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.
Work in the HPC |
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Before we start using the HPC, let’s start an interactive session:
qsub -I -S /bin/bash -l walltime=10:00:00 -l select=1:ncpus=1:mem=4gb
Get a copy of the scripts to be used in this module
Use the terminal to log into the HPC and create a /RNAseq/ folder to run the nf-core/rnaseq pipeline. For example:
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/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
mkdir -p $HOME/workshop/sarek/data/WES/trio mkdir -p $HOME/workshop/sarek/data/WES/liver cp /work/training/sarek/data/WES/trio/* $HOME/workshop/sarek/data/WES/trio cp /work/training/sarek/data/WES/liver/* $HOME/workshop/sarek/data/WES/liver
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.
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:
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/
Lines 1-4: create sub-folders for each exercise
Line 5: 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_RNAseq_test.pbs to the working directory
cd $HOME/workshop/sarek/run1_test cp $HOME/workshop/sarek/scripts/launch_nf-core_sarek_test.pbs .
View the content of the script as follows:
cat launch_nf-core_sarek_test.pbs
#!/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:
qsub launch_nf-core_sarek_test.pbs
Monitoring the Run
qjobs
Outputs:
The test run should take about ~14 min to complete. Find run outputs in the “results” folder:
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
Exercise 2: Run nf-core/sarek using trio data
The pipeline requires preparing at least 2 files:
Metadata file (samplesheet.csv) that specifies the following information:
patient,sample,lane,fastq_1,fastq_2 ID1,S1,L002,/full/path/to/ID1_S1_L002_R1_001.fastq.gz,/full/path/to/ID1_S1_L002_R2_001.fastq.gz
PBS Pro script (launch_nf-core_sarek_trio.pbs) with instructions to run the pipeline
Create the metadata file (samplesheet.csv):
Change to the data folder directory:
cd $HOME/workshop/sarek/data/trio pwd
Copy the python script “create_samplesheet_nf-core_sarek.py" to the working folder
cp /work/training/sarek/scripts/create_samplesheet_nf-core_sarek.py $HOME/workshop/sarek/data/trio
Note: you could replace ‘$HOME/workshop/sarek/data’ with “.” A dot indicates ‘current directory’ and will copy the file to the directory where you are currently located
Check help option on how to run the script:
python create_samplesheet_nf-core_sarek.py --help
python create_samplesheet_nf-core_sarek.py -h
usage: create_samplesheet_nf-core_sarek.py [-h] [--dir DIR] [--read1_extension READ1_EXTENSION] [--read2_extension READ2_EXTENSION] [--out OUT] Extract metadata from fastq files in a directory. optional arguments: -h, --help show this help message and exit --dir DIR Directory to search for files (default: current directory) --read1_extension READ1_EXTENSION Extension for fastq_1 files (default: R1_001.fastq.gz) --read2_extension READ2_EXTENSION Extension for fastq_2 files (default: R2_001.fastq.gz) --out OUT Output metadata CSV file |
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Let’s generate the metadata file by running the following command:
python create_samplesheet_nf-core_sarek.py --dir $HOME/workshop/sarek/data/trio \ --read1_extension R1.fastq.gz \ --read2_extension R2.fastq.gz \ --out samplesheet.csv
Check the newly created samplesheet.csv file:
ls -l cat samplesheet.cvs
patient,sample,lane,fastq_1,fastq_2 SRR14724455,NA12892a,L001,/work/training/sarek/data/WES/trio/SRR14724455_NA12892a_L001_R1.fastq.gz,/work/training/sarek/data/WES/trio/SRR14724455_NA12892a_L001_R2.fastq.gz SRR14724456,NA12891a,L001,/work/training/sarek/data/WES/trio/SRR14724456_NA12891a_L001_R1.fastq.gz,/work/training/sarek/data/WES/trio/SRR14724456_NA12891a_L001_R2.fastq.gz SRR14724463,NA12878a,L001,/work/training/sarek/data/WES/trio/SRR14724463_NA12878a_L001_R1.fastq.gz,/work/training/sarek/data/WES/trio/SRR14724463_NA12878a_L001_R2.fastq.gz SRR14724474,NA12892b,L001,/work/training/sarek/data/WES/trio/SRR14724474_NA12892b_L001_R1.fastq.gz,/work/training/sarek/data/WES/trio/SRR14724474_NA12892b_L001_R2.fastq.gz SRR14724475,NA12891b,L001,/work/training/sarek/data/WES/trio/SRR14724475_NA12891b_L001_R1.fastq.gz,/work/training/sarek/data/WES/trio/SRR14724475_NA12891b_L001_R2.fastq.gz SRR14724483,NA12878b,L001,/work/training/sarek/data/WES/trio/SRR14724483_NA12878b_L001_R1.fastq.gz,/work/training/sarek/data/WES/trio/SRR14724483_NA12878b_L001_R2.fastq.gz |
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Copy the PBS Pro script for running the nf-core/sarek pipeline (launch_nf-core_sarek_trio.pbs)
Copy and paste the code below to the terminal:
cp $HOME/workshop/sarek/data/WES/trio/samplesheet.csv $HOME/workshop/sarek/runs/run2_sarek_trio cp $HOME/workshop/sarek/scripts/launch_nf-core_sarek_trio.pbs $HOME/workshop/sarek/runs/run2_trio cd $HOME/workshop/sarek/runs/run2_trio
Line 1: Copy the samplesheet.csv file generated above to the working directory
Line 2: copy the launch_nf-core_sarek_trio.pbs submission script to the working directory
Line 3: move to the working directory
View the content of the launch_nf-core_RNAseq_QC.pbs script:
cat launch_nf-core_RNAseq_QC.pbs
#!/bin/bash -l #PBS -N nfsarek_run2_trio #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 singularity \ --genome GATK.GRCh38 \ --input samplesheet.csv \ --wes \ --outdir ./results \ --step mapping \ --tools haplotypecaller,snpeff,vep \ --snpeff_cache /work/training/sarek/NXF_SINGULARITY_CACHEDIR/snpeff_cache \ --vep_cache /work/training/sarek/NXF_SINGULARITY_CACHEDIR/vep_cache \ -resume |
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The above script will screen for germline (inherited) mutations using GATK’s haplotypecaller and then annotate the identified variants using snpeff and VEP.
Version 3.3.2 allows running the pipeline to do quality assessment only, without any alignment, read counting, or trimming.
The pipeline enables use to start at distinct stages, we are commencing from the start “--step mapping”
Submitting the job
Once you have created the folder for the run, the samplesheet.csv file, and launch.pbs, you are ready to submit the job to the HPC scheduler:
qsub launch_nf-core_sarek_trio.pbs
Monitoring the Run
qjobs
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.
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:
Windows PC
\\hpc-fs\work\training\rnaseq
Mac
smb://hpc-fs/work/training/rnaseq
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:
The overall quality of the experiment and reads. Look at the “Sequence Quality Histogram” plot. For example, if Phred assigns a quality score of 30 to a base, the chances that this base is called incorrectly are 1 in 1000. Phred quality scores are logarithmically linked to error probabilities.
Phred Quality Score | Probability of incorrect base call | Base call accuracy |
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10 | 1 in 10 | 90% |
20 | 1 in 100 | 99% |
30 | 1 in 1000 | 99.9% |
40 | 1 in 10,000 | 99.99% |
50 | 1 in 100,000 | 99.999% |
60 | 1 in 1,000,000 | 99.9999% |
Assess QC reports (FastQC and MultiQC) to define how many nucleotides should be trimmed from the 5'-end and/or 3-end regions of the FASTQ reads (see Case 3 below).
Exercise 3: Healthy vs. disease liver samples
Copy and paste the code below to the terminal:
cp $HOME/workshop/data/samplesheet.csv $HOME/workshop/RNAseq/run3_RNAseq cd $HOME/workshop/RNAseq/run3_RNAseq pwd
Line 1: Copy the samplesheet.csv file to the working directory
Line 2: move to the working directory
Line 3: print working directory → verify the folder location
Copy the PBS Pro script to run the nf-core/rnaseq pipeline:
cp $HOME/workshop/scripts/launch_nf-core_RNAseq_pipeline.pbs $HOME/workshop/RNAseq/run3_RNAseq
Adjusting the Trim Galore (read trimming) options
Print the content of the launch_RNAseq.pbs script:
cat launch_nf-core_RNAseq_pipeline.pbs
#!/bin/bash -l #PBS -N nfRNAseq #PBS -l select=1:ncpus=2:mem=4gb #PBS -l walltime=48:00:00 #work on current directory cd $PBS_O_WORKDIR #load java and set up memory settings to run nextflow module load java export NXF_OPTS='-Xms1g -Xmx4g' nextflow run nf-core/rnaseq --input samplesheet.csv \ --outdir results \ -r 3.12.0 \ --genome GRCm38-local \ -profile singularity \ --aligner star_salmon \ --extra_trimgalore_args "--quality 30 --clip_r1 10 --clip_r2 10 --three_prime_clip_r1 1 --three_prime_clip_r2 1 " |
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Submitting the job
qsub launch_nf-core_RNAseq_pipeline.pbs
Monitoring the Run
qjobs
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: