CoolerCodonOpt

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Codon optimization using the dnachisel library

Installation with conda (recommended)

Conda is an open source package and environment management system that runs on Windows, macOS and Linux. To know more about Conda take a look at the workshop Introduction to Conda for (Data) Scientists or the YouTube workshop prepared by the KAUST Visualization Core Lab.

1. Install Miniconda in your system:

   For MacOS:

   • Download the installer in your home directory: https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh

   • Open your terminal, go to your home directory (or wherever you downloaded the file) and run the installation script:

     bash Miniconda3-latest-MacOSX-x86_64.sh
     

   • The script will present several prompts that allow you to customize the Miniconda install. It is recommended to accept the default settings. However, when prompted with the following:

     Do you wish the installer to initialize Miniconda3
     by running conda init?
     

     Type yes to avoid to manually initialize Conda later. If you accidentally type no, when the script finishes you simply need to type the following command:

     conda init bash
     

   • Close and open the terminal again. You will see either (miniconda3) or (base) before the terminal prompt, which means conda was installed correctly and the base environment is activated.

2. Open your terminal and go to the directory where you would like to save the project. Then run the following command:

   git clone https://github.com/strubelab/CoolerCodonOpt.git

   • The directory CoolerCodonOpt/ is created.

3. Create a virtual environment to work on:

   • Go to the CoolerCodonOpt directory

     cd CoolerCodonOpt

   • Create a virtual environment

     conda env create --prefix ./env --file environment.yml

   • Activate the virtual environment

     conda activate ./env

4. Install the CoolerCodonOpt package in your virtual environment:

   pip install -e .
   

Installation with venv and pip

1. Verify that you have python 3 installed in your computer:

   • Open your terminal and type python --version
   • If the version number is less than 3, for example Python 2.7.10, download the current python version from https://www.python.org/ or alternatively follow the installation instructions in https://realpython.com/installing-python/

2. Open your terminal and go to the directory where you would like to save the project. Then run the following command:

   git clone https://github.com/strubelab/CoolerCodonOpt.git

   • The directory CoolerCodonOpt/ is created.

3. Create a virtual environment to work on:

   • Go to the CoolerCodonOpt directory

     cd CoolerCodonOpt

   • Create a virtual environment

     python3 -m venv venv

   • Activate the virtual environment

     source venv/bin/activate

4. Install requirements and the CoolerCodonOpt package

   pip install -r requirements.txt
   pip install -e .
   

5. When you finish working, you can deactivate the virtual environment

   deactivate

Usage

1. Now you can call the optimize script from any location:

optimize --help

usage: optimize [-h] [--species SPECIES] [-v] [-d DESTINATION] input

Takes a DNA sequence and optimizes it for expression in E. coli

positional arguments:
  input                 Fasta file with the sequence(s) to be optimized, or a directory with fasta
                        files.

optional arguments:
  -h, --help            show this help message and exit
  --species SPECIES     Species for which the sequence will be codon-optimized. Can be either a TaxID
                        (requires internet connection) or the name of the species from the available
                        choices: b_subtilis, c_elegans, d_melanogaster, e_coli, g_gallus, h_sapiens,
                        m_musculus, m_musculus_domesticus, s_cerevisiae
  -v, --verbose         Show the constraints evaluations, and optimization objectives score.
  -d DESTINATION, --destination DESTINATION
                        Path for saving the resulting sequences. It defaults to the same directory as
                        the input.

A. Run providing a fasta file with one or more sequences

The following command will optimize the given sequence, output the optimized sequence and optimization score to the terminal and save the optimized sequence to a file in fasta format:

python optimize.py sequence.fasta --verbose

B. Provide a directory name with fasta sequence files

The following command will take all the sequences in the sequences/ directory, and save them in the optimized_sequences/ directory.

python optimize.py sequences/ --destination optimized_sequences/