Metadata-Version: 1.1
Name: mackinac
Version: 0.8.1
Summary: Mackinac: A bridge between ModelSEED and COBRApy
Home-page: https://github.com/mmundy42/mackinac
Author: Michael Mundy, Helena Mendes-Soares, Nicholas Chia
Author-email: mundy.michael@mayo.edu
License: BSD
Download-URL: https://pypi.python.org/pypi/mackinac
Description: Mackinac: A bridge between ModelSEED and COBRApy
        ================================================
        
        Mackinac combines the ability of `ModelSEED <http://modelseed.org>`_ to automatically
        reconstruct metabolic models with the advanced analysis capabilities in
        `cobrapy <https://github.com/opencobra/cobrapy>`_ to bridge the differences between
        the two frameworks. Mackinac provides support for using the ModelSEED
        web service to create draft genome-scale models from genomes available in the
        `Pathosystems Resource Integration Center <https://www.patricbrc.org/portal/portal/patric/Home>`_
        (PATRIC) and creates a COBRA model from a ModelSEED model. If you are not a
        `registered PATRIC user <http://enews.patricbrc.org/faqs/workspace-faqs/registration-faqs/>`_,
        you must complete a `new user registration <https://user.patricbrc.org/register/>`_
        to work with the ModelSEED web service.
         
        If you already have models available in ModelSEED, you can simply import and
        create a COBRA model with the ``create_cobra_model_from_modelseed_model()``
        function. You can then use all of the features in cobrapy to analyze,
        inspect, explore, and draw conclusions from the model.
        
        You can also reconstruct and gap fill models using the ModelSEED
        service for any organism with a genome available in PATRIC. Additional functions
        are available for working with ModelSEED models, managing workspace objects,
        getting information about PATRIC genomes, and calculating reaction likelihoods.
        
        Please use the `cobrapy Google
        Group <http://groups.google.com/group/cobra-pie>`_ for help.
        Alternatively, you can use
        `gitter.im <https://gitter.im/opencobra/cobrapy>`_ for quick questions
        and discussions (faster response times).
        
        More information about opencobra is available at the
        `website <http://opencobra.github.io/>`_.
        
        Installation
        ------------
        
        Use pip to install Mackinac from
        `PyPI <https://pypi.python.org/pypi/mackinac>`_ (we recommend doing this
        inside a `virtual environment
        <http://docs.python-guide.org/en/latest/dev/virtualenvs/>`_)::
        
            pip install mackinac
        
        Mackinac requires the cobrapy, requests, configparser, and six packages.
        
        Web service URLs
        ----------------
        
        Mackinac uses web services provided by other organizations which can be offline, the interface can
        change, or the URL can change. Mackinac uses these default URLs:
        
        * ModelSEED web service at https://p3.theseed.org/services/ProbModelSEED
        * Workspace web service at https://p3.theseed.org/services/Workspace
        * PATRIC web service at https://www.patricbrc.org/api/
        
        Alternate URLs include the following:
        
        * ModelSEED web service at http://p3c.theseed.org/dev1/services/ProbModelSEED
        
        You can change the URL used to connect to a web service as shown below:
        
            >>> import mackinac
            >>> mackinac.modelseed.ms_client.url = 'https://p3.theseed.org/services/ProbModelSEED'
            >>> mackinac.workspace.ws_client.url = 'https://p3.theseed.org/services/Workspace'
            >>> mackinac.genome.patric_url = 'https://www.patricbrc.org/api/'
        
        Direct installation in virtual environment
        ------------------------------------------
        
        1. If virtualenvwrapper is not installed, `follow the directions <https://virtualenvwrapper.readthedocs.io/en/latest/>`__
           to install virtualenvwrapper.
        
        2. Clone the `git repository <https://github.com/mmundy42/mackinac>`_ to your computer.
        
            $ git clone https://github.com/mmundy42/mackinac.git
        
        3. Create a virtualenv for Mackinac with these commands::
        
            $ cd mackinac
            $ mkvirtualenv --python python2 mackinac-py27
        
           Use the ``--python`` option to select a specific version of Python for the virtualenv. For example,
           ``python=python3`` to select the current python3 installed on the system.
        
           Note on macOS, matplotlib requires Python be installed as a framework but virtualenv creates a
           non-framework build of Python. See the `matplotlib FAQ <http://matplotlib.org/1.5.3/faq/virtualenv_faq.html>`__
           for details on a workaround.
        
        4. Upgrade pip and setuptools to the latest versions with these commands::
        
            (mackinac-py27)$ pip install --upgrade pip setuptools
        
        5. Install all of the Mackinac dependencies with this command::
        
            (mackinac-py27) pip install -r requirements.txt
        
           This command can take a few minutes while numpy, pandas, and libsbml are built in the virtualenv.
        
        6. Install the latest version of Mackinac with this command::
        
            (mackinac-py27)$ python setup.py install
        
        7. Install the pytest package with this command::
        
            (mackinac-py27)$ pip install pytest
        
        8. You need to provide a username and password for the tests to obtain an authentication
           token. Substitute your PATRIC username and password and run the tests with this command::
        
            (mackinac-py27)$ TEST_USERNAME=<username> TEST_PASSWORD=<password> pytest mackinac/test
        
        Run examples in a notebook
        ^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        An example of how to use Mackinac is provided in a notebook. Here's how to start Jupyter and run
        the notebook from the virtualenv.
        
        1. Install Jupyter with this command::
        
            (mackinac-py27)$ pip install jupyter
        
        2. Install a kernel that uses the virtualenv installation with this command::
        
            (mackinac-py27)$ ipython kernel install --name "Mackinac_Python27" --user
        
        3. Start the Jupyter notebook server with this command::
        
            (mackinac-py27)$ jupyter notebook
        
           Jupyter opens a web page in your default browser with a file browser.
        
        4. Navigate to the "documentation_builder" folder and click on the "modelseed.ipynb" notebook.
        
        5. After the notebook opens, from the "Kernel" menu, select "Change kernel" and click on "Mackinac_Python27".
        
        6. Now you can run the cells in the notebook.
        
        References
        ----------
        
        1. `COBRApy: COnstraints-Based Reconstruction and Analysis for Python <http://dx.doi.org/doi:10.1186/1752-0509-7-74>`_
        2. `High-throughput generation, optimization and analysis of genome-scale metabolic models <http://dx.doi.org/doi:10.1038/nbt.1672>`_ (ModelSEED)
        3. `PATRIC, the bacterial bioinformatics database and analysis resource <http://dx.doi.org/doi:10.1093/nar/gkt1099>`_
        
        Mackinac Bridge
        ^^^^^^^^^^^^^^^
        
        The `Mackinac Bridge <http://www.mackinacbridge.org>`_ is one of the longest suspension bridges in
        the United States and spans the Straits of Mackinac to connect the Upper and Lower Peninsulas of Michigan.
        
Keywords: metabolism biology optimization flux balance analysis fba
Platform: GNU/Linux
Platform: Mac OS X >= 10.7
Platform: Microsoft Windows >= 7
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
