Metadata-Version: 1.1
Name: proteofav
Version: 0.2.2
Summary: PROtein Feature Aggregation and Variants.
Home-page: https://github.com/bartongroup/ProteoFAV/tree/master
Author: ['Fábio Madeira', 'Thiago Britto-Borges', 'Stuart MacGowan']
Author-email: tbrittoborges@dundee.ac.uk
License: LICENSE.md
Download-URL: https://github.com/bartongroup/ProteoFAV/archive/master.zip
Description-Content-Type: UNKNOWN
Description: ProteoFAV
        =========
        
        **Protein Feature Aggregation and Variants**
        
        |Pypi| |Build Status| |Documentation| |Python: versions| |License|
        
        .. |Pypi| image:: https://img.shields.io/pypi/v/proteofav.svg
          :target: https://pypi.python.org/pypi/proteofav
        .. |Build Status| image:: https://img.shields.io/travis/bartongroup/proteofav.svg
          :target: https://travis-ci.org/bartongroup/proteofav
        .. |Documentation| image:: https://readthedocs.org/projects/proteofav/badge/?version=latest
          :target: https://proteofav.readthedocs.io/en/latest/?badge=latest
          :alt: Documentation Status
        .. |Coverage Status| image:: https://coveralls.io/repos/github/bartongroup/proteofav/badge.svg?branch=master
          :target: https://coveralls.io/github/bartongroup/proteofav?branch=master
        .. |Health| image:: https://landscape.io/github/bartongroup/proteofav/master/landscape.svg?style=flat
          :target: https://landscape.io/github/bartongroup/proteofav/master
        .. |Pyup| image:: https://pyup.io/repos/github/bartongroup/proteofav/shield.svg
           :target: https://pyup.io/repos/github/bartongroup/proteofav/
           :alt: Updates
        .. |License| image:: http://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat
          :target: https://github.com/bartongroup/proteofav//blob/master/LICENSE.md
        .. |Python: versions| image:: https://img.shields.io/badge/python-3.5,_3.6-blue.svg?style=flat
           :target: http://travis-ci.org/bartongroup/proteofav/
        
        ProteoFAV is a Python module that address the challenge of cross-mapping protein structures and protein sequences,
        allowing for protein structures to be annotated with sequence features. It implements methods for working with
        protein structures (via mmCIF, PDB, PDB Validation, DSSP and SIFTS files), sequence Features (via UniProt GFF annotations) and
        genetic variants (via UniProt/EBI Proteins API and Ensembl REST API). Cross-mapping of structure and sequence is
        performed with the aid of SIFTS.
        
        ProteFAV relies heavily in the `Pandas`_ library to quickly load data into DataFrames for fast
        data exploration and analysis. Structure and sequence
        data are parsed/fetched onto Pandas DataFrames that are then merged-together (collapsed) onto a
        single DataFrame.
        
        Getting Started
        ---------------
        
        Dependencies
        ~~~~~~~~~~~~
        
        ProteoFAV was developed to support Python 3.5+ and Pandas 0.20+.
        
        Check `requirements`_ for specific requirements.
        
        .. _requirements: https://github.com/bartongroup/ProteoFAV/blob/master/requirements.txt
        
        
        Installation
        ~~~~~~~~~~~~
        
        To install the stable release, run this command in your terminal:
        
        .. code-block:: console
        
            $ pip install proteofav
        
        If you don't have `pip`_ installed, this `Python installation guide`_ can guide
        you through the process.
        
        .. _pip: https://pip.pypa.io
        .. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/
        
        
        Installing from source in a virtual environment
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Getting ProteoFAV:
        
        .. code-block:: bash
        
            $ wget https://github.com/bartongroup/ProteoFAV/archive/master.zip -O ProteoFAV.zip
            $ unzip ProteoFAV.zip
        
            # alternatively, cloning the git repository
            $ git clone https://github.com/bartongroup/ProteoFAV.git
        
        
        Installing With Conda:
        
        .. code-block:: bash
        
            $ conda-env create -n proteofav -f path/to/ProteoFAV/requirements.txt
            $ source activate proteofav
            $ cd path/to/ProteoFAV
            $ pip install .
        
        Installing with Virtualenv:
        
        .. code-block:: bash
        
            $ virtualenv --python `which python` env
            $ source env/bin/activate
            $ pip install -r requirements.txt
            $ python path/to/ProteoFAV/setup.py install
        
        
        Testing the installation
        ~~~~~~~~~~~~~~~~~~~~~~~~
        
        Test dependencies should be resolved with:
        
        .. code-block:: bash
        
            $ python path/to/ProteoFAV/setup.py develop --user
        
        
        Run the Tests with:
        
        .. code-block:: bash
        
            $ python path/to/ProteoFAV/setup.py test
            # or
            $ cd path/to/ProteoFAV/tests
            $ python -m unittest discover
        
        
        ProteoFAV Configuration
        ~~~~~~~~~~~~~~~~~~~~~~~
        
        ProteoFAV uses a configuration file `config.ini` where the user can specify the directory paths, as well as urls for commonly used data sources.
        
        After installing run:
        
        .. code-block:: bash
        
            $ proteofav-setup
        
        
        Example Usage
        -------------
        
        Example usage is currently provided as a `Jupyter Notebook`, which can be viewed with the `GitHub's`_ file viewer or with the Jupyter `nbviewer`_.
        
        You can download the Jupyter notebook from `GitHub`_ and test it with your ProteoFAV's installation.
        
        .. _GitHub's: https://github.com/bartongroup/ProteoFAV/blob/master/Examples.ipynb
        .. _nbviewer: https://nbviewer.jupyter.org/github/bartongroup/ProteoFAV/blob/master/Examples.ipynb
        .. _GitHub: https://github.com/bartongroup/ProteoFAV
        
        
        Contributing and Bug tracking
        -----------------------------
        
        Feel free to fork, clone, share and distribute. If you find any bugs or
        issues please log them in the `issue tracker`_.
        
        Before you submit your *Pull-requests* read the `Contributing Guide`_.
        
        Credits
        -------
        
        See the `Credits`_
        
        
        Changelog
        ---------
        
        See the `Changelog`_
        
        
        Licensing
        ---------
        
        The MIT License (MIT). See `license`_ for details.
        
        .. _requirements: https://github.com/bartongroup/ProteoFAV/blob/master/requirements.txt
        .. _license: https://github.com/bartongroup/ProteoFAV/blob/master/LICENSE.md
        .. _issue tracker: https://github.com/bartongroup/ProteoFAV/issues
        .. _docs: https://github.com/bartongroup/ProteoFAV/blob/master/docs/index.rst
        .. _Pandas: http://pandas.pydata.org/
        .. _Contributing Guide: https://github.com/bartongroup/ProteoFAV/wiki/Contributing-Guide
        .. _Changelog: https://github.com/bartongroup/ProteoFAV/blob/master/CHANGELOG.rst
        .. _Credits: https://github.com/bartongroup/ProteoFAV/blob/master/AUTHORS.rst
        
Keywords: bioinformatics structural-biology data-analysis python pandas
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Internet
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: MIT License
