jlanillos / mtx-mtdna

mtDNA variation analysis from mtx-WGS paired-tumor/normal samples

mtdna-dlp

scripts to generate figures/tables for mtDNA DLP+ paper

Installation (in the command line)

Clone this repository (git clone https://github.com/reznik-lab/mtdna-dlp.git)

Install miniconda: https://docs.conda.io/en/latest/miniconda.html

Create a new conda environment and activate it (make sure it is activated before installing the dependencies):

conda create -n [environment]
conda activate [environment]

Install the following:

• VEP (https://anaconda.org/bioconda/ensembl-vep, version 95.3 recommended)
• vt (https://anaconda.org/bioconda/vt)
• pybedtools (https://anaconda.org/bioconda/pybedtools)
• bcftools (https://anaconda.org/bioconda/bcftools)
• samtools (https://anaconda.org/bioconda/samtools)
• vcf2maf (https://anaconda.org/bioconda/vcf2maf)
• pysam (https://anaconda.org/bioconda/pysam)
• gatk4 (https://anaconda.org/bioconda/gatk4)
• biopython (https://anaconda.org/conda-forge/biopython)
• numpy (https://anaconda.org/anaconda/numpy)
• pandas (https://anaconda.org/anaconda/pandas)
• matplotlib (https://anaconda.org/conda-forge/matplotlib)

conda install -c bioconda ensembl-vep=95.3
conda install -c bioconda vt
conda install -c bioconda pybedtools
conda install -c bioconda bcftools
conda install -c bioconda samtools
conda install -c bioconda vcf2maf
conda install -c bioconda pysam
conda install -c bioconda gatk4
conda install -c conda-forge biopython
conda install numpy
conda install pandas
conda install -c conda-forge matplotlib

Additionally, a VEP offline cache needs to be installed (NOTE: CACHE MUST BE SAME VERSION AS VEP VERSION). Please refer to https://uswest.ensembl.org/info/docs/tools/vep/script/vep_cache.html for instructions on how to install a VEP cache. Due to the size of the caches, it will likely take several hours to install.

Running the single cell pipeline (in the command line)

Navigate to the directory with the scMTpipeline.py file and run the following (replace all brackets):

conda activate [environment]
python3 scMTpipeline.py -d [data_directory] -r [reference_fasta] -w [working_directory] -l [library_id] -re [results_directory] -vc [optional_vep_cache_directory] -q [optional_mapping_quality] -Q [optional_base_quality] -s [optional_strand] -p [optional_patternlist] -t [optional_threshold]

Parameter descriptions:

• Data directory: path to directory with input .bam files
• Reference fasta: path to fasta file (recommended to use [working_directory]/reference/b37/b37_MT.fa for GRCh37 or [working_directory]/reference/GRCh38/genome_MT.fa for GRCh38)
• Working directory: path to directory with scMTpipeline.py file in it
• Library ID: name of .bam file to use as input
• Results directory: path to directory where results will be stored
• (OPTIONAL )VEP cache directory: path to directory with VEP cache
• (OPTIONAL) Mapping quality: minimum mapping quality (default=20)
• (OPTIONAL) Base qualtiy: minimum base quality (default=20)
• (OPTIONAL) Strand: minimum number of reads mapping to forward and reverse strand to call mutation (default=2)
• (OPTIONAL) Patternlist: file containing a list of filenames to process at a time (to be used when there are many files to process)
• (OPTIONAL) Threshold: critical threshold for calling a cell wild-type (default=0.1)

For example, a call to run the single cell pipeline with the minimum paramaters could look like this:

python3 scMTpipeline.py -d /my_data/ -r /my_home/mtdna-dlp/python/reference/b37/b37_MT.fa -w /my_home/mtdna-dlp/python/ -l my_file -re /my_home/mtdna-dlp/results/

To run scMTpipeline.py on the provided example data:

python3 scMTpipeline.py -d /mtdna-dlp/python/example_data/ -r /mtdna-dlp/python/reference/b37/b37_MT.fa -w /mtdna-dlp/python/ -l SA1101-A96155C -re /mtdna-dlp/python/results