Multispecies Mitochondrial Variant Analysis

This workflow performs a variant analysis on mitochondrial genomes using the bcftools variant caller. For human samples, the workflow also performs a variant analysis using mutserve. It is listed in the Snakemake Workflow Catalog where usage of standardized Snakemake workflows is described.

Local installation

1. Clone this repo

git clone https://github.com/tlenfers/multispecies_mitochondrial_variant_analysis.git
cd multispecies_mitochondrial_variant_analysis

2. Install dependencies

# download Miniconda3 installer
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
# install Conda (respond by 'yes')
bash miniconda.sh
# update Conda
conda update -y conda
# setup channels 
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
# create & activate new env with installed deps
conda env create -n wf -f environment.yaml
source activate wf

Configuration

Config files:

• config.yaml - analysis-specific settings
• environment.yaml - software dependencies and versions
• samples.tsv - list of (paired) samples

Samples:

• Put all sample names in a single column in samples.tsv.
• Add the data folder to config.yaml where all files to be analysed are located
  • standard path is set to data/
• assumed naming convention:
  • sampleName_R1.fastq.gz
  • sampleName_R2.fastq.gz

Reference:

To analyse dog, mouse or human samples the corresponding reference will be downloaded. Define the to be analysed species in config.yaml under reference.

If you want to analyse a different species or use your own reference, enter the name of the file and it's path in config.yaml.

Execute the workflow

cd workflow
# 'dry' run only checks I/O files
snakemake -np

# To run mutlipecies variant analysis
snakemake -j n all --use-conda --use-singularity
# where n is the numer of cores to use

# To run human variant analysis with mutserve
snakemake -j n all_human --use-conda --use-singularity
# where n is the numer of cores to use

Output

All output files are put in /results and in their own subfolder regarding the used reference and caller.
The results are in a sub-folder corresponding to the name of the reference file used.

• /results/calls_bcftools contains all called variants using bcftools. The variants are normalized and saved as sample_name.vcf.gz. In addition, the file mergerd.vcf is created in which all variants are merged together.
  • Example file without header:

    #CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	results/mapped/human/sample_name.bam
    chrM	73	.	A	G	225.417	.	DP=253;VDB=3.59147e-17;SGB=-0.693147;MQSBZ=0;FS=0;MQ0F=0;AC=1;AN=1;DP4=0,0,240,5;MQ=60	GT:PL	1:255,0
    chrM	146	.	T	C	225.422	.	DP=242;VDB=0.795672;SGB=-0.693147;MQSBZ=0;FS=0;MQ0F=0;AC=1;AN=1;DP4=0,0,165,52;MQ=60	GT:PL	1:255,0

• /results/calls_mutserve contains all called variants using mutserve.
• /results/mapped contains all aligned reads as sample_name.bam ad their index file sample_name.bam.bai.
• /results/plots contains the created heatmap plots for the bctools caller. Example plots:
  • ref_heatmap.pdf
  • ref_heatmap_clusterrow.pdf
  • The name of the samples is on the X-axis, the variants on the Y-axis
  • The values of the heatmap refer to the Phred-scaled likelihood for homomorphic reference allele (scale 0-255; 255: reference is very unlikely -> alternative more likely).
  • The plots of alt_heatmap are containing the Phred-scaled likelihood for homomorphic alternative allele, i.e. that the variant is present at this position (scale 0-255; 0: variant is present).
• /results/sequences contains the created consensus sequences for each sample in regard to the used reference in fasta format as sample_name.fa.

Snakedeploy usage

The usage of this workflow is described in the Snakemake Workflow Catalog.

# To run human variant analysis with mutserve
snakemake --cores all all_human --use-conda --use-singularity 

If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this (original) repository.

Planned features

This pipeline is work in progress. The following features are planned:

• mutserve caller:
  • extraction of common variants
  • plotting variant heatmaps based on variants called by mutserve
  • building the consensus sequence
• additional quality control strep:
  • remove samples with low coverage from further analysis
• dynamic sizing of the heatmap based on samples and variants