MeLanGE - Documentation

Snakemake python


MeLanGE is a automated pipeline for the genomic annotation of a group of genomes, followed by the detection of the most important features to distinguish a group of genomes, as determined by a metadata label.

MeLanGE has two independent, but connected, components:

  • Genome annotation
  • Feature selection

MeLanGE is implemented in a Snakemake workflow, thus contributing to reproducible and scalable data analysis.

Quick setup

Step 0: Install conda, snakemake and ensure git

Conda and Snakemake are required to be able to use MeLanGE.
Most people would probably want to install Miniconda.
After having conda installed, install Snakemake:

# As described in Snakemake documentation:
conda install -c conda-forge mamba
mamba create -c conda-forge -c bioconda -n snakemake snakemake
conda activate snakemake

Step 1: Clone workflow

To use MeLanGE, you need a local copy of the workflow repository. Start by making a clone of the repository:

git clone

Step 2: Configure workflow

Configure the workflow according to your needs by editing the file config.yaml.

Step 3: Execute workflow

Test your configuration by performing a dry-run via

snakemake --use-conda -n

Execute the workflow locally via

snakemake --use-conda --cores N

This will run the workflow locally using N cores.


Examine workflow:

snakemake --dag  | dot -Tsvg > dag.svg

Investigate results:

After successful execution, you can create a self-contained interactive HTML report with all results via:

snakemake --report report.html

Future implementations

  • Improve report output.


For now MeLanGE does not have a publication describing its functionalities (we are working on it). Please use a link to MeLanGE github when you reference this tool.