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Genomics
RNA-Seq

RNA-Seq

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

    • samplesheet.csv
    • rnaseq_workflow.ipynb

    • This tutorial demonstrates that Nextflow Engine can handle nf-core/rnaseq 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/rnaseq": 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_rnaseq_job = nextflow.create_job(
    pipeline="nf-core/rnaseq",
    engine_size="MICRO",
    num_engines=4,
    params={
        "--input": "./samplesheet.csv",
        "--outdir": "/camber_outputs",
        "-r": "3.18.0",
        "--fasta": "s3://camber-open-storage-prod/public/fastq/rnaseq/ITAG2.3_genomic_Ch6.fasta",
        "--gtf": "s3://camber-open-storage-prod/public/fastq/rnaseq/ITAG_pre2.3_gene_models_Ch6.gtf",
        "--aligner": "star_rsem",
        "--skip_biotype_qc": "true",
    },
)

    This step is to check job status:

    nf_rnaseq_job.status

    View job logs online:

    nf_rnaseq_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