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25S1W1 - Case study 2: Liver disease

25S1W1 - Case study 2: Liver disease

Owned by Roberto Barrero Gumiel
Last updated: Mar 17, 2025
4 min read

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:

  1. Non-alcoholic fatty liver (NAFL)

  2. Non-alcoholic steatohepatitis (NASH)

NAFL (Non-alcoholic fatty liver) - It is a benign condition and in this fat is accumulated in the liver that can cause inflammation of liver but not a progressive damage or complications of the liver. This may result in abdominal discomfort or pain due to an enlarged liver (hepatomegaly).

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.

image-20240513-101207.png

Location of raw data:

ls -l /work/training/data/sarek/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 │   ├── NAFLD3_P3_L001_R2_001.fastq.gz │   ├── 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/2025/S1W1/variant_calling/runs/run2_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/2025/S1W1/variant_calling/runs/run2_liver

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

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

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

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

python create_samplesheet_nf-core_sarek.py --dir $DIR \ --read1_extension R1.fastq.gz \ --read2_extension R2.fastq.gz \ --out samplesheet.csv

We will use a BASH script to create the samplesheet.csv file using the python script.

First copy the BASH script to your working directory:

cp /work/training/2025/S1W1/session2_variant_calling/scripts/run_create_sarek_samplesheet_liver.sh .

Print the script using the cat command:

cat run_create_sarek_samplesheet_liver.sh
image-20250317-001228.png

Run the script as follows. Note: after the script leave one space and then type the path to the input data folder:

bash run_create_sarek_samplesheet_liver.sh /work/training/data/sarek/WES/liver

Alternatively copy the samplesheet.csv file:

Check the newly created samplesheet.csv file:

ls -l cat samplesheet.cvs

The samplesheet.csv for the Case Study 2 looks like this:

patient,sample,lane,fastq_1,fastq_2

Control1,C1,L001,/sarek/data/WES/liver/Control1_C1_L001_R1_001.fastq.gz,/sarek/data/WES/liver/Control1_C1_L001_R2_001.fastq.gz

Control2,C2,L001,/sarek/data/WES/liver/Control2_C2_L001_R1_001.fastq.gz,/sarek/data/WES/liver/Control2_C2_L001_R2_001.fastq.gz

Control3,C3,L001,/sarek/data/WES/liver/Control3_C3_L001_R1_001.fastq.gz,/sarek/data/WES/liver/Control3_C3_L001_R2_001.fastq.gz

Control4,C4,L001,/sarek/data/WES/liver/Control4_C4_L001_R1_001.fastq.gz,/sarek/data/WES/liver/Control4_C4_L001_R2_001.fastq.gz

NAFLD1,P1,L001,/sarek/data/WES/liver/NAFLD1_P1_L001_R1_001.fastq.gz,/sarek/data/WES/liver/NAFLD1_P1_L001_R2_001.fastq.gz

NAFLD2,P2,L001,/sarek/data/WES/liver/NAFLD2_P2_L001_R1_001.fastq.gz,/sarek/data/WES/liver/NAFLD2_P2_L001_R2_001.fastq.gz

NAFLD3,P3,L001,/sarek/data/WES/liver/NAFLD3_P3_L001_R1_001.fastq.gz,/sarek/data/WES/liver/NAFLD3_P3_L001_R2_001.fastq.gz

NAFLD4,P4,L001,/sarek/data/WES/liver/NAFLD4_P4_L001_R1_001.fastq.gz,/sarek/data/WES/liver/NAFLD4_P4_L001_R2_001.fastq.gz

patient,sample,lane,fastq_1,fastq_2

Control1,C1,L001,/sarek/data/WES/liver/Control1_C1_L001_R1_001.fastq.gz,/sarek/data/WES/liver/Control1_C1_L001_R2_001.fastq.gz

Control2,C2,L001,/sarek/data/WES/liver/Control2_C2_L001_R1_001.fastq.gz,/sarek/data/WES/liver/Control2_C2_L001_R2_001.fastq.gz

Control3,C3,L001,/sarek/data/WES/liver/Control3_C3_L001_R1_001.fastq.gz,/sarek/data/WES/liver/Control3_C3_L001_R2_001.fastq.gz

Control4,C4,L001,/sarek/data/WES/liver/Control4_C4_L001_R1_001.fastq.gz,/sarek/data/WES/liver/Control4_C4_L001_R2_001.fastq.gz

NAFLD1,P1,L001,/sarek/data/WES/liver/NAFLD1_P1_L001_R1_001.fastq.gz,/sarek/data/WES/liver/NAFLD1_P1_L001_R2_001.fastq.gz

NAFLD2,P2,L001,/sarek/data/WES/liver/NAFLD2_P2_L001_R1_001.fastq.gz,/sarek/data/WES/liver/NAFLD2_P2_L001_R2_001.fastq.gz

NAFLD3,P3,L001,/sarek/data/WES/liver/NAFLD3_P3_L001_R1_001.fastq.gz,/sarek/data/WES/liver/NAFLD3_P3_L001_R2_001.fastq.gz

NAFLD4,P4,L001,/sarek/data/WES/liver/NAFLD4_P4_L001_R1_001.fastq.gz,/sarek/data/WES/liver/NAFLD4_P4_L001_R2_001.fastq.gz

Alternatively copy the samplesheet.csv file:

cp /work/training/sarek/data/WES/liver/samplesheet.csv .

 

STEP2 - Run the nf-core/sarek pipeline

Copy and paste the code below to the terminal:

cp $HOME/workshop/2025/S1W1/variant_calling/scripts/launch_nf-core_sarek_liver.pbs $HOME/workshop/2025/S1W1/variant_calling/runs/run2_liver cd $HOME/workshop/2025/S1W1/variant_calling/runs/run2_liver

  • 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_sarek_liver.pbs
image-20250316-230454.png

  • 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 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:

Windows PC

\\hpc-fs\work\training\sarek

Mac

smb://hpc-fs/work/training/sarek

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

Go to next section: 25S1W1 - Case study 3: (optional) Personal Genome Project - Han Chinese family trio

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