tdayris/fair_gatk_mutect2
Snakemake workflow used to call germline and/or somatic variants with GATK Mutect2
Overview
Topics: fair gatk-bestpractices mutect2 reproducible-workflows snakemake snakemake-workflow variant-calling
Latest release: 1.5.4, Last update: 2024-07-22
Linting: linting: passed, Formatting:formatting: failed
Deployment
Step 1: Install Snakemake and Snakedeploy
Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.
When using Mamba, run
mamba create -c conda-forge -c bioconda --name snakemake snakemake snakedeploy
to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via
conda activate snakemake
Step 2: Deploy workflow
With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:
mkdir -p path/to/project-workdir
cd path/to/project-workdir
In all following steps, we will assume that you are inside of that directory. Then run
snakedeploy deploy-workflow https://github.com/tdayris/fair_gatk_mutect2 . --tag 1.5.4
Snakedeploy will create two folders, workflow
and config
. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.
Step 3: Configure workflow
To configure the workflow, adapt config/config.yml
to your needs following the instructions below.
Step 4: Run workflow
The deployment method is controlled using the --software-deployment-method
(short --sdm
) argument.
To run the workflow with automatic deployment of all required software via conda
/mamba
, use
snakemake --cores all --sdm conda
To run the workflow using a combination of conda
and apptainer
/singularity
for software deployment, use
snakemake --cores all --sdm conda apptainer
Snakemake will automatically detect the main Snakefile
in the workflow
subfolder and execute the workflow module that has been defined by the deployment in step 2.
For further options such as cluster and cloud execution, see the docs.
Step 5: Generate report
After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using
snakemake --report report.zip
Configuration
The following section is imported from the workflow’s config/README.md
.
This pipeline requires two configuration file:
A standard Snakemake
configuration, yaml-formatted file containing a list of
all parameters accepted in this workflow:
-
samples
: Path to the file containing link between samples and their fastq file(s) -
params
: Per-tool list of optional parameters
Example:
samples: config/samples.csv
Optional parameters
params: fait_gatk_mutect2_gatk_filtermutectcalls: “–create-output-variant-index –min-median-mapping-quality 35 –max-alt-allele-count 3”
A complete list of accepted keys is available in schemas, with their default value, expected type, and human readable description.
A CSV-formatted text file containing the following mandatory columns:
- sample_id: Unique name of the sample
- upstream_file: Path to upstream fastq file
- species: The species name, according to Ensembl standards
- build: The corresponding genome build, according to Ensembl standards
- release: The corresponding genome release, according to Ensembl standards
- downstream_file: Optional path to downstream fastq file, leave it empty in case of single ended library
- normal_sample_id: Optional identifier of a normal sample to be used in a tumor/normal pair
Example:
sample_id,upstream_file,downstream_file,species,build,release,normal_id
sac_a,data/reads/a.scerevisiae.1.fq,data/reads/a.scerevisiae.2.fq,saccharomyces_cerevisiae,R64-1-1,105,
sac_b,data/reads/b.scerevisiaeI.1.fq,data/reads/b.scerevisiaeI.2.fq,saccharomyces_cerevisiae,R64-1-1,105,sac_a
A complete list of accepted keys is available in schemas, with their default value, expected type, and human readable description.
While CSV
format is tested and recommended, this workflow uses python
csv.Sniffer()
to detect column separator. Tabulation and semicolumn are
also accepted as field separator. Remember that only comma-separator is
tested.
This file is fully optional. When missing, the genome sequences will be downloaded from Ensembl and indexed.
A CSV-formatted text file containing the following mandatory columns:
-
species
: The species name, according to Ensembl standards -
build
: The corresponding genome build, according to Ensembl standards -
release
: The corresponding genome release, according to Ensembl standards
The following columns are optional and are used to avoid downloading genomes:
-
fasta
: Path to the reference genome sequence (FASTA formatted) -
fasta_index
: Path to the reference genome sequence index (FAI formatted) -
bowtie2_dna_index
: Path to the main directory containing reference index
Example:
species,build,release,fasta,fasta_index,bowtie2_index
homo_sapiens,GRCh38,105,/path/to/sequence.fasta,/path/to/sequence.fasta.fai,/path/to/bowtie2_sequence/
mus_musculus,GRCm38,99,,,
mus_musculus,GRCm39,105,,,
A complete list of accepted keys is available in schemas, with their default value, expected type, and human readable description.
Linting and formatting
Linting results
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Formatting results
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[INFO] 5 file(s) would be changed 😬
[INFO] 6 file(s) would be left unchanged 🎉
snakefmt version: 0.10.2