brunosate21-a11y/Metabolic-Modeling-Workflow.

None

Overview

Latest release: None, Last update: 2026-06-14

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=brunosate21-a11y/Metabolic-Modeling-Workflow.

Quality control: linting: failed formatting: failed

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/brunosate21-a11y/Metabolic-Modeling-Workflow. . --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 download_checkm_db (line 6, /tmp/tmpfozs0f7s/workflow/rules/checkm.smk):
 2    * Specify a conda environment or container for each rule.:
 3      This way, the used software for each specific step is documented, and the
 4      workflow can be executed on any machine without prerequisites.
 5      Also see:
 6      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
 7      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
 8    * Param dest is a prefix of input or output file but hardcoded:
 9      If this is meant to represent a file path prefix, it will fail when
10      running workflow in environments without a shared filesystem. Instead,
11      provide a function that infers the appropriate prefix from the input or
12      output file, e.g.: lambda w, input: os.path.splitext(input[0])[0]
13      Also see:
14      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules
15      https://snakemake.readthedocs.io/en/stable/tutorial/advanced.html#tutorial-input-functions
16
17Lints for rule filter_checkm (line 40, /tmp/tmpfozs0f7s/workflow/rules/checkm.smk):
18    * Specify a conda environment or container for each rule.:
19      This way, the used software for each specific step is documented, and the
20      workflow can be executed on any machine without prerequisites.
21      Also see:
22      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
23      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
24
25Lints for rule memote_summary (line 17, /tmp/tmpfozs0f7s/workflow/rules/memote.smk):
26    * Specify a conda environment or container for each rule.:
27      This way, the used software for each specific step is documented, and the
28      workflow can be executed on any machine without prerequisites.
29      Also see:
30      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
31      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
32
33Lints for rule filter_memote (line 28, /tmp/tmpfozs0f7s/workflow/rules/memote.smk):
34    * Specify a conda environment or container for each rule.:
35      This way, the used software for each specific step is documented, and the
36      workflow can be executed on any machine without prerequisites.
37      Also see:
38      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
39      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
Formatting results
 1[DEBUG] 
 2[DEBUG] In file "/tmp/tmpfozs0f7s/workflow/rules/memote.smk":  Formatted content is different from original
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 4[DEBUG] In file "/tmp/tmpfozs0f7s/workflow/Snakefile":  Formatted content is different from original
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 8[DEBUG] In file "/tmp/tmpfozs0f7s/workflow/rules/sensitivity.smk":  Formatted content is different from original
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10[DEBUG] In file "/tmp/tmpfozs0f7s/workflow/rules/smetana.smk":  Formatted content is different from original
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12[DEBUG] In file "/tmp/tmpfozs0f7s/workflow/rules/micom.smk":  Formatted content is different from original
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14[DEBUG] In file "/tmp/tmpfozs0f7s/workflow/rules/checkm.smk":  Formatted content is different from original
15[DEBUG] 
16[DEBUG] In file "/tmp/tmpfozs0f7s/workflow/rules/comparison.smk":  Formatted content is different from original
17[DEBUG] 
18[DEBUG] In file "/tmp/tmpfozs0f7s/workflow/rules/carveme.smk":  Formatted content is different from original
19[INFO] 9 file(s) would be changed 😬
20
21snakefmt version: 0.11.5