# Example 4: cnvkit There is an existing public Docker image available for CNVkit ("etal/cnvkit:latest"), so another option is to build a WDL version that will download and use this image at runtime rather than installing the Python and R modules ourselves. In this example, you will: * Use WDL and Docker to build the CNVkit ## Getting Started To start, create a new directory called *cnvkit\_wdl* parallel to the bash directory. Inside this new directory, create the file *workflow\.wdl* with the following contents: ``` version 1.0 task cnvkit_wdl_kyc { input { Array[File] bam_tumor File reference } command <<< cnvkit.py batch \ ~{sep=" " bam_tumor} \ -r ~{reference} \ -p $(expr $(nproc) -1) \ -d output/ \ --scatter >>> runtime { docker: "etal/cnvkit:latest" cpu: 16 } output { Array[File]+ cns = glob("output/[!.call]*.cns") Array[File]+ cns_filtered = glob("output/*.call.cns") Array[File]+ plot = glob("output/*-scatter.png") } } ``` Next, ensure you have a working Java compiler and then download the latest dxCompiler Jar file. You can use the following command to place the 2.10.3 release into your home directory: ``` $ cd && wget https://github.com/dnanexus/dxCompiler/releases/download/2.10.3/dxCompiler-2.10.3.jar ``` Use the dxCompiler to turn *workflow\.wdl* into an applet equivalent to the bash version. In the following command, the workflow and all related applets will be placed into a *workflows* directory in the given project to keep all this neatly contained. The given the project ID `project-GFf2Bq8054J0v8kY8zJ1FGQF` is the *caris\_cnvkit* project, so change this to if you wish to place this into a different project. Note the use of the `-archive` option to archive any existing version of the applet and allow the new version to take precendence and the `-reorg` to reorganize the output files. As shown in the following command, successful compilation will result in printing the new workflow's ID: ``` $ java -jar ~/dxCompiler-2.10.3.jar compile workflow.wdl \ -archive \ -reorg \ -folder /workflows \ -project project-GFf2Bq8054J0v8kY8zJ1FGQF applet-GFyVxpQ0VGFgGQBy4vJ0kxK2 ``` Run the new workflow with the `-h|--help` flag to verify the inputs: ``` $ dx run applet-GFyVxpQ0VGFgGQBy4vJ0kxK2 -h usage: dx run applet-GFyVxpQ0VGFgGQBy4vJ0kxK2 [-iINPUT_NAME=VALUE ...] Applet: cnvkit_wdl_kyc Inputs: bam_tumor: [-ibam_tumor=(file) [-ibam_tumor=... [...]]] reference: -ireference=(file) Reserved for dxCompiler overrides___: [-ioverrides___=(hash)] overrides______dxfiles: [-ioverrides______dxfiles=(file) [-ioverrides______dx> Outputs: cns: cns (array:file) cns_filtered: cns_filtered (array:file) plot: plot (array:file) ``` As with the bash version, you can launch the workflow from the CLI as follows: ``` $ dx run -y --watch applet-GFyVxpQ0VGFgGQBy4vJ0kxK2 \ -ibam_tumor=file-GFxXjV006kZVQPb20G85VXBp \ -ireference=file-GFxXvpj06kZfP0QVKq2p2FGF \ --destination project-GFyPxb00VGFz5JZQ4f5x424q:/users/kyclark ``` The resulting output will show the JSON you can alternatively use to launch the job: ``` $ cat inputs.json { "bam_tumor": [ { "$dnanexus_link": "file-GFxXjV006kZVQPb20G85VXBp" } ], "reference": { "$dnanexus_link": "file-GFxXvpj06kZfP0QVKq2p2FGF" } } ``` Following is the command you can use to launch the workflow from the CLI with the JSON file: ``` $ dx run -y --watch applet-GFyVxpQ0VGFgGQBy4vJ0kxK2 -f inputs.json \ --destination project-GFyPxb00VGFz5JZQ4f5x424q:/users/kyclark ``` As before, you can use the web interface to monitor the progress of the workflow and inspect the outputs. ## Saving a Docker Image Run the following command to start a new cloud workstation: ``` $ dx run -imax_session_length="1d" app-cloud_workstation --ssh -y ``` From the cloud workstation, pull the CNVkit Docker image: ``` $ docker pull etal/cnvkit:latest ``` Save and compress the image to a file: ``` $ docker save etal/cnvkit:latest | gzip - > cnvkit.tar.gz ``` Add the tarball to the project: ``` $ dx upload cnvkit.tar.gz --path project-GFyPxb00VGFz5JZQ4f5x424q:/ [===========================================================>] Uploaded 503,092,072 of 503,092,072 bytes (100%) cnvkit.tar.gz ID file-GFyq05j0VGFqJqq54q98pbBK Class file Project project-GFyPxb00VGFz5JZQ4f5x424q Folder / Name cnvkit.tar.gz State closing Visibility visible Types - Properties - Tags - Outgoing links - Created Thu Aug 18 03:20:55 2022 Created by kyclark via the job job-GFypx3Q0VGFgb71g4gYY3GF3 Last modified Thu Aug 18 03:20:57 2022 Media type archivalState "live" cloudAccount "cloudaccount-dnanexus" ``` Update the WDL to use the tarball: ``` version 1.0 task cnvkit_wdl_tarball { input { Array[File] bam_tumor File reference } command <<< cnvkit.py batch \ ~{sep=" " bam_tumor} \ -r ~{reference} \ -p $(expr $(nproc) -1) \ -d output/ \ --scatter >>> runtime { docker: "dx://file-GFyq05j0VGFqJqq54q98pbBK" cpu: 16 } output { Array[File]+ cns = glob("output/[!.call]*.cns") Array[File]+ cns_filtered = glob("output/*.call.cns") Array[File]+ plot = glob("output/*-scatter.png") } } ``` Build the app and run it. ## Review In this chapter, you learned another strategy for packaging an applet's dependencies using Docker and then running the applet's code inside the Docker image using WDL. ## Resources [Full Documentation](https://documentation.dnanexus.com/) To create a support ticket if there are technical issues: 1. 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