Task 3: Run nf-core/rnaseq pipeline

Aim

Demonstrate how to run the nextflow nf-core/rnaseq pipeline in the HPC cluster. Initially, running a test and then processing real-life data.

Create a folder for running the nf-RNA-seq pipeline

Create a new folder under your ‘myname’ folder called nextflow, and also create a subfolder called ‘run1’ for the first test of the pipeline:

Go to the subfolder to run the RNAseq test

Case 1: Running a test

Download and run the workflow using minimal data provided by nf-core/rnaseq. We recommend using singularity as the profile for QUT’s HPC. Another profile option can be ‘conda.’ Note: the profile option ‘docker’ is unavailable on the HPC.

To execute the above command in the HPC cluster, prepare a PBS Pro submission script as follows:

Submitting the job

Once you have created the folder for the run, the samplesheet.csv file, nextflow.config, and launch.pbs, you are ready to submit.

Submit the run with this command (On Lyra)

Monitoring the Run

You can use the command

Alternatively, use the command

to check on the jobs you are running. Nextflow will launch additional jobs during the run.

You can also check the .nextflow.log file for details on what is going on.

Case 2: Run RNA-seq QC check

Preparing a ‘samplesheet.csv’ file

Prepare a sample sheet file that specifies the input files to be used. To do this, we use an nf-core script to generate the ‘samplesheet.csv’ file as follows (setting strandedness to auto allows the pipeline to determine the strandedness of your RNA-seq data automatically):

Load module Python 3.10

Download the script for creating the ‘samplesheet.csv’ metadata file.

If you do not know already, determine the path to where the FASTQ files were downloaded/are located.

Using a text editor (i.e., VIM), edit the following code with the appropriate path to the files:

Example of 'samplesheet.csv' required for nf-core/rnaseq pipeline version 3.12.0:

Prepare the following ‘launch_QC.pbs’ script:

We recommend running the nextflow nf-core/rnaseq pipeline once and then assessing the fastqc results folder to assess if sequence biases are present in the 5'-end and 3'-end ends of the sequences. Version 3.12.0 allows running the pipeline to do quality assessment only, without any alignment, read counting, or trimming. To execute that option, add the following flags to your nextflow run nf-core/rnaseq command: --skip_trimming, --skip_alignment, and --skip_pseudo_alignment.

Submitting the job

Once you have created the folder for the run, the samplesheet.csv file, and launch.pbs, you are ready to submit.

Submit the run with this command

Monitoring the Run

You can use the command

Alternatively, use the command

to check on the jobs, you are running. Nextflow will launch additional jobs during the run.

You can also check the .nextflow.log file for details on what is going on.

Assessing QC report

Evaluate the nucleotide distributions in the 5'-end and 3'-end of the sequenced reads (Read1 and Read 2). Define how many nucleotides should be trimmed from each region of the sequences. This will inform the parameter setting for Case 3 below.

Case 3: Run RNA-seq pipeline

Adjusting the Trim Galore options

When the initial trimming is done, verify if any more clipping needs to be done and run the nf-core/rnaseq pipeline that will perform all the steps. For example:

Submitting the job

Once you have created the folder for the run, the samplesheet.csv file, and ‘launch.pbs', you are ready to submit.

Submit the run with this command

Monitoring the Run

You can use the command

Alternatively, use the command

to check on the jobs you are running. Nextflow will launch additional jobs during the run.

You can also check the .nextflow.log file for details on what is going on.

Outputs

The pipeline will produce two folders, one called “work,” where all the processing is done, and another called “results,” where we can find the pipeline's outputs. The content of the results folder is as follows:

The quantification of the gene and transcript expressions can be found in the ‘star_salmon’ directory.

The following feature count tables are generated: