3d-omics/hg_genotype

None

Overview

Latest release: v1.3.0, Last update: 2025-11-07

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=3d-omics/hg_genotype

Quality control: linting: failed formatting: passed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --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

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

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/3d-omics/hg_genotype . --tag v1.3.0

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

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.

Describe how to configure the workflow (using config.yaml and maybe additional files). All of them need to be present with example entries inside of the config folder.

Linting and formatting

Linting results

 1Workflow defines that rule reference__recompress__genome is eligible for caching between workflows (use the --cache argument to enable this).
 2Workflow defines that rule reference__recompress__vcf is eligible for caching between workflows (use the --cache argument to enable this).
 3Workflow defines that rule reference__recompress__gff is eligible for caching between workflows (use the --cache argument to enable this).
 4Workflow defines that rule align__bwamem2__index is eligible for caching between workflows (use the --cache argument to enable this).
 5Lints for rule annotate__vep__download_plugins (line 16, /tmp/tmpmtrcdh46/3d-omics-hg_genotype-20b2f9d/workflow/rules/annotate/vep.smk):
 6    * No log directive defined:
 7      Without a log directive, all output will be printed to the terminal. In
 8      distributed environments, this means that errors are harder to discover.
 9      In local environments, output of concurrent jobs will be mixed and become
10      unreadable.
11      Also see:
12      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files

Formatting results

All tests passed!