Running GermlineStructuralV-nf

To call structural variants for Illumina short read data, you will be running the GermlineStructuralV-nf pipeline. This pipeline was developed collaboratively by SIH Bioinformatics and ANZAC Neurogenetics groups. It leverages multiple structural variant detection methods to identify insertions, deletions, transversions, inversions, and duplications in whole genome sequence data. The pipeline is written in Nextflow and can be configured for multiple compute environments. It has been configured for NCI’s Gadi HPC and Pawsey’s Setonix HPC.

Considerations

Structural variant detection in short reads is currently challenging due to limitations of the data. This pipeline has been designed to maximise sensitivity and will likely return a large number of false positives. It is recommended that the pipeline is run using the AnnotSV tool, and the output variants be prioritised using the AnnotSV annotations.

For detailed instructions on how to run the pipeline, see the README.md file in the GermlineStructuralV-nf GitHub repository.

Instructions for running the pipeline on Gadi have been provided here and a pipeline execution script for Gadi has been provided along with the code.

Reference data on Gadi

Previous cohorts have been run using the Hg38 (chromosome only) and chm13-t2t assemblies. Copies of these reference and their index files are available at /g/data/<project>/reference on Gadi.

Quickstart guide

It is strongly suggested you read through the detailed instructions in the GermlineStructural-nf GitHub repository. Below is a quickstart guide to get you up and running if you are already familiar with the pipeline.

1. Download a copy of the GermineStructuralV-nf pipeline code to your Gadi environment

git clone https://github.com/Sydney-Informatics-Hub/Germline-StructuralV-nf.git

Move into this directory, this is where you will be working:

cd Germline-StructuralV-nf

2. Prepare your input file for all samples you wish to process

You can do this in a text editor or Excel if that is easier. Just ensure you have a file that has comma-separated columns with a header for each column. For example:

sample,bam,bai
sample1,/path/to/sample1.bam,/path/to/sample1.bam.bai
sample2,/path/to/sample2.bam,/path/to/sample2.bam.bai

3. Run the pipeline 4. View the results of the pipeline

Use the run_gadi.pbs script provided alongside the code inside the scripts/ directory. This script will submit all jobs to the Gadi HPC queue system. Before executing you will need to edit the script, how you do this will depend on how you intend to run the pipeline. Edit the following PBS variables at the top of the script:

#PBS -P <PROJECT>
#PBS -l storage=
  • Provide your group’s project code in place of <PROJECT>
  • Provide the storage you will be using for the pipeline in place of storage=. This can be scratch or gdata

For example:

#PBS -P aa00
#PBS -l storage=scratch/aa00+gdata/aa00

Next, edit the script variables to pass the required files and parameters to the pipeline:

ref= #REQUIRED: full path to your reference.fasta
samples= #REQUIRED: full path to your input.tsv file
annotsvDir= #OPTIONAL: full path to directory housing Annotations_Human directory
annotsvMode= #OPTIONAL: specift one of both|full|split. see annotation mode in https://github.com/lgmgeo/AnnotSV/blob/master/README.AnnotSV_3.3.4.pdf
outDir= #OPTIONAL: name of the results directory

Save your changes and run the script with:

qsub scripts/run_gadi.pbs

4. View the results of the pipeline

This pipeline runs multiple processes described here, most processes generate some output that you can view in your specified outdir. See here for a description of how the outputs are organised and what files are generated.

Resources

All materials copyright Sydney Informatics Hub, University of Sydney