CVUA-RRW/FooDMe
A reproducible and scalable snakemake workflow for the analysis of DNA metabarcoding experiments, with a special focus on food and feed samples.
Overview
Topics: ngs ngs-pipeline metabarcoding targeted-sequencing snakemake food-monitoring public-health food-authenticity workflow pipeline bioinformatics
Latest release: 1.7.2, Last update: 2024-09-19
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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/CVUA-RRW/FooDMe . --tag 1.7.2
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.
To configure the workflow, modifiy config/config.yaml according
to your needs.
For specific details about the parameters check the documentation.
A configuration file with optimized parameters (with no warranty!) for the analysis of 16S metabarcoding experiments of birds and mammals is included in this folder. You will still need to modify the indicated paths to match your own folder structure.
See for reference:
Denay, G.; Preckel, L.; Petersen, H.; Pietsch, K.; Wöhlke, A.; Brünen-Nieweler, C. Benchmarking and Validation of a Bioinformatics Workflow for Meat Species Identification Using 16S rDNA Metabarcoding. Foods 2023, 12, 968. https://doi.org/10.3390/foods12050968
Add your samples to config/samples.tsv or use the
the helper script from Bundes Insitut für Risikobewertung in
ressources/creat_sampleSheet.sh.
If using the benchmark mode, sdd information for your reference samples in the reference.tsv template.
Note that proportions are given as fractions in the [0, 1] interval.
To configure a parameter space exploration analysis, modify the file
config_paramspace.yaml according to your needs.
For specific details about the parameters check the documentation.
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