rnabioco/mzt-introns repository overview

⁠Using docker to run snakemake pipeline

⁠Install docker

MacOS: Install Docker desktop here: https://docs.docker.com/desktop/mac/install/⁠

Windows: If you do not have the WSL enabled or installed, following the instructions here: https://docs.microsoft.com/en-us/windows/wsl/install⁠

Then download and install Docker desktop for windows: https://docs.docker.com/desktop/windows/install/⁠

Open the docker application to confirm installation.

⁠Initialize container

The docker container also contains Rstudio to enable exploration of the data in rstudio. To start the docker image provide a password string, which will be used for logging into rstudio if desired.

Get the image

docker pull rnabioco/mztintrons

Start a container

docker run \
  -e PASSWORD=rna \ # password used to login into rstudio if desired
  -p 8787:8787 \ # port that rstudio will be active on, navigate browser to http://localhost:8787/ to login
  -v /path/to/mztintrons:/home/rstudio/ \ # path to local directory with pipeline and data 
  -it \ 
  rnabioco/mzt-introns

Once you have a container running, you can get access a bash shell to run snakemake in two ways:

1. login to rstudio, and use the terminal in the IDE (e.g. navigate to http://localhost:8787/⁠, then login with username rstudio, password rna)

2. start a terminal in the docker container.

docker run -it -v /path/to/mztintrons:/home/rstudio/ rnabioco/mzt-introns bash

⁠Test snakemake

snakemake -npr --configfile config-test.yaml

⁠Additional resources

If you’ve never used docker here are some useful tutorials on using docker:

https://bioconductor.org/help/docker/#quickstart⁠
https://jsta.github.io/r-docker-tutorial/⁠
https://replikation.github.io/bioinformatics_side/docker/docker/#important-commands⁠

⁠Software versions

The software versions used in this docker container are listed in the environment.yaml file (python + command line tools) and in the renv.lock file (R packages). These files can be used to install the software locally, outside the docker container, using conda and renv respectively.