orthanq/orthanq-hla-quantification

A Snakemake workflow for typing and quantifying HLAs using Orthanq.

Overview

Topics:

Latest release: v1.0.2, Last update: 2025-03-07

Linting: linting: failed, 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/orthanq/orthanq-hla-quantification . --tag v1.0.2

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.

General settings

To configure this workflow, modify config/config.yaml according to your needs, following the explanations provided in the file.

Unit sheet

Add samples to config/units.tsv.

  • Each unit has a unit_name. This can be a running number, or an actual run, lane or replicate id (for now this is not functional as we want to make this workflow to comply with the dna-seq-varlociraptor-workflow because we will import this wokrflow as a module there. For that reason, it's not called samples.tsv)
  • Each unit has a sample_name, which associates it with the biological sample it comes from.
  • For each unit, you need to specify:
    • fq1 and fq2 for paired end reads. These can point to any FASTQ files on your system.

Linting and formatting

Linting results

Workflow defines that rule get_genome is eligible for caching between workflows (use the --cache argument to enable this).
Workflow defines that rule genome_faidx is eligible for caching between workflows (use the --cache argument to enable this).
Workflow defines that rule bwa_index is eligible for caching between workflows (use the --cache argument to enable this).
Lints for rule get_hla_genes_and_xml (line 27, /tmp/tmp6v4e0iwb/orthanq-orthanq-hla-quantification-1f17ab5/workflow/rules/preparation.smk):
    * Specify a conda environment or container for each rule.:
      This way, the used software for each specific step is documented, and the
      workflow can be executed on any machine without prerequisites.
      Also see:
      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers

Lints for rule unzip_xml (line 40, /tmp/tmp6v4e0iwb/orthanq-orthanq-hla-quantification-1f17ab5/workflow/rules/preparation.smk):
    * Specify a conda environment or container for each rule.:
      This way, the used software for each specific step is documented, and the
      workflow can be executed on any machine without prerequisites.
      Also see:
      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers

Lints for rule get_pangenome (line 62, /tmp/tmp6v4e0iwb/orthanq-orthanq-hla-quantification-1f17ab5/workflow/rules/preparation.smk):

... (truncated)

Formatting results

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[DEBUG] 
[DEBUG] In file "/tmp/tmp6v4e0iwb/orthanq-orthanq-hla-quantification-1f17ab5/workflow/rules/preparation.smk":  Formatted content is different from original
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[DEBUG] In file "/tmp/tmp6v4e0iwb/orthanq-orthanq-hla-quantification-1f17ab5/workflow/rules/orthanq.smk":  Formatted content is different from original
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[DEBUG] In file "/tmp/tmp6v4e0iwb/orthanq-orthanq-hla-quantification-1f17ab5/workflow/rules/common.smk":  Formatted content is different from original
[INFO] 4 file(s) would be changed 😬

snakefmt version: 0.10.2