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Sarek

Sarek

You can find this application in the demos folder of your Jupyter notebook environment.

    • sample.csv
    • sarek_workflow.ipynb

    • This tutorial demonstrates that Nextflow Engine can handle nf-core/sarek pipeline.

    The first step is to import the nextflow package:

    from camber import nextflow

    Here’s an example of how to setup configurations and execute a job:

    • pipeline="nf-core/sarek": specify pipeline to run.
    • engine_size="MICRO": indicate engine size to perform the job.
    • num_engines=4: indicate number of engines to run workflow tasks in parallel.

    Pipeline parameters must be defined in params argument. To ensure the pipeline works as expected, please take note that:

    • "--input": "./samplesheet.csv": the relative path of samplesheet.csv file to the current notebook. In case of using local FastQ files, the locations of them in samplesheet.csv file content are relative also.
    • "--outdir": "/camber_outputs": the location stores output data of the job.
    nf_sarek_job = nextflow.create_job(
    pipeline="nf-core/sarek",
    engine_size="MICRO",
    num_engines=4,
    params={
        "--input": "./samplesheet.csv",
        "--outdir": "/camber_outputs",
        "-r": "3.5.1",
        "--tools": "freebayes",
    },
)

    This step is to check job status:

    nf_sarek_job.status

    To monitor job exectution, you can show job logs in real-time by read_logs method:

    nf_sarek_job.read_logs()

    When the job is done, you can discover and download the results and logs of the job by two ways:

    1. Browser data directly in notebook environment:

    image

    1. Go to the Stash UI:

    image