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• Nextflow is a free and open-source pipeline management software that enables scalable and reproducible scientific workflows. It allows the adaptation of pipelines written in the most common scripting languages.

• Key features of Nextflow:

  • Reproducible → version control and use of containers ensure the reproducibility of nextflow pipelines

  • Portable → compute agnostic (i.e., HPC, cloud, desktop)

  • Scalable → run from a single to thousands of samples

  • Minimal digital literacy → accessible to anyone

  • Active global community → more and more nextflow pipelines are available (i.e., https://nf-co.re/pipelines )

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For more information about Nextflow, please visit Nextflow - A DSL for parallel and scalable computational pipelines

Installing Nextflow

Nextflow is meant to run from your home folder on a Linux machine like the HPC.

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To install Nextflow, copy and paste the following block of code into your terminal (i.e., PuTTy that is already connected to the terminal) and hit 'enter':

module load java
curl -s https://get.nextflow.io | bash
mv nextflow $HOME/bin

• Line 1: The module load command is necessary to ensure java is available

• Line 2: This command downloads and assembles the parts of nextflow - this step might take some time.

• Line 3: When finished, the nextflow binary will be in the current folder so it should be moved to your “bin” folder” so it can be found later.

To verify that Nextflow is installed properly, you can run the following command:

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mkdir $HOME/nftemp && cd $HOME/nftemp
nextflow run hello

• Line 1: Make a temporary folder for Nextflow to create files when it runs.

• Line 2: Verify Nextflow is working.

You should see something like this:

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cd $HOME
rm -rf nftemp

Nextflow’s base configuration

A key Nextflow feature is the ability to decouple the workflow implementation, which describes the flow of data and operations to perform on that data, from the configuration settings required by the underlying execution platform. This enables the workflow to be portable, allowing it to run on different computational platforms such as an institutional HPC or cloud infrastructure, without needing to modify the workflow implementation.

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[[ -d $HOME/.nextflow ]] || mkdir -p $HOME/.nextflow
cat <<EOF > $HOME/.nextflow/config
singularity {
    cacheDir = '$HOME/.nextflow/NXF_SINGULARITY_CACHEDIR'
    autoMounts = true
}
conda {
    cacheDir = '$HOME/.nextflow/NXF_CONDA_CACHEDIR'
}
process {
  executor = 'pbspro'
  scratch = false
  cleanup = false
}
includeConfig '/work/datasets/reference/nextflow/qutgenome.config'
EOF

• Line 1: Check if a .nextflow/config file already exists in your home directory. Create it if it does not exist

• Line 2-15: Using the cat command, paste text in the newly created .nextflow/config file which specifies the cache location for your singularity and conda.

• What are the parameters you are setting?

• Line 3-6 set the directory where remote Singularity images are stored and direct Nextflow to automatically mount host paths in the executed container.

• Line 7-9 set the directory where Conda environments are stored.

• Line 10-14 sets default directives for processes in your pipeline. Note that the executor is set to pbspro on line 11.

• Line 15 provides the local path to genome files required for pipelines such as nf-core/rnaseq

More in depth information on Nextflow configuration is described here: https://www.nextflow.io/docs/latest/config.html.

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Column names has to be specified in a header row as shown in the samplesheet example below:

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sample,fastq_1
Clone1_N1,s3://ngi-igenomes/test-data/smrnaseq/C1-N1-R1_S4_L001_R1_001.fastq.gz
Clone1_N3,s3://ngi-igenomes/test-data/smrnaseq/C1-N3-R1_S6_L001_R1_001.fastq.gz
Clone9_N1,s3://ngi-igenomes/test-data/smrnaseq/C9-N1-R1_S7_L001_R1_001.fastq.gz
Clone9_N2,s3://ngi-igenomes/test-data/smrnaseq/C9-N2-R1_S8_L001_R1_001.fastq.gz
Clone9_N3,s3://ngi-igenomes/test-data/smrnaseq/C9-N3-R1_S9_L001_R1_001.fastq.gz
Control_N1,s3://ngi-igenomes/test-data/smrnaseq/Ctl-N1-R1_S1_L001_R1_001.fastq.gz
Control_N2,s3://ngi-igenomes/test-data/smrnaseq/Ctl-N2-R1_S2_L001_R1_001.fastq.gz
Control_N3,s3://ngi-igenomes/test-data/smrnaseq/Ctl-N3-R1_S3_L001_R1_001.fastq.gz

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For the nf-core/rnaseq pipeline, the samplesheet has to be a comma-separated file with the following 4 columns:

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Column names has to be specified in a header row as shown in the samplesheet example below:

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sample,fastq_1,fastq_2,strandedness
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,auto
CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz,auto
CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz,auto

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Please note that in this example, the same sample (CONTROL_REP1) was sequenced across 3 lanes. The nf-core/sarek pipeline will concatenate the raw reads before performing any downstream analysis.

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We can use the Bash tree command to list the contents of the work directory. Note: By default tree does not print hidden files (those beginning with a dot .). Use the -a to view all files.

tree -a work

Provide a relevant example from test run

Task execution directory

Within the work directory there are multiple task execution directories. There is one directory for each time a process is executed. These task directories are identified by the process execution hash. For example the task directory fa/cd3e49b63eadd6248aa357083763c1 would be location for the process identified by the hash fa/cd3e49 .

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