Prior running the nf-core/sarek pipeline with real data, we will first run a test with sample data to make sure the pipeline runs properly.
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qsub -I -S /bin/bash -l walltime=10:00:00 -l select=1:ncpus=1:mem=4gb |
You should be in your home directory, if unsure you can run the following command:
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cd ~ |
List the existing files and folders:
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ls -l |
Let’s create a folder for the workshop
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mkdir -p $HOME/workshop/sarek |
Get a copy of the scripts to be used in this module
Use the terminal to log into the HPC and Now let’s create a /RNAseq/ folder to run the nf-core/rnaseq pipeline. For example‘scripts’ folder and copy all scripts that we will using in the session:
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mkdir -p $HOME/workshop/sarek/scripts
cp /work/training/sarek/scripts/* $HOME/workshop/sarek/scripts/
ls -l $HOME/workshop/sarek/scripts/ |
Line 1: The -p indicates create 'parental directories as required. Thus the line 1 command creates both /workshop/ and the subfolder /workshop/sarek/scripts/
Line 2: Copies all files from /work/datasets/workshop/scripts/ as noted by an asterisk to the newly created folder $HOME/workshop/scripts/
Copy public data to your $HOME
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$HOME/workshop/sarek/
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Lines 1 -2: Command creates the folders to copy data
Line 3: Copies all files from /work/datasets/workshop/sarek/data/WES/trio folder as noted by an asterisk to newly created $HOME/workshop/sarek/data/WES/trio folder.
Line 4: Copies all files from /work/datasets/workshop/sarek/data/WES/liver folder as noted by an asterisk to newly created $HOME/workshop/sarek/data/WES/liver folder.scripts/
Create folders for running the nf-core/sarek pipeline
Let’s create an “RNAseq” folder to run the nf-core/rnaseq pipeline and move into it. For example:
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mkdir -p $HOME/workshop/sarek mkdir $HOME/workshop/sarek/run1_test mkdir $HOME/workshop/sarek/run2_trio mkdir $HOME/workshop/sarek/run3_liver cd $HOME/workshop/sarek |
Lines 1-43: create sub-folders for each exercise
Line 54: change the directory to the folder “run1_test”Line 6: print the current working directory
Exercise 1: Running a test with nf-core sample data
First, let’s assess the execution of the nf-core/rnaseq pipeline by running a test using sample data.
Copy the launch_nf-core_RNAseqsarek_test.pbs to the working directory
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#!/bin/bash -l #PBS -N nfsarek_run1_test #PBS -l walltime=48:00:00 #PBS -l select=1:ncpus=1:mem=5gb cd $PBS_O_WORKDIR NXF_OPTS='-Xms1g -Xmx4g' module load java #specify the nextflow version to use to run the workflow export NXF_VER=23.10.1 #run the sarek pipeline nextflow run nf-core/sarek \ -r 3.3.2 \ -profile test,singularity \ --outdir ./results |
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nextflow command: nextflow run
pipeline name: nf-core/sarek
pipeline version: -r 3.3.2
container type and sample data: -profile test,singularity
output directory: --outdir results
Submitting the job
Submit the test job to the HPC cluster as follows:
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results/
├── csv
│ ├── markduplicates.csv
│ ├── markduplicates_no_table.csv
│ ├── recalibrated.csv
│ └── variantcalled.csv
├── multiqc
│ ├── multiqc_data
│ ├── multiqc_plots
│ └── multiqc_report.html
├── pipeline_info
│ ├── execution_report_2024-05-08_15-28-38.html
│ ├── execution_timeline_2024-05-08_15-28-38.html
│ ├── execution_trace_2024-05-08_15-28-38.txt
│ ├── params_2024-05-08_15-41-30.json
│ ├── pipeline_dag_2024-05-08_15-28-38.html
│ └── software_versions.yml
├── preprocessing
│ ├── markduplicates
│ ├── recalibrated
│ └── recal_table
├── reports
│ ├── bcftools
│ ├── fastqc
│ ├── markduplicates
│ ├── mosdepth
│ ├── samtools
│ └── vcftools
├── tabix
│ ├── genome.bed.gz
│ └── genome.bed.gz.tbi
└── variant_calling
└── strelka |
Once the pipeline has finished running - Assess the QC report:
NOTE: To proceed, you need to be on QUT’s WiFi network or signed via VPN.
To browse the working folder in the HPC type in the file finder:
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Evaluate the nucleotide distributions in the 5'-end and 3'-end of the sequenced reads (Read1 and Read2). Look into the “MultiQC” folder and open the provided HTML report.
Items to check:
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Phred Quality Score
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Probability of incorrect base call
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Base call accuracy
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10
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1 in 10
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90%
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20
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1 in 100
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99%
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30
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1 in 1000
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99.9%
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40
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1 in 10,000
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99.99%
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50
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1 in 100,000
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99.999%
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60
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1 in 1,000,000
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