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Nanopype can be installed without root privileges as it's maintaining most of its dependencies by building required tools from source in a user accessible path. Currently the following list of system wide packages is required to build all included software packages (starting with ubuntu 18.04 LTS, names on MacOS might be different):

  • git gcc g++ wget rsync
  • binutils autoconf make cmake
  • libgomp1
  • zlib1g-dev
  • bzip2 libbz2-dev
  • liblzma-dev libncurses5-dev
  • libcunit1 libhdf5-100 libidn11 libopenblas-base
  • libgssapi-krb5-2
  • libzstd-dev

These packages are likely present in most production environments. Please also refer to the Dockerfiles in the singularity folder of the pipeline repository. If you need only a subset of the provided tools the number of dependencies might decrease.

The following build process is the most flexible and customizable installation and might despite careful tests lead to errors on your specific system. Please do not hesitate to contact us and ask for help. Also check and open an issue where needed on github.

Start with creating a virtual python environment:

python3 -m venv /path/to/your/environment
cd /path/to/your/environment
source bin/activate

Or using conda:

conda create -n nanopype python=3.6 anaconda
source activate nanopype


Install Nanopype from If you use conda you find pip in the bin/ folder of the conda installation. If you use a conda virtual environment use the pip from the envs/nanopype/bin.

git clone
cd nanopype
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt
cd ..

It is recommended to install a tagged version of Nanopype. Using the 'latest' from master will always pull the most recent Singularity images. If the remaining pipeline is then not regularly updated via git pull, pipeline code and container code can diverge. To install a specific version modify the above commands to:

git clone
cd nanopype
git fetch --tags && git checkout v1.0.0
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt
cd ..


The installation will create the following folder structure relative to a given [INSTALL_PREFIX] directory:


Nanopype integrates a variety of different tools merged into processing pipelines for common use cases. We provide Snakemake rules to download and build all dependencies. From within the Nanopype repository run

snakemake --snakefile rules/install.smk --directory /path/to/INSTALL_PREFIX all

to build and install all tools into src, bin and lib folders of the INSTALL_PREFIX directory. To only build a subset or specific targets e.g. samtools you can use (The file rules/install.smk lists all available groups):

# core functionality of basecalling and alignment
snakemake --snakefile rules/install.smk --directory [INSTALL_PREFIX] processing
# extended methylation detection functionality
snakemake --snakefile rules/install.smk --directory [INSTALL_PREFIX] methylation
# specific tool only
snakemake --snakefile rules/install.smk --directory [INSTALL_PREFIX] samtools

The --directory argument of Snakemake is used as installation prefix. By running Snakemake with e.g. -j 4 multiple targets are build in parallel at the cost of interleaved output to the shell. To further accelerate the build you may try the following to build 8 tools (-j 8) with 8 threads each (threads_build=8) using the CMake generator Ninja:

snakemake --snakefile rules/install.smk --directory [INSTALL_PREFIX] all -j 8 --config threads_build=8 build_generator=Ninja

In case your bin directory is not listed in the PATH variable (echo $PATH), execute

python3 scripts/ /path/to/INSTALL_PREFIX

to make Nanopype aware of the installed tools. This will create a .pth file in your python3 installation, modifying some environment variables temporarily on python startup. Alternatively you can run the following line once or append it to your ~/.bashrc. Note that in cluster environments this is not necessarily changing the PATH on all nodes!

export PATH=/path/to/INSTALL_PREFIX/bin:$PATH

To deactivate a virtual python environment after installation or usage of the pipeline just type:


for plain and

source deactivate

for conda virtual environments.

Mission accomplished! Everything else is solved at run time by Snakemake and Nanopype.


Do not forget to follow the global configuration once and for each sample the local one.


There are some common errors that could arise during the installation process. If you encounter one of the following error messages, please consider the respective solution attempts.

not a supported wheel on this platform
Nanopype requires at least python3.6. If you install additional packages (e.g. albacore) from python wheels, make sure the downloaded binary packages matches the local python version.
terminated by signal 4
Nanopype is mostly compiling integrated tools from source. In heterogeneous cluster environments this can lead to errors if the compilation takes place on a machine supporting modern vector instructions (SSE, AVX, etc.) but execution also uses less recent computers. The error message terminated by signal 4 indicates an instruction in the software not supported by the underlying hardware. Please re-compile and install the tools from a machine with a common subset of vector instructions in this case.
The TensorFlow library was compiled to use AVX instructions, but these aren't available on your machine
This error can occur while running tools which are using tensorflow in the backend (e.g. Deepbinner). The PyPl version of tensorflow installed with Nanopype is pre-compiled to use AVX (Advanced Vector Extensions). Either run the pipeline for these workflows on a different node with AVX (Intel since Haswell, AMD since Excavator) or build tensorflow from source on a computer without AVX. Install tensorflow afterwards into the same python as Nanopype.