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Installation

Installation#

We support different ways to install dbcan, including:

Note

If you prefer not installing dbcan locally, you can also use it via our online server.

Requirements#

  • A Posix-compliant operating system, e.g. Linux or MacOS.

  • A Python 3.6 or later environment (you can use conda to create it).

  • When using Conda or PyPI to install dbcan, you also need to prepare the databases used by dbcan seperately (See Database Installation Command).

Installing with Conda#

If you haven’t already installed conda, you need to install a conda environment. Conda is available through the Anaconda or Miniconda. Then, you can create a new conda environment (optional but recommended) using the command:

conda create --name dbcan python=3.8

If you already have a conda environment, you can skip the step above.

To install the dbcan package, use the conda install command:

conda install dbcan -c conda-forge -c bioconda

Build database#

You can build database via this command:

dbcan_build --cpus 8 --db-dir db --clean

  1. --cpu indicates count of cpu you can use. Try as many as possible for fast building.

  2. --db-dir indicates database folder path. Default is db on your current database

3. --clean indicates clean the folder indicated by --db-dir. You can remove this parameter if you don’t want to clean, but we recommend you add this to keep away from index contamination.

Installing with PyPI#

To install the dbcan package via pip, you first need to install a few executable dependencies:

Warning

SignalP Integration Notice

Due to the specific licensing terms of SignalP, it is not included directly as a dependency in our package. This requires users to undertake a separate installation process.

Installing SignalP (Optional):

  • SignalP is optional and not essential for the core functionality of our software. Users requiring its specific features can integrate it as follows:

    1. Visit the SignalP website.

    2. Submit a download request.

    3. Post-download, add SignalP to your system’s environmental variables to make it executable.

  • For installation assistance, refer to the SignalP Peptide Prediction Integration.

This approach ensures compliance with SignalP’s licensing while offering the tool’s functionality to those who need it.

After the dependencies are installed, dbcan can be installed via PyPI:

pip install dbcan

Note

Since PyPI doesn’t have an independent build system, the dependencies of dbcan need to be installed seperatedly. Therefore, we recommended users to install dbcan via Conda which can resolve all dependencies automatically.

Installing with Docker#

To use dbcan via Docker, please follow these steps:

  1. Install Docker on your system (e.g. Linux, MacOS);

  2. Pull the image haidyi/run_dbcan from Docker Hub;

  3. Run the run_dbcan tool via Docker:

    docker run -it haidyi/run_dbcan:latest <input_file> [args] --out_dir <output_dir>

    Note

    To use your own local files as input when using Docker, make sure the local files are mounted and visible to your container.

Check Installation#

After installation, you can check if dbcan is successfully installed by running:

run_dbcan -h

If it shows all the help information, congratulations! You are ready to annotate your own proteins right now.

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