GaspardR/snakemake-github-module-keyerror

None

Overview

Latest release: None, Last update: 2026-04-10

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=GaspardR/snakemake-github-module-keyerror

Quality control: linting: failed formatting: failed

Workflow Rule Graph

This visualization of the workflow’s rule graph was automatically generated using Snakevision

Rule Graph light

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/GaspardR/snakemake-github-module-keyerror . --tag None

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 using apptainer/singularity, use

snakemake --cores all --sdm apptainer

To run the workflow using a combination of conda and apptainer/singularity for software deployment, use

snakemake --cores all --sdm conda apptainer

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.

Workflow overview

This workflow is a best-practice workflow for <detailed description>. The workflow is built using snakemake and consists of the following steps:

  1. Download genome reference from NCBI

  2. Validate downloaded genome (python script)

  3. Simulate short read sequencing data on the fly (dwgsim)

  4. Check quality of input read data (FastQC)

  5. Collect statistics from tool output (MultiQC)

Running the workflow

Input data

This template workflow creates artificial sequencing data in *.fastq.gz format. It does not contain actual input data. The simulated input files are nevertheless created based on a mandatory table linked in the config.yaml file (default: .test/samples.tsv). The sample sheet has the following layout:

sample

condition

replicate

read1

read2

sample1

wild_type

1

sample1.bwa.read1.fastq.gz

sample1.bwa.read2.fastq.gz

sample2

wild_type

2

sample2.bwa.read1.fastq.gz

sample2.bwa.read2.fastq.gz

Linting and formatting

Linting results
 1Lints for rule A (line 12, /tmp/tmpmmniwpev/workflow/Snakefile):
 2    * Do not access input and output files individually by index in shell commands:
 3      When individual access to input or output files is needed (i.e., just
 4      writing '{input}' is impossible), use names ('{input.somename}') instead
 5      of index based access.
 6      Also see:
 7      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#rules
 8    * No log directive defined:
 9      Without a log directive, all output will be printed to the terminal. In
10      distributed environments, this means that errors are harder to discover.
11      In local environments, output of concurrent jobs will be mixed and become
12      unreadable.
13      Also see:
14      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
15    * Specify a conda environment or container for each rule.:
16      This way, the used software for each specific step is documented, and the
17      workflow can be executed on any machine without prerequisites.
18      Also see:
19      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
20      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
21
22Lints for rule B (line 39, /tmp/tmpmmniwpev/workflow/Snakefile):
23    * Do not access input and output files individually by index in shell commands:
24      When individual access to input or output files is needed (i.e., just
25      writing '{input}' is impossible), use names ('{input.somename}') instead
26      of index based access.
27      Also see:
28      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#rules
29    * No log directive defined:
30      Without a log directive, all output will be printed to the terminal. In
31      distributed environments, this means that errors are harder to discover.
32      In local environments, output of concurrent jobs will be mixed and become
33      unreadable.
34      Also see:
35      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
36    * Specify a conda environment or container for each rule.:
37      This way, the used software for each specific step is documented, and the
38      workflow can be executed on any machine without prerequisites.
39      Also see:
40      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
41      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
42
43Lints for rule C (line 24, /tmp/tmpmmniwpev/workflow/Snakefile):
44    * No log directive defined:
45      Without a log directive, all output will be printed to the terminal. In
46      distributed environments, this means that errors are harder to discover.
47      In local environments, output of concurrent jobs will be mixed and become
48      unreadable.
49      Also see:
50      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
51    * Specify a conda environment or container for each rule.:
52      This way, the used software for each specific step is documented, and the
53      workflow can be executed on any machine without prerequisites.
54      Also see:
55      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
56      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
Formatting results
1[DEBUG] 
2[DEBUG] In file "/tmp/tmpmmniwpev/workflow/Snakefile":  Formatted content is different from original
3[INFO] 1 file(s) would be changed 😬
4
5snakefmt version: 0.11.5