RNA Sequencing

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

RNA sequencing (RNA-seq) is a key technique in modern biology, used to quantify gene expression, detect alternative splicing, and understand transcriptional changes under different conditions—whether in development, disease, or response to treatment. This notebook demonstrates a full RNA-seq analysis workflow powered by Nextflow and the nf-core/rnaseq pipeline, showcasing how Camber simplifies and scales reproducible cloud-based analysis.

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:

• command: The full Nextflow command to run the nf-core/rnaseq pipeline.

  • --input: "./samplesheet.csv": the relative path of samplesheet.csv file to the current notebook. In case of using local fastq files, the locations in samplesheet.csv file content are relative.

  • --outdir: "./outputs": the location stores output data of the job.

• engine_size="MICRO": indicate engine size to perform the job.

• num_engines=4: indicate number of engines to run workflow tasks in parallel when possible.

command = "nextflow run nf-core/rnaseq \
    --aligner star_rsem \
    --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 \
    --input ./samplesheet.csv \
    --outdir ./outputs \
    --skip_biotype_qc true \
    -r 3.18.0"

nf_rnaseq_job = nextflow.create_job(
    command=command,
    engine_size="XXSMALL",
    num_engines=4
)

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 of the job by two ways:

1. Browser data directly in notebook environment:

2. Go to the Stash UI:

By running this RNA-seq pipeline on Camber, you’ve leveraged a reproducible, cloud-optimized workflow with minimal infrastructure overhead. This approach streamlines large-scale data analysis and sets the stage for scalable genomics research using community standards and modern tools.

Note: Please note that the files and folders saved in the demos directory are temporary and will be reset after each JupyterHub session. We recommend changing the value of --outdir to a different location if you wish to store your data permanently.