DROP is available on bioconda . In case the conda channel priority is set to strict, it should be reset to flexible:

conda config --set channel_priority true

We recommend using a dedicated conda environment (here: drop_env) for installing drop. For installing, use mamba instead of conda as it provides more reliable and faster dependency solving.

mamba create -n drop_env -c conda-forge -c bioconda drop --override-channels

In the case of mamba/conda troubles we recommend using the fixed DROP_<version>.yaml installation file we make available on our public server. Install the current version and use the full path in the following command to install the conda environment drop_env

mamba env create -f DROP_1.2.4.yaml

Installation time: ~ 10min

Test whether the pipeline runs through by setting up the demo dataset in an empty directory (e.g. ~/drop_demo).

conda activate drop_env
mkdir ~/drop_demo
cd ~/drop_demo

# demo will download the necessary data and pipeline files
drop demo

The pipeline can be run using snakemake commands Run time: ~25min

snakemake --cores 1 -n # dryrun
snakemake --cores 1

Initialize a project

The demo project can be modified to be used for a new project. Alternatively, a new DROP project can be set up using drop init.

cd <path/to/project>
drop init

This will create an empty config.yaml file that needs to be filled according to the project data. You also need to prepare a sample annotation file. Go to Preparing the Input Data for more details.

Other DROP versions

The developer version of DROP can be found in the repository under the branch dev. Make sure that the Prerequisites are installed, preferably in a conda environment. Then install DROP from github using pip.

pip install git+

Alternatively, you can clone the desired branch of the repository and install from directory.

git clone -b dev
pip install ./drop

If the package needs to be updated frequently, it is more useful to use the -e` option of ``pip. Any new update pulled from the repository will be available without reinstall. Note, that this requires an explicit call to update any existing project (Updating DROP).

pip install -e ./drop

# update project directory
cd <path/to/project>
drop update


The easiest way to ensure that all dependencies are installed is to install the bioconda package, as described above. Once the environment is set up and installation was successful, other versions of drop can be installed with pip, overwriting the conda version of DROP (see Other DROP versions).

Installation without conda

Alternatively, DROP can be installed without conda. In this case the following dependencies must be met:


If you are using an already existing R installation, make sure that the R and bioconductor versions match. Otherwise, use the newest versions of R and bioconductor.

At first invocation, all necessary R packages will be installed with the first pipeline call. As this is a lengthy process, it might be desirable to install them in advance, if a local copy of the repository exists.

# optional
Rscript <path/to/drop/repo>/drop/installRPackages.R drop/requirementsR.txt