Ctrl+K

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 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.

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.

Contents