SomaticShortV-nf

:wrench: This pipeline is currently under development :wrench:

• Description
• Diagram
• User guide
  • Infrastructure usage and recommendations
• Benchmarking
• Workflow summaries
  • Metadata
  • Component tools
• Additional notes
• Help/FAQ/Troubleshooting
• Acknowledgements/citations/credits

Description

SomaticShortV-nf is a Nextflow pipeline for identifying somatic short variant events in Illumina short read whole genome sequence data. We have followed the GATK Best practices . The input to this pipeline are the per sample "g.vcf" files which are generated using nf-core/sarek.

There are three main steps in the process of calling Somatic Short Variants:

(1) Creation of Somatic short variants Panel of Normals (PON) :
This involves converting the Normal BAMs to PON. The PON's are -

• Made from normal samples i.e. the samples derived from healthy tissue and
• Their main purpose is to capture recurrent technical artifacts in order to improve the results of the variant calling analysis.
  

(2) Call the somatic short variants for a tumor-normal pair :
The two main steps involved in calling Somatic Short Variants are -

• Generate a large set of candidate somatic variants,
• Filter the candidate somatic variants into a more confident set of somatic variant calls.

• Calling candidate somatic short variants involved the use of the tool Mutect2 which calls both SNVs and indels simultaneously by generating a local assembly of haplotypes in an active region, de-novo. When Mutect2 sees a region with somatic variations, it dis-regards the existing mapping information completely and re-assembles the reads in that region in order to generate candidate variant haplotypes.

• This was followed by calculating contamination using the tools GetPileupSummaries and CalculateContamination

• The next step was to learn the parameters of a model for orientation bias using the tool LearnReadOrientationModel

• The FilterMutectCalls tool then applies filters to the raw output of Mutect2.

(3) Annotating the variants
An external genomic variant annotations and functional effect prediction tool SnpEff was used for annotating the filtered variants (such as amino-acid changes etc). Please refer to the above link for SnpEff details.

Diagram

User guide

To run this pipeline, you will need to prepare your input files, reference data, and clone this repository. Before proceeding, ensure Nextflow is installed on the system you're working on. To install Nextflow, see these instructions.

1. Prepare inputs

To run this pipeline you will need the following inputs:

• Paired-end BAM files
• Corresponding BAM index files
• Input sample sheet

This pipeline processes paired-end BAM files and is capable of processing multiple samples in parallel. BAM files are expected to be coordinate sorted and indexed (see Fastq-to-BAM for an example of a best practice workflow that can generate these files).

You will need to create a sample sheet with information about the samples you are processing, before running the pipeline. This file must be tab-separated and contain a header and one row per sample. Columns should correspond to sampleID, BAM file, BAI file:

When you run the pipeline, you will use the mandatory --input parameter to specify the location and name of the input file:

--input /path/to/samples.tsv

2. Prepare the reference materials

To run this pipeline you will need the following reference files:

• Indexed reference genome in FASTA format

You will need to download and index a copy of the reference genome you would like to use. Reference FASTA files must be accompanied by a .fai index file. If you are working with a species that has a public reference genome, you can download FASTA files from the Ensembl, UCSC, or NCBI ftp sites. You can use our IndexReferenceFasta-nf pipeline to generate indexes.

When you run the pipeline, you will use the mandatory --ref parameter to specify the location and name of the reference.fasta file:

--ref /path/to/reference.fasta

3. Clone this repository

Download the code contained in this repository with:

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

This will create a directory with the following structure:

SomaticShortV-nf/
├── LICENSE
├── README.md
├── config/
├── main.nf
├── modules/
└── nextflow.config

The important features are:

• main.nf contains the main nextflow script that calls all the processes in the workflow.
• nextflow.config contains default parameters to use in the pipeline.
• modules contains individual process files for each step in the workflow.
• config contains infrastructure-specific config files (this is currently under development)

4. Run the pipeline

The most basic run command for this pipeline is:

nextflow run main.nf --ref reference.fasta

By default, this will generate work directory, results output directory and a runInfo run metrics directory in the same location you ran the pipeline from.

To specify additional optional tool-specific parameters, see what flags are supported by running:

nextflow run main.nf --help 

If for any reason your workflow fails, you are able to resume the workflow from the last successful process with -resume.

Infrastructure useage and recommendations

Coming soon!

Benchmarking

Coming soon!

Workflow summaries

Metadata

Component tools

To run this pipeline you must have Nextflow and Singularity installed on your machine. All other tools are run using containers.

Additional notes

Resources

• Nextflow documentation

Help/FAQ/Troubleshooting

• It is essential that the reference genome you're using contains the same chromosomes, contigs, and scaffolds as the BAM files. To confirm what contigs are included in your indexed BAM file, you can use Samtools idxstats:

  samtools idxstats input.bam | cut -f 1

Acknowledgements/citations/credits

Authors

• Nandan Deshpande (Sydney Informatics Hub, University of Sydney)
• Georgie Samaha (Sydney Informatics Hub, University of Sydney)

Acknowledgements

• This pipeline was built using the Nextflow DSL2 template.
• Documentation was created following the Australian BioCommons documentation guidelines.

Cite us to support us!

Acknowledgements (and co-authorship, where appropriate) are an important way for us to demonstrate the value we bring to your research. Your research outcomes are vital for ongoing funding of the Sydney Informatics Hub and national compute facilities. We suggest including the following acknowledgement in any publications that follow from this work:

The authors acknowledge the technical assistance provided by the Sydney Informatics Hub, a Core Research Facility of the University of Sydney and the Australian BioCommons which is enabled by NCRIS via Bioplatforms Australia.