The snakyVC pipeline is designed for efficiently and parallelly executing variant calling on next-generation sequencing (NGS) whole-genome datasets.

In order to run the snakyVC pipeline, users need to install Miniconda and prepare the Miniconda environment in their computing systems.

The required software, programming languages, and packages include:

bwa>=0.7.17
gatk4>=4.4.0.0
samtools>=1.6
htslib>=1.3
python>=3.12
snakemake>=8.4
scipy>=1.12
numpy>=1.26
pandas>=2.2

Miniconda can be downloaded from https://docs.anaconda.com/free/miniconda/.

For example, if users plan to install Miniconda3 Linux 64-bit, the wget tool can be used to download the Miniconda.

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

To install Miniconda in a server or cluster, users can use the command below.

Please remember to replace the <installation_shell_script> to the actual Miniconda installation shell script. In our case, it is Miniconda3-latest-Linux-x86_64.sh.

Please also remember to replace the <desired_new_directory> to an actual directory absolute path.

chmod 777 -R <installation_shell_script>
./<installation_shell_script> -b -u -p <desired_new_directory>
rm -rf <installation_shell_script>

After installing Miniconda, initialization of Miniconda for bash shell can be done using the command below.

Please also remember to replace the <desired_new_directory> to an actual directory absolute path.

<desired_new_directory>/bin/conda init bash

Installation of the Miniconda is required, and Miniconda environment needs to be activated every time before running the snakyVC pipeline.

Write a Conda configuration file (.condarc) before creating a Conda environment:

nano ~/.condarc

Put the following text into the Conda configuration file (make sure you change envs_dirs and pkgs_dirs) then save the file.

Please make sure not use tab in this yaml file, use 4 spaces instead.

Please make sure to replace /new/path/to/ to an actual directory absolute path.

envs_dirs:
    - /new/path/to/miniconda/envs
pkgs_dirs:
    - /new/path/to/miniconda/pkgs
channels:
    - conda-forge
    - bioconda
    - defaults

Create a Conda environment by specifying all required packages (option 1).

Please make sure to replace the <conda_environment_name> to an environment name of your choice.

conda create -n <conda_environment_name> bioconda::gatk4 bioconda::samtools bioconda::bcftools bioconda::htslib \
bioconda::bedtools bioconda::bwa bioconda::snakemake bioconda::snakemake-executor-plugin-cluster-generic \
conda-forge::numpy conda-forge::pandas conda-forge::scipy

Create a Conda environment by using a yaml environment file (option 2).

Please make sure to replace the <conda_environment_name> to an environment name of your choice.

conda create --name <conda_environment_name> --file snakyVC-environment.yml

Create a Conda environment by using a explicit specification file (option 3).

Please make sure to replace the <conda_environment_name> to an environment name of your choice.

conda create --name <conda_environment_name> --file snakyVC-spec-file.txt

Activate Conda environment using conda activate command.

This step is required every time before running snakyVC pipeline.

Please make sure to replace the <conda_environment_name> to an environment name of your choice.

conda activate <conda_environment_name>

You can install the snakyVC from Github with:

git clone https://github.com/yenon118/snakyVC.git

Please save the file with .json extension.

{
	"project_name": "Test",
	"workflow_path": "/scratch/yenc/projects/snakyVC",
	"input_files_1": [
		"/scratch/yenc/projects/snakyVC/data/CCCRR108616_1.fastq",
		"/scratch/yenc/projects/snakyVC/data/CCCRR108617_1.fastq",
		"/scratch/yenc/projects/snakyVC/data/CCCRR108618_1.fastq",
		"/scratch/yenc/projects/snakyVC/data/CCCRR108619_1.fastq"
	],
	"input_files_2": [
		"/scratch/yenc/projects/snakyVC/data/CCCRR108616_2.fastq",
		"/scratch/yenc/projects/snakyVC/data/CCCRR108617_2.fastq",
		"/scratch/yenc/projects/snakyVC/data/CCCRR108618_2.fastq",
		"/scratch/yenc/projects/snakyVC/data/CCCRR108619_2.fastq"
	],
	"reference_file": "/scratch/yenc/projects/snakyVC/data/Wm82.a2.v1.subset.fa",
	"output_folder": "/scratch/yenc/projects/snakyVC/output/",
	"memory": 100,
	"threads": 10
}

snakemake -j NUMBER_OF_JOBS --configfile CONFIGURATION_FILE --snakefile SNAKEMAKE_FILE

Mandatory Positional Argumants:
	NUMBER_OF_JOBS                          - the number of jobs
	CONFIGURATION_FILE                      - a configuration file
	SNAKEMAKE_FILE                          - the snakyVC.smk file that sit inside this repository

Below are some fundamental examples illustrating the usage of the snakyVC pipeline.

Please adjust /path/to/ to an actual directory absolute path.

Examples of running without an executor.

cd /path/to/snakyVC

snakemake -j 4 --configfile inputs.json --snakefile snakyVC.smk

cd /path/to/snakyVC

snakemake -j 4 --configfile workflow_inputs/BWA_alignment_to_GATK_HaplotypeCaller_inputs.json \
--snakefile workflows/BWA_alignment_to_GATK_HaplotypeCaller.smk

cd /path/to/snakyVC

snakemake -j 9 --configfile lewis_slurm_inputs.json --snakefile snakyVC.smk

Examples of running with an executor.

Snakemake version >= 8.0.0.

cd /path/to/snakyVC

snakemake --executor cluster-generic \
--cluster-generic-submit-cmd "sbatch --account=xulab --time=1-21:00 \
--nodes=1 --ntasks=1 --cpus-per-task=3 \
--partition=Lewis,BioCompute,hpc5,General --mem=16G" \
--jobs 30 --latency-wait 180 --configfile lewis_slurm_inputs.json \
--snakefile snakyVC.smk

Snakemake version < 8.0.0.

cd /path/to/snakyVC

snakemake --cluster "sbatch --account=xulab --time=1-21:00 \
--nodes=1 --ntasks=1 --cpus-per-task=3 \
--partition=Lewis,BioCompute,hpc5,General --mem=16G" \
--jobs 30 --latency-wait 180 --configfile lewis_slurm_inputs.json \
--snakefile snakyVC.smk

Chan YO, Dietz N, Zeng S, Wang J, Flint-Garcia S, Salazar-Vidal MN, Škrabišová M, Bilyeu K, Joshi T: The Allele Catalog Tool: a web-based interactive tool for allele discovery and analysis. BMC Genomics 2023, 24(1):107.

1. The GATK:CombineGVCFs and GATK:GenotypeGVCFs can be either parallelly executed based on number of chromosomes or not parallelly executed at all. If users are combining a large number of accessions into one to perform calling, these two processes will take a lot of time.
2. The execution time of the SnakyVC pipeline mainly depends on the size of the data and the available computing resources on the machine.