Page Comparison

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)

...

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.

Location of raw data:

/work/training/sarek/data/WES/trio

...

• Exome sequencing of Homo sapiens: NA12878a, A12891a, NA12892a with Illumina NovaSeq 6000 TruSeq capture

• Exome sequencing of Homo sapiens: NA12878b, A12891b, NA12892b with Illumina NovaSeq 6000 Agilent SureSelect v7 capture

Pipeline requirements

The pipeline requires preparing at least 2 files:

• Metadata file (samplesheet.csv) thatspecifies 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 (see below)

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

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

...

cp $HOME/workshop/sarek/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:

...

python create_samplesheet_nf-core_sarek.py --dir $HOME/work/workshoptraining/sarek/data/WES/trio \
  --read1_extension R1.fastq.gz \
  --read2_extension R2.fastq.gz \
  --out samplesheet.csv

Alternatively copy the samplesheet.csv file:

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

Check the newly created samplesheet.csv file:

ls -l
cat samplesheet.cvscsv

Copy the PBS Pro script for STEP 2: 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/scripts/launch_nf-core_sarek_trio.pbs $HOME/workshop/sarek/runs/run2_trio
cd $HOME/workshop/sarek/runs/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:

...

• 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:

...

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:

...

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

...