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Exercise 3: Run nf-core/sarek using a liver samples 

What is NAFLD?

Non-alcoholic fatty liver disease (NAFLD) is a condition characterized by an accumulation of fat in liver cells (hepatocytes). Excess fat in the liver can lead to significant damage over the years. There are two types of NAFLD:

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NASH (Non-alcoholic steatohepatitis) – It is a malignant condition and in this fat is accumulated in the liver can cause scarring of liver, liver fibrosis and liver damage further it can cause cirrhosis of liver (also called as end-stage liver disease), further cirrhosis of the liver may lead to liver cancer.

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The pipeline requires preparing at least 2 files:

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Location of raw data:

/work/training/sarek/data/WES/liver

├── liver
│   ├── Control1_C1_L001_R1_001.fastq.gz
│   ├── Control1_C1_L001_R2_001.fastq.gz
│   ├── Control2_C2_L001_R1_001.fastq.gz
│   ├── Control2_C2_L001_R2_001.fastq.gz
│   ├── Control3_C3_L001_R1_001.fastq.gz
│   ├── Control3_C3_L001_R2_001.fastq.gz
│   ├── Control4_C4_L001_R1_001.fastq.gz
│   ├── Control4_C4_L001_R2_001.fastq.gz
│   ├── NAFLD1_P1_L001_R1_001.fastq.gz
│   ├── NAFLD1_P1_L001_R2_001.fastq.gz
│   ├── NAFLD2_P2_L001_R1_001.fastq.gz
│   ├── NAFLD2_P2_L001_R2_001.fastq.gz
│   ├── NAFLD3_P3_L001_R1_001.fastq.gz,/full/path/to/ID1_S1_L002
│   ├── NAFLD3_P3_L001_R2_001.fastq.gz

• PBS Pro script (launch_nf-core_sarek_trio.pbs) with instructions to run the pipeline

│   ├── NAFLD4_P4_L001_R1_001.fastq.gz
│   ├── NAFLD4_P4_L001_R2_001.fastq.gz
│   └── samplesheet.csv

STEP1: Create the metadata file (samplesheet.csv):

Change to the data folder directory:

cd $HOME/workshop/sarek/dataruns/triorun3_liver
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/triorun3_liver

• Note: you could replace ‘$HOME/workshop/sarek/runs/data’ liver’ 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:

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python create_samplesheet_nf-core_sarek.py -h

Let’s generate the metadata file by running the following command:

python create_samplesheet_nf-core_sarek.py --dir $HOME/workshop/sarek/data/trioliver \
  --read1_extension R1.fastq.gz \
  --read2_extension R2.fastq.gz \
  --out samplesheet.csv

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ls -l
cat samplesheet.cvs

Copy the PBS Pro script for running the nf-core/sarek pipeline (launch_nf-core_sarek_trio.pbs)

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Alternatively copy the samplesheet.csv file:

cp $HOME/work/workshoptraining/sarek/data/WES/trioliver/samplesheet.csv $HOME/workshop/sarek/runs/run2_sarek_trio
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STEP2 - Run the nf-core/sarek pipeline

Copy and paste the code below to the terminal:

cp $HOME/workshop/sarek/scripts/launch_nf-core_sarek_trioliver.pbs $HOME/workshop/sarek/runs/run2run3_trioliver
cd $HOME/workshop/sarek/runs/run2run3_trioliver

• 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_RNAseqsarek_QCliver.pbs

• 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:

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Once the pipeline has finished running - Assess the results as follows:

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.

During execution of the workflow two output folders are generated:

• work - where all intermediate results and tasks are run

• results - where all final results for all stages of the pipeline are copied

Let’s browse the results of the pipeline:

results/
├── annotation
│   └── haplotypecaller
├── csv
│   ├── markduplicates.csv
│   ├── markduplicates_no_table.csv
│   ├── recalibrated.csv
│   └── variantcalled.csv
├── multiqc
│   ├── multiqc_data
│   ├── multiqc_plots
│   └── multiqc_report.html
├── pipeline_info
│   ├── execution_report_2024-05-11_20-17-13.html
│   ├── execution_timeline_2024-05-11_20-17-13.html
│   ├── execution_trace_2024-05-11_20-17-13.txt
│   ├── params_2024-05-11_23-31-28.json
│   ├── pipeline_dag_2024-05-11_20-17-13.html
│   └── software_versions.yml
├── preprocessing
│   ├── markduplicates
│   ├── recalibrated
│   └── recal_table
├── reports
│   ├── bcftools
│   ├── EnsemblVEP
│   ├── fastp
│   ├── fastqc
│   ├── markduplicates
│   ├── mosdepth
│   ├── samtools
│   ├── snpeff
│   └── vcftools
├── tabix
│   ├── wgs_calling_regions_noseconds.hg38.bed.gz
│   └── wgs_calling_regions_noseconds.hg38.bed.gz.tbi
└── variant_calling
    └── haplotypecaller