Basic concepts on Linux containers

During this course we will use tools that are stored in a Linux container.

A Linux Container can be seen as a minimal virtual environment that can be used in any Linux-compatible machine.

Using containers is time- and resources-saving as they allow:

• Controlling for software installation and dependencies.
• Reproducibility of the analysis.

For this course, we created a Docker image from this Dockerfile available for download from DockerHub.

This will allow all of us to use exactly the same versions of the tools.

This image can be downloaded and used on computers running Linux/Mac OS (if you have a root access on the computer) or can be converted into another Linux Container called Singularity, which we will be using in this course. Singularity containers are easier to share and to export, and they can be run without root privileges.

The Singularity image is a file that is accessed by the program singularity in order to execute the programs installed inside this image.
This image can be created using the following command (imported from the Docker hub repository and converted on the fly):

# go to the folder created to store the image
cd ~/rnaseq_course/singularity_image

# import image from Docker hub
singularity pull docker://biocorecrg/rnaseq2020:1.0

This creates the rnaseq2020-1.0.simg file.

The image can be accessed/run the following way:

singularity exec -e rnaseq2020-1.0.simg salmon --help

To access the image during the course, we will store the following command in the environmental variable RUN :

# Export the RUN variable
export RUN="singularity exec -e FULL_PATH_TO_HOME/rnaseq_course/singularity_image/rnaseq2020-1.0.simg"
	# Put the full path to your home directory (~ will not work properly)

# Access the salmon program inside the image
$RUN salmon --help