marrip/wgs_somatic_pon

Create panel of normals for different tools

wgs_somatic_pon

Create panel of normals for different tools

💬 Introduction

This snakemake workflow is designed to generate panel of normal (PoN) files for a plethora of tools. Which can in turn be used for variant calling analyses.

Mutect2

The PoN is generated according to the best practices from GATK as described in this tutorial. For each .bam file of a normal sample variants are called using mutect2 and combined into a .vcf file representing the PoN.

CNVkit

For CNVkit, the docs talk about a reference of pooled normals which is generated in two steps from .bam files of normal samples.

❗ Dependencies

To run this workflow, the following tools need to be available:

🎒 Preparations

Sample data

1. Add all sample ids to samples.tsv in the column sample.
2. Add sample type information, normal or tumor, to units.tsv.
3. Use the analysis_output folder from wgs_std_viper as input.

Reference data

1. You need a reference .fasta file representing the genome used for mapping. For the different tools to work, you also need to prepare index files and a .dict file.

• The required files for the human reference genome GRCh38 can be downloaded from google cloud. The download can be manually done using the browser or using gsutil via the command line:

gsutil cp gs://genomics-public-data/resources/broad/hg38/v0/Homo_sapiens_assembly38.fasta /path/to/download/dir/

• If those resources are not available for your reference you may generate them yourself:

samtools faidx /path/to/reference.fasta
gatk CreateSequenceDictionary -R /path/to/reference.fasta -O /path/to/reference.dict

2. In order to split the variant calling process by chromosome, a locus file containg a list of all available chromosomes is used in the analysis.
3. Mutect2 requires an .interval_list file which needs to be supplied. For GRCh38, the file is also available in the google bucket.
4. Mutect2 also requires a modified gnomad database as a .vcf.gz. For GRCh38, the file can be retrieved from google cloud as described under 1.
5. Add the paths of the different files to the config.yaml. The index files should be in the same directory as the reference .fasta.
6. Make sure that the docker container versions are correct.

✅ Testing

The workflow repository contains a small test dataset .tests/integration which can be run like so:

cd .tests/integration
snakemake -s ../../workflow/Snakefile -j1 --use-singularity

🚀 Usage

The workflow is designed for WGS data meaning huge datasets which require a lot of compute power. For HPC clusters, it is recommended to use a cluster profile and run something like:

snakemake -s /path/to/Snakefile --profile my-awesome-profile

🧑‍⚖️ Rule Graph