This installation guide describes the installation of VPMBench for Linux. We currently do not support Windows.

Getting the Source

You can download the current version of the VPMBench source from GitHub:

$ git clone


Python Version

We recommend using the latest version of Python 3. VPMBench supports Python 3.6 or newer.

You can check your Python version by running the following command in your terminal:

$ python --version
Python 3.9.1

These Python libraries will be installed automatically when installing VPMBench:

  • Pandas implements a tabular data-structure.

  • Pandera provides schema-based validations for Pandas

  • PyYaml implements a YAML-Parser

  • Docker-SDK lets you do anything docker command does

  • Scikit-learn implements a bunch of machine-learning algorithms

  • Numpy provides a large collection of mathematical functions

  • PyVCF implement a parser for VCF-files


VPMBench requires Docker to run the variant prioritization methods. Therefore, you have to ensure that you have the proper rights to run Docker commands as the current user. You can easily check this by running:

$ docker run hello-world

Hello from Docker!
This message shows that your installation appears to be working correctly.

To generate this message, Docker took the following steps:
 1. The Docker client contacted the Docker daemon.
 2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
 3. The Docker daemon created a new container from that image which runs the executable that produces the output you are currently reading.
 4. The Docker daemon streamed that output to the Docker client, which sent it to your terminal.

To try something more ambitious, you can run an Ubuntu container with:
 $ docker run -it ubuntu bash

Share images, automate workflows, and more with a free Docker ID:

For more examples and ideas, visit:

If an error occurs check the Docker Documentation and try again.

Automatic Installation

In the repository, we provide an installation script Using the installation script, you have to answer questions, e.g., for the plugin directory. Currently, we support the automatic installation of CADD and fathmm-MKL. Please make sure that Docker is installed and that enough disk space is available to install the plugin. If you want to install fathmm-MKL, tabix needs to be installed on your machine:

$ sudo apt install tabix
$ tabix --version
tabix (htslib) 1.10.2-3
Copyright (C) 2019 Genome Research Ltd.

After answering the questions, you will see an overview of the selected files before the download and installation starts.

$ chmod +x
$ ./
Guided Installation for VPMBench-v0.1

The following questions will guide you through selecting the files and dependencies needed for VPMBench.
After this, you will see an overview before the download and installation starts.

Where do you want to store the plugins? [~/VPMBench-Plugins]
Do you want to test if Docker works? (y)/n
> Assuming YES.

Do you want to copy the provided plugin to /home/andreas/VPMBench-Plugins? (y)/n
> Assuming YES.

Do you want to install the provided plugins (Warning: Might take a while)? (y)/n
> Assuming YES.

Do you want to install CADD (~ 200GB, Warning: Sometimes the installation seems to fail for no obvious reasons)? (y)/n
> Assuming YES.

Do you want to fathmm-MKL (~80GB)? (y)/n
> Assuming YES.

Do you want to do a test run after the installation? (y)/n
> Assuming YES.

* Plugin Path: /home/andreas/VPMBench-Plugins
* Test Docker: true
* Copy provided plugins: true
* Install provided plugins: true
  - Install CADD: true
  - Install fathmm-MKL: true
  - Test run: true
Please make sure you have enough disk space available to install the plugins.
Please make sure you have the rights to run docker and install python packages!

Ready to continue? (y)/n

The complete installation with the two provided plugins for CADD and fathmm-MKL might take 2-3h and uses about 300GB of your disk space. So it’s enough time to drink a coffee or two.

Manual Installation

While we recommend using the automatic installation procedure, you can also install VPMBench following the following steps.

Step 1 - Create a plugin directory

By default VPMBench expects the plugins to be installed in the VPMBench directory in the home directory of the current user. You can create the directory via:

$ mkdir ~/VPMBench-Plugins

Step 2 - Install VPMBench

To install VPMBench, simply run this simple command in your terminal after entering VPMBench directory:

$ cd VPMBench
$ pip install .

We recommend installing VPMBench in its own virtual environment to prevent any conflicts with already installed Python libraries.

After the installation you should be able to run the following command without errors:

$ python -c "import vpmbench"

Congratulations, you now can use VPMBench in your projects. You now might have a look at the Quickstart Guide or the API Documentation.

Step 3 - Copy and install plugins (optional)

The currently provided plugins can be found in the /plugin directory for the repository. To use these plugins you have to copy them to your plugin directory from Step 1. The following copies all plugins to the default plugin directory:

$ cp plugins/* ~/VPMBench-Plugins

After this, the plugins have to be installed. Therefore, each plugin directory contains its installation script which builds the Docker image and downloads the required files. After this, the plugins are ready to be used in VPMBench.

Step 4 - Test the installed plugins (optional)

To test the installed plugins you can run the script bin/ Therefore, you have to provide a VCF-file as input and specify your plugin-path.

$ python /bin/ tests/resources/test_grch37.vcf ~/VPMBench-Plugins

During the execution of the script logging information is written to your terminal. The output should look like this:

$ python /bin/ tests/resources/test_grch37.vcf ~/VPMBench-Plugins
#### Run pipeline
- Starting time: 10/03/2021 13:15:49
#### Extract data from tests/resources/test_grch37.vcf
- Used extractor: <class 'vpmbench.extractor.ClinVarVCFExtractor'>!
- Extracted Data:
0    0     1    865568   G   A  hg19  snp
1    1     1    949738   C   T  hg19  snp
2    2     1    949739   G   A  hg19  snp
3    3     1    955597   G   T  hg19  snp
4    4     1    955601   C   T  hg19  snp
5    5     1  20416314   G   T  hg19  snp
6    6     1  20978410   T   C  hg19  snp
7    7     1  20978956   G   A  hg19  snp
8    8     1  20978971   C   T  hg19  snp
#### Load plugins from ../VPMBench-Plugins
- Absolute plugin path: /home/arusch/extern/VPMBench-Plugins
- Found 3 plugins: ['fathmm-MKL (coding)', 'fathmm-MKL (non-coding)', 'CADD']
- Returning 3 filtered plugins: ['fathmm-MKL (coding)', 'fathmm-MKL (non-coding)', 'CADD']
#### Invoke methods
- #CPUs: 11
- Invoke method: CADD
- Invoke method: fathmm-MKL (coding)
- Invoke method: fathmm-MKL (non-coding)
- Finish method: fathmm-MKL (non-coding)
- Finish method: fathmm-MKL (coding)
- Finish method: CADD
#### Calculate reports
- Calculate Specificity
- Calculate Sensitivity
#### Stop pipeline
- Finishing time: 10/03/2021 13:16:07
- fathmm-MKL (coding): 0.75
- fathmm-MKL (non-coding): 0.5
- CADD: 0.75
- fathmm-MKL (coding): 0.0
- fathmm-MKL (non-coding): 0.6
- CADD: 0.0