Our aim is to provide up-to-date containers for the current release and devel. Prerequisites: On Linux, you need Docker installed and on Mac or Windows you. Dec 29, 2017 - Under the hood, Docker for Mac is running an Alpine Linux virtual machine. This guide helps with issues related to communication between OS.
. Docker for Bioconductor Docker containers for Bioconductor allows software to be packaged into containers: self-contained environments that contain everything needed to run the software. Containers can be run anywhere (containers run in modern Linux kernels, but can be run on Windows and Mac as well using a virtual machine called.
Containers can also be deployed in the cloud using or other cloud providers. With Bioconductor containers, we hope to enhance.
reproducibility: If you run some code in a container today, you can run it again in the same container (with the same ) years later and know that nothing in the container has changed. You should always take note of the tag you used if you think you might want to reproduce some work later. ease of use: With one command, you can be running the latest release or devel Bioconductor. No need to worry about whether packages and system dependencies are installed. convenience: Sometimes you just want a fresh R with no packages installed, in order to test something; or you typically don’t have microarray packages installed but suddenly you need to do a microarray analysis.
Containers make this easy. Our aim is to provide up-to-date containers for the current release and devel versions of Bioconductor, and some older versions. Bioconductor’s Docker images are stored in; the source Dockerfiles are in. Our release images are based on and built when a Biocondcutor Release occurs. Our devel images are based on and built weekly with the latest versions of R and Bioconductor packages.
For each supported version of Bioconductor, we provide several images:. base2: Contains R, RStudio, and a single Bioconductor package ( BiocManager, providing the install function for installing additional packages). Also contains many system dependencies for Bioconductor packages. Useful when you want a relatively blank slate for testing purposes. R is accessible via the command line or via RStudio Server. core2: Built on base2, so it contains everything in base2, plus a selection of core Bioconductor packages. protmetcore2: Built on core2, so it contains everything in core2, plus a selection of core Bioconductor packages recommended for proteomic and metabolomics analysis.
metabolomics2: everything in protmetcore2, plus select packages from the biocView. Current Containers Maintained by the Bioconductor Core Team: [email protected].
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Maintained by Steffen Neumann: [email protected] Maintained as part of the “PhenoMeNal, funded by Horizon2020 grant 654241”. Maintained by Laurent Gatto: [email protected]. Legacy Containers The following containers are legacy and no longer updated. They have been kept to retain previous versions available via tags:. bioconductor/develbase.
bioconductor/develcore. bioconductor/develflow. bioconductor/develmicroarray. bioconductor/develproteomics. bioconductor/develsequencing. bioconductor/develmetabolomics. bioconductor/releasebase.
bioconductor/releasecore. bioconductor/releaseflow. bioconductor/releasemicroarray. bioconductor/releaseproteomics. bioconductor/releasesequencing.
bioconductor/releasemetabolomics Using the containers The following examples use the bioconductor/develbase2 container. Note that you may need to prepend sudo to all docker commands.
Prerequisites: On Linux, you need Docker and on or you need Docker Toolbox installed and running. To run RStudio Server: docker run -p 8787:8787 bioconductor/develbase2 You can then open a web browser pointing to your docker host on port 8787.
If you’re on Linux and using default settings, the docker host is 127.0.0.1 (or localhost, so the full URL to RStudio would be If you are on Mac or Windows and running Docker Toolbox, you can determine the docker host with the docker-machine ip default command. Log in to RStudio with the username rstudio and password rstudio. If you want to run RStudio as a user on your host machine, in order to read/write files in a host directory, please. To run R from the command line: docker run -ti bioconductor/develbase2 R To open a Bash shell on the container: docker run -ti bioconductor/develbase2 bash Note: The docker run command is very powerful and versatile. For full documentation, type docker run -help or visit the. Modifying the images There are two ways to modify these images:. Making changes in a running container and then committing them using the docker commit command.
Using a Dockerfile to declare the changes you want to make. The second way is the recommended way. Both ways are. List of packages installed on the core2 container These packages, plus their dependencies, are installed:.
BiocManager. OrganismDbi. ExperimentHub. Biobase. BiocParallel.
biomaRt. Biostrings.
BSgenome. ShortRead. IRanges. GenomicRanges.
GenomicAlignment. GenomicFeatures. SummarizedExperiment. VariantAnnotation. DelayedArray.
GSEABase. Gviz. graph.
RBGL. Rgraphviz. rmarkdown. httr. knitr. BiocStyle Acknowledgements Thanks to the project for providing the R/RStudio Server containers upon which ours are based.
There hasn’t been Docker for Mac Stable release in a couple of months. It’s still at 17.03 and compose 1.11, two versions behind the regular docker release (17.05 / 1.13).
I know I can try Edge but I believe that requires losing current containers / images, and I would rather stay on “released” versions. I’m otherwise using released/official docker on ubuntu, so it’s a pain dealing with the old client and old bugs on MacOS. I guess I’m just wondering if this is temporary – does Docker for Mac intend for Stable to keep up with docker releases or is it totally on its own schedule? Maybe I should be using docker-machine instead of Docker for Mac. Thanks, Jamshid.
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