Prerequisites

You will require a basic knowledge of Linux/Unix commands to be able to participate effectively in this workshop. If you don’t, please attend the following training [Introduction to HPC].

Getting started with Nextflow

• What is a workflow and what are workflow management systems?

• Why should I use a workflow management system?

• What is Nextflow?

• What are the main features of Nextflow?

• What are the main components of a Nextflow script?

What is Nextflow?

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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:

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

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

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Scalable → run from a single to thousands of samples

This instructional material was originally developed by Maely Gauthier in 2024 as part of the QUT eResearch infrastructure. It is free to distribute but we just require that you acknowledge eResearch for any outputs (e.g. training, presentation slides, publications) that might result from using this training material.

Prerequisites

You will require a basic knowledge of Linux/Unix commands to be able to participate effectively in this workshop. If you don’t, please attend the following training [Introduction to HPC].

Getting started with Nextflow

• What is a workflow and what are workflow management systems?

• Why should I use a workflow management system?

• What is Nextflow?

• What are the main features of Nextflow?

• What are the main components of a Nextflow script?

What is Nextflow?

• 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 )

...

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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[[ -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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One of the core features of Nextflow is the ability to cache task executions and re-use them in subsequent runs to minimize duplicate work. Resumability is useful both for recovering from errors and for iteratively developing a pipeline

You can enable resumability in Nextflow with the -resume flag when launching a pipeline with nextflow run.

All task executions are automatically saved to the task cache, regardless of the -resume option (so that you always have the option to resume later).

Structure of work folder

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to cache task executions and re-use them in subsequent runs to minimize duplicate work. Resumability is useful both for recovering from errors and for iteratively developing a pipeline

Resume option

You can enable resumability in Nextflow with the -resume flag when launching a pipeline with nextflow run.

All task executions are automatically saved to the task cache, regardless of the -resume option (so that you always have the option to resume later).

Structure of work folder

When nextflow runs, it assigns a unique ID to each task. This unique ID is used to create a separate execution directory, within the work directory, where the tasks are executed and the results stored. A task’s unique ID is generated as a 128-bit hash number.

When we resume a workflow, Nextflow uses this unique ID to check if:

1. The working directory exists

2. It contains a valid command exit status

3. It contains the expected output files.

If these conditions are satisfied, the task execution is skipped and the previously computed outputs are applied.

When a task requires recomputation, ie. the conditions above are not fulfilled, the downstream tasks are automatically invalidated.

Therefore, if you modify some parts of your script, or alter the input data using -resume, Nextflow will only execute the processes that are actually changed.

The execution of the processes that are not changed will be skipped and the cached result used instead.

This helps a lot when testing or modifying part of your pipeline without having to re-execute it from scratch.

By default the pipeline results are cached in the directory work where the pipeline is launched.

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 .

The task execution directory contains:

• .command.sh: The command script.

• .command.run: The file is a bash script that Nextflow generates to execute the .command.sh script, handling the necessary environment setup and command execution details.

• .command.out: The complete job standard output.

• .command.err: The complete job standard error.

• .command.log: The wrapper execution output.

• .command.begin: A file created as soon as the job is launched.

• .exitcode: A file containing the task exit code.

• Any task input files (symlinks)

• Any task output files

Specifying another work directory

Depending on your script, this work folder can take a lot of disk space. You can specify another work directory using the command line option -w. Note Using a different work directory will mean that any jobs will need to re-run from the beginning.

Clean the work directory

If you are sure you won’t resume your pipeline execution, clean this folder periodically using the command nextflow clean.

nextflow clean [run_name|session_id] [options]

Nextflow pipeline outputs and PBS outputs

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