eDNA docker

This repository contains a Dockerfile used to build the edna_tools docker image including Rstudio web interface and the following packages:

• tidyverse (ggplot2, dplyr ...)
• dada2
• OBITools
• cutadapt
• MKBRD
• vsearch
• ecoPCR
• ShortRead
• Biostrings
• phyloseq
• ape
• mFD
• worrms
• patchwork
• hillR
• betapart
• ecoPrimers
• seqRFLP

The docker image itself is hosted on dockerhub. It allows to run a container with usefull softwares for eDNA analysis. Independently from your operating system, the container is launched with all the softwares pre-installed in a self-contained linux environment.

Keep in mind that any modifications made inside the container (e.g. packages newly installed or files modified) will be lost after stopping it. Therefore, you need to use the -v argument (or use the dedicated field in docker desktop) to mount the local files inside the container as explained in the tutorial bellow. Modification made to the mounted files will not be lost and you can even create new files from inside the container in the mounted path.

The Rstudio web interface provides an R IDE (Integrated Development Environment) in addition to a terminal to execute any bash command you may need.

Docker tutorial

Docker images can be either used with the command line interface (CLI) after installing docker from your favorite linux package manager or by using the docker desktop UI available on Mac, Windows and Linux.

Command line interface

Start the docker daemon by running:

sudo dockerd

In another terminal, download the image from dockerhub and run it with this single command:

docker run -v /path/to/your/local/file:/path/inside/the/container -p 8787:8787 thesanc/edna_tools

You should now be able to have access to the Rstudio instance by typing localhost:8787 in your browser. You can log in with rstudio both as username and password.

Docker desktop

After unstalling the docker desktop UI, you can search for the thesanc/edna_tools image in the research toolbar:

Once you have found the image, download it and run it by clicking on "run".

A pop-up windows should open to set up the container. Click on "optional settings" to set up the port and paths:

Fill the Host port field with 8787 (you can set up another port if this one is already used).

Fill the Host path with the file path to your files and fill the Container path with the path where these fill should appear in the container. If you are using linux or macos, I recommend to use the Host path as Container path to avoid path related issues when you are running your code inside and outside the container. On windows, you can type in the desired Host path in windows style and type in /home/rstudio for the Container path, since the path should be in unix style.

Now click on "run". You should now be able to have access to the Rstudio instance by typing localhost:8787 (if you have configured the Host port to 8787) in your browser. You can log in with rstudio both as username and password.