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Test and run the nextflow nf-core/sarek pipeline in the HPC cluster using public data. Exercises include:
- Running a test to verify the execution of the pipeline
- Running the sarek variant calling pipeline with a HapMap trio data
- Running the sarek variant calling pipeline with liver samples

Work in the HPC

Before we start using the HPC, let’s start an interactive session:

Code Block
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:

Code Block
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

Code Block

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:

Code Block
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

Code Block
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:

Code Block
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

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:

Code Block
qsub launch_nf-core_sarek_test.pbs

Monitoring the Run

Code Block
qjobs

Outputs:

The test run should take about ~14 min to complete. Find run outputs in the “results” folder:

Code Block

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:

...

Code Block
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:

Code Block
cd $HOME/workshop/sarek/data/trio pwd

Copy the python script “create_samplesheet_nf-core_sarek.py" to the working folder

Code Block
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:

Code Block
python create_samplesheet_nf-core_sarek.py --help

Code Block
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

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

...

Exercise 2: Run nf-core/sarek using a family trio data (HapMap; Genome in a Bottle)

Public data

Family ID: 1463
Family information: family lineage from Utah of four grandparents, two parents, and 11 children (17 family members)
Genomics consortia: Genome in a Bottle, 1000 Genomes Project, International HapMap Project, Centre d'Etude du Polymorphisme Humain (CEPH)

CEPH is an international genetic research center that provides a resource of immortalized cell cultures used to map genetic markers. The whole pedigree was sequenced to 50x depth on a HiSeq 2000 Illumina system, which is considered a platinum standard, where platinum refers to the quality and completeness of the resulting assembly, such as providing full chromosome scaffolds with phasing and haplotypes resolved across the entire genome.

...

This figure depicts the pedigree of the family sequenced for this study, where the ID for each sample is defined by adding the prefix NA128 to each numbered individual, so that 77 = NA12877 and 78 = NA12878, corresponding to the VCF tracks available in this track set. The dark orange individuals indicate sequences used in the analysis methods, whereas the blue represent the founder generations (grandparents), which were also sequenced and used in validation steps. The genomes of the parent-child trio on the top right side, 91-92-78, were also sequenced during Phase I of the 1000 Genomes Project.

Sample ID

Description

Biological sample source

NA12878 (Daughter)

Mother; donor subject has a single bp (G-to-A) transition at nucleotide 681 in exon 5 of the CYP2C19 gene (CYP2C19*2) which creates an aberrant splice site. The change altered the reading frame of the mRNA starting with amino acid 215 and produced a premature stop codon 20 amino acids downstream, resulting in a truncated, nonfunctional protein. Because of the aberrant splice site, a 40-bp deletion occurred at the beginning of exon 5 (from bp 643 to bp 682), resulting in deletion of amino acids 215 to 227. The truncated protein had 234 amino acids and would be catalytically inactive because it lacked the heme-binding region.

https://catalog.coriell.org/0/Sections/Search/Sample_Detail.aspx?Ref=NA12878&Product=DNA

NA12891 (Father)

Maternal Grandfather; donor subject is homozygous for a single bp (G-to-A) transition at nucleotide 681 in exon 5 of the CYP2C19 gene (CYP2C19*2) which creates an aberrant splice site. The change altered the reading frame of the mRNA starting with amino acid 215 and produced a premature stop codon 20 amino acids downstream, resulting in a truncated, nonfunctional protein. Because of the aberrant splice site, a 40-bp deletion occurred at the beginning of exon 5 (from bp 643 to bp 682), resulting in deletion of amino acids 215 to 227. The truncated protein had 234 amino acids and would be catalytically inactive because it lacked the heme-binding region.

https://catalog.coriell.org/0/Sections/Search/Sample_Detail.aspx?Ref=NA12891&Product=DNA

NA12892 (Mother)

Maternal Grandmother

https://catalog.coriell.org/0/Sections/Search/Sample_Detail.aspx?Ref=NA12892&Product=DNA

Location of raw data:

Code Block
/work/training/sarek/data/WES/trio

Code Block

├── trio
│   ├── samplesheet.csv
│   ├── SRR14724455_NA12892a_L001_R1.fastq.gz
│   ├── SRR14724455_NA12892a_L001_R2.fastq.gz
│   ├── SRR14724456_NA12891a_L001_R1.fastq.gz
│   ├── SRR14724456_NA12891a_L001_R2.fastq.gz
│   ├── SRR14724463_NA12878a_L001_R1.fastq.gz
│   ├── SRR14724463_NA12878a_L001_R2.fastq.gz
│   ├── SRR14724474_NA12892b_L001_R1.fastq.gz
│   ├── SRR14724474_NA12892b_L001_R2.fastq.gz
│   ├── SRR14724475_NA12891b_L001_R1.fastq.gz
│   ├── SRR14724475_NA12891b_L001_R2.fastq.gz
│   ├── SRR14724483_NA12878b_L001_R1.fastq.gz
│   └── SRR14724483_NA12878b_L001_R2.fastq.gz

Where:

Pipeline requirements

The pipeline requires preparing at least 2 files:

Metadata file (samplesheet.csv) thatspecifies the following information:

Code Block
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 (see below)

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

List FASTQ files in the data folder directory of the family trio:

Code Block
ls -l /work/training/sarek/data/WES/trio

Copy the python script “create_samplesheet_nf-core_sarek.py

...

" to the working (run2) folder

Code Block
cp $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:

Code Block
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

...

scripts/create_samplesheet_nf-core_sarek.py $HOME/workshop/sarek/run2_trio
cd $HOME/workshop/sarek/run2_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:

Code Block
python create_samplesheet_nf-core_sarek.py --help

Code Block
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

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

Code Block
python create_samplesheet_nf-core_sarek.py --dir /work/training/sarek/data/WES/trio

...

 \
  --read1_extension R1.fastq.gz \
  --read2_extension R2.fastq.gz \
  --out samplesheet.csv

Alternatively copy the samplesheet.csv file:

Code Block
cp /work/training/sarek/data/WES/trio/

...

samplesheet.csv .

Check the newly created samplesheet.csv file:

Code Block
ls -l cat samplesheet.csv

patient,sample,lane,fastq_1,fastq_2

SRR14724455,NA12892a,L001,/sarek/data/WES/trio/

SRR14724483
SRR14724455_
NA12878b
NA12892a_L001_R1.fastq.gz,
/work/training
/sarek/data/WES/trio/
SRR14724483
SRR14724455_
NA12878b
NA12892a_L001_R2.fastq.gz

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:

...

SRR14724456,NA12891a,L001,/sarek/data/WES/trio/SRR14724456_NA12891a_L001_R1.fastq.gz,/sarek/data/WES/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:

Code Block
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

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:

Code Block
qsub launch_nf-core_sarek_trio.pbs

Monitoring the Run

Code Block
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

Code Block
\\hpc-fs\work\training\rnaseq

Mac

Code Block
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
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:

Code Block
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:

Code Block
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:

Code Block
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 "

Submitting the job

Code Block
qsub launch_nf-core_RNAseq_pipeline.pbs

Monitoring the Run

Code Block
qjobs

Outputs

...

SRR14724456_NA12891a_L001_R2.fastq.gz

SRR14724463,NA12878a,L001,/sarek/data/WES/trio/SRR14724463_NA12878a_L001_R1.fastq.gz,/sarek/data/WES/trio/SRR14724463_NA12878a_L001_R2.fastq.gz

SRR14724474,NA12892b,L001,/sarek/data/WES/trio/SRR14724474_NA12892b_L001_R1.fastq.gz,/sarek/data/WES/trio/SRR14724474_NA12892b_L001_R2.fastq.gz

SRR14724475,NA12891b,L001,/sarek/data/WES/trio/SRR14724475_NA12891b_L001_R1.fastq.gz,/sarek/data/WES/trio/SRR14724475_NA12891b_L001_R2.fastq.gz

SRR14724483,NA12878b,L001,/sarek/data/WES/trio/SRR14724483_NA12878b_L001_R1.fastq.gz,/sarek/data/WES/trio/SRR14724483_NA12878b_L001_R2.fastq.gz

STEP 2: running the nf-core/sarek pipeline (launch_nf-core_sarek_trio.pbs)

Copy and paste the code below to the terminal:

Code Block
cp $HOME/workshop/sarek/scripts/launch_nf-core_sarek_trio.pbs $HOME/workshop/sarek/run2_trio cd $HOME/workshop/sarek/run2_trio

Line 1: copy the launch_nf-core_sarek_trio.pbs submission script to the working directory
Line 2: move to the working directory

View the content of the launch_nf-core_sarek_trio.pbs script:

Code Block
cat launch_nf-core_sarek_trio.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

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”
Pipeline steps:
- mapping
- markduplicates
- prepare_recalibration
- recalibrate
- variant_calling
- annotation
  - See more information on the input requirements for each step https://nf-co.re/sarek/3.3.2/docs/usage

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:

Code Block
qsub launch_nf-core_sarek_trio.pbs

Monitoring the Run

Code Block
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

Code Block
\\hpc-fs\work\training\sarek

Mac

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

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:

Code Block

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_20-14-17.html
│   ├── execution_timeline_2024-05-11_20-14-17.html
│   ├── execution_trace_2024-05-11_20-14-17.txt
│   ├── params_2024-05-12_00-00-57.json
│   ├── pipeline_dag_2024-05-11_20-14-17.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

Versions Compared

Old Version 2

New Version Current

Key

Exercise 1: Running a test with nf-core sample data

Exercise 2: Run nf-core/sarek using trio data

Create the metadata file (samplesheet.csv):

Exercise 2: Run nf-core/sarek using a family trio data (HapMap; Genome in a Bottle)

Public data

Pipeline requirements

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

Submitting the job

Monitoring the Run

Once the pipeline has finished running - Assess the QC report:

Exercise 3: Healthy vs. disease liver samples

Adjusting the Trim Galore (read trimming) options

Submitting the job

Monitoring the Run

Outputs

Submitting the job

Monitoring the Run

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

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Exercise 1: Running a test with nf-core sample data

Exercise 2: Run nf-core/sarek using trio data

Create the metadata file (samplesheet.csv):

Exercise 2: Run nf-core/sarek using a family trio data (HapMap; Genome in a Bottle)

Public data

Pipeline requirements

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

Submitting the job

Monitoring the Run

Once the pipeline has finished running - Assess the QC report:

Exercise 3: Healthy vs. disease liver samples

Adjusting the Trim Galore (read trimming) options

Submitting the job

Monitoring the Run

Outputs

Submitting the job

Monitoring the Run

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