowledge and glossary. We will introduce different analysis types one can perform using RNA-Seq data and mention common mistakes, method pitfalls and other gotchas. In the second half of the lecture, we will perform simple single condition (WT vs KO) differential expression and GO term enrichment analysis from start to finish. All the files that are needed and will be generated are in the google drive folder shared. The files that are shared are small enough to be downloaded full but the only ones that are absolutely required and cannot be downloaded form elsewhere are the reads folder. This course assumes that the participants have some familiarity with R and the Linux shell. The data is small enough be run on a personal computer and each step will complete in a few minutes at most. We will go through each command and discuss the reason for including/not including certain parameters in depth in the second half. There are a few software packages that need to be installed beforehand to be able to run all the commands. For Linux and mac users all software can be installed using apt-get and brew respectively. For windows users, please install WSL2 (windows subsystem for Linux) and follow the Linux commands form there. The participants are not required to follow along the code if they choose not to. We will be sharing all the material to assist participants in their own analyses. We will briefly discuss how to structure these analyses for an HPC environment and how to import necessary programs beforehand. For those who want to install the necessary packages:
For Linux, most distributions come with python3 already installed, for some reason you do not have that Install python with:
sudo apt install python3
# and install pip with
python3 -m ensurepip --upgrade
# install multiqc
python3 -m pip install multiqc --user
# install other dependencies
sudo apt install samtools, rsem, openjdk-17-jdk openjdk-17-jreFor MacOS first install brew:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
#then install the other stuff
brew install python
python -m ensurepip --upgrade
python -m pip install multiqc
brew install samtool
brew install picard-toolsFor rsem follow the instructions on their github profile. Please not that these are not required and any hpc system will either have these already installed as modules or they can be installed upon request by your system administrators.
Clone this repository
git clone https://github.com/celalp/compute_ontario_rna_seq
cd compute_ontario_rna_seqIn this git repository I have a Dockerfile, you can create your own container using the installation instructions on the Docker website. You can use this to run the commands in real time along with the instructions. If you want you can create a container using docker build -t celalp/compute_ontario_rna_seq:latest or pull from the dockerhub using docker pull celalp/compute_ontario_rna_seq:latest. However, this requires that you have a computer that you have sudo/administrator priviliges.
If you do not have this option you can use apptainer or singularity. After intallation you can pull the container from the docker hub.
docker run -v .:/data --rm -it --entrypoint bash celalp/compute_ontario_rnaseq:latestThis will create an interactive bash session. All the necassary dependencies (except for data) should be in the container.
# pull the container from dockerhub and convert to .sif
singularity pull rnaseq.sif docker://celalp/compute_ontario_rnaseq:latest
# start an interactive bash session
singularity shell rnaseq.sif