Direct usage of the execution engine Cromwell features complicated backend configuration, workflow options and command line parameters. Caper (developed and mantain by the ENCODE project) hides the complexity and consolidates configuration in one file. Caper is available in PyPI and it is installed by running:
$ pip install caper
Note that conda run mode that is described in caper documentation is not supported by this pipeline.
About caper config file
Once caper is installed, the following config file should be available:
/Users/[[mac_user]]/.caper/default.conf
(please, create otherwise)
Edit the config file and add the following options in order to be able to run it on GCP
backend=local
# Caper stores all important temp files and cached big data files here
tmp-dir=/Users/[[mac_user]]/temp/caper_temp
# GCP options
# GCP Project name
gcp-prj=[[your-gcp-project-here]]
# GCP output bucket
out-gcs-bucket=gs://[[your-bucket-location]]/proteomics_tests_gcp
Java is required to run execution engine Cromwell that caper uses under the hood. To check which Java version you already have, run:
$ java -version
You are looking for 1.8 or higher. If the requirement is not fulfilled follow installation instructions for mac or use your favorite installation method.
Pipeline code is packaged and distributed in Docker containers, and thus Docker installation is needed. Follow instructions for mac. (Docker Desktop recommended)
Note: When configuring Docker, don't forget to "share" the local folders where the source code of this project, data files, and the output are available. Check out the file sharing section of the manual to find out more. In Docker Desktop: "Preferences" > "Resources" > "File Sharing" and use the "+" to add the directories.
To build, tag, and push docker images used in this repo, check here.
We recommend using caper for running the pipeline, although it is possible to use Cromwell directly (you have to figure it out by yourself ;-)