cristaniguti/poly_qtl repository overview

⁠Why should you use these docker containers?

• If you do not want to install every software needed for the Polyploid Training Workshop
• If you want to run every software via RStudio in your web browser
• If you want to use a cluster to run the analysis

All images will run on top of your OS kernel, so they are cross-platform enabled, do not need any configuration, and are ready to go.

⁠What will you find in this repository?

• Docker installation instructions⁠

• Images to run self-contained containers with the following software:

  • SuperMASSA/VCF2SM⁠
  • polyRAD⁠
  • fitPoly⁠
  • MAPpoly⁠
  • polymapR⁠
  • PolyOrigin⁠
  • QTLpoly⁠
  • polyqtlR⁠
  • diaQTL⁠
  • GWASpoly⁠

⁠Installing Docker

Linux users: use the following instructions to Install Docker on Ubuntu⁠, or find your distribution here⁠.

Windows users: download the Docker Desktop Installer for Windows⁠ and follow the Installation instructions for Windows⁠.

Mac users: download the Docker Desktop Installer for Mac⁠ and follow the Installation instructions for Mac⁠.

Cluster users: most computing clusters already have Docker or Singularity installed - you can jump to the next session, download, and run the images. If your cluster does not have any image rendering system, please contact your system administrator. This information⁠ may help you to elaborate a request.

Docker requires administrator permissions to run. If you do not have these permissions you can use singularity⁠. You can access here⁠ some instructions about how to use docker images with singularity.

⁠Docker useful commands

After installing docker here are some useful commands:

# Dowload images and run containers
docker pull                               # Get image from a registry
docker run                                # run a command in a new container
docker run -it -v                         # run in an interactive mode and transferring directory to the container environment

# Delete container
docker ps                                 # list all containers available on your computer, search the container you want to delete
docker stop <cointainer_id>               # before delete you should stop it
docker rm <cointainer_id>                 # delete container using the same ID

# Delete image
docker images                             # list all images available on your computer, search the image ID that you want to delete
docker rmi <image_id>                     # delete the image using the same ID

# Delete all images and containers
docker stop $(docker ps -a -q)            # stop all running containers
docker rm $(docker ps -a -q)              # remove all stopped containers
docker rmi $(docker images -q)            # remove all images

⁠Genotype calling software

• Docker Hub image: cristaniguti/poly_genocalls⁠

Image contents

• Ubuntu 20.04
• R 4.0.3
• RStudio 1.3
• Python 2.7
• SuperMASSA
• VCF2SM
• polyRAD
• fitPoly

Running instructions

Download the container image: open a terminal and run: docker pull cristaniguti/poly_genocalls

Then start the image by running the following commands (feel free to choose another port different than 8787):

# Use VCF2SM
docker run -v $(pwd):/opt cristaniguti/poly_genocalls python /scripts/VCF2SM.py -i /opt/example.vcf -o /opt/example_poly.vcf -S /scripts/SuperMASSA.py -I hw -a RA/AA -r 1:84 -d 15 -D 500 -M 4:6 -f 4 -p 0.80 -n 0.90 -c 0.75 -t 1

# Access the RStudio
docker run -p 8787:8787 -v $(pwd):/home/rstudio/ -e DISABLE_AUTH=true cristaniguti/poly_genocalls

Keep this terminal open and access http://localhost:8787/ or http://127.0.0.1:8787/ in your web browser. You can also run the VCF2SM using the RStudio terminal, but do not forget the path of the scripts inside the container: /scripts/SuperMASSA.py and /scripts/VCF2SM.py. If you want to load files or folders into the container, simply run the commands above from a directory containing them (you can use commands such as cd and cd .. to navigate through directories).

Warning: container images can be large depending on their content. If you do not plan to use the images soon and want to free up some storage space, please see Docker useful commands session⁠ to remove them.

⁠Linkage Mapping and Haplotype Reconstruction

• Docker Hub image: cristaniguti/poly_haplo⁠

Image contents

• Ubuntu 20.04
• R 4.0.3
• RStudio 1.3
• Julia 1.5.3
• MAPpoly
• polymapR
• PolyOrigin

Running instructions

Download the container image: open a terminal and run: docker pull cristaniguti/poly_haplo

Then start the image by running the following commands (feel free to choose another port different than 8787):

# Access Julia with
docker run -it -v $(pwd):/opt cristaniguti/poly_haplo /scripts/./julia

# Access the RStudio
docker run -p 8787:8787 -v $(pwd):/home/rstudio/ -e DISABLE_AUTH=true cristaniguti/poly_haplo

Keep this terminal open and access http://localhost:8787/ or http://127.0.0.1:8787/ in your web browser. You can also run the Julia using the RStudio terminal, but do not forget the path of the script inside the container: /scripts/julia. If you want to load files or folders into the container, simply run the commands above from a directory containing them (you can use commands such as cd and cd .. to navigate through directories).

With RStudio, try to load any of the included R packages:

# Loading MAPpoly
library(mappoly)
plot(solcap.err.map[[1]])

If you see the same plot shown above, you are ready to go! We recommend that you start by following the vignettes and examples for each package.

Warning: container images can be large depending on their content. If you do not plan to use the images soon and want to free up some storage space, please see Docker useful commands session⁠ to remove them.

⁠QTL mapping

• Docker Hub image: cristaniguti/poly_qtl⁠

Image contents

• Ubuntu 20.04
• R 4.0.3
• RStudio 1.3
• QTLpoly
• diaQTL
• GWASpoly

Running instructions

Download the container image: open a terminal and run: docker pull cristaniguti/poly_qtl

Then start the image by running the following commands (feel free to choose another port different than 8787):

# Access the RStudio
docker run -p 8787:8787 -v $(pwd):/home/rstudio/ -e DISABLE_AUTH=true cristaniguti/poly_qtl

Keep this terminal open and access http://localhost:8787/ or http://127.0.0.1:8787/ in your web browser. If you want to load files or folders into the container, simply run the commands above from a directory containing them (you can use commands such as cd and cd .. to navigate through directories).

Inside RStudio, try to load any of the included packages:

# Loading QTLpoly
library(qtlpoly)
load(pheno)
head(pheno)

##              T32        T17        T45
## Ind_1 -0.1698446 -0.9332320 -1.2259338
## Ind_2  2.5319356  0.1997378 -1.8004184
## Ind_3  1.3669074  1.0584794 -0.7980037
## Ind_4  0.7955652 -1.7186921  1.5834176
## Ind_5  1.3168502 -0.7119421  1.3099067
## Ind_6 -0.8778211 -0.2339543 -0.6323779

If you see the output shown above, you are ready to go! We recommend that you start by following the vignettes and examples for each package.

Warning: container images can be large depending on their content. If you do not plan to use the images soon and want to free up some storage space, please see Docker useful commands session⁠ to remove them.

⁠Help us to improve

If you find any problem or want to add more software to the images, or even create a new one, feel free to send us messages, open issues, or pull requests.