.. shephard documentation master file, created by sphinx-quickstart on Thu Mar 15 13:55:56 2018. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. SHEPHARD ========================================================= SHEPHARD is a Python-based software framework for reading, annotating, and analyzing large protein datasets. It's objective is to make it easy for people to perform reproducible, error-free analyses of large protein datasets with arbitrarily complex sequence annotations. The general use-case for SHEPHARD would include scenarios such as: 1. Wanting to ask large-scale statistical questions about big protein datasets. 2. Asking how different types of sequence or protein annotations relate to one another. 3. Easily associating experimentally-generated data with extant sequence information. Installation -------------- SHEPHARD is distributed via the Python packaging index (PyPI). As such, the current public release candidate can be installed using:: pip install shephard Alternatively, you can install the current bleeding-edge version from GitHub using .. code-block:: Bash pip install shephard@git+git://github.com/holehouse-lab/shephard.git This should install without issue, and once installed, SHEPHARD is available for import in any Python code you write when executed from within that :code:`conda` environment. To test if the installation has worked, open up the Python interpreter or a Jupyter notebook and run .. code-block:: Python import shephard If this works, you should be good to go! A note on Python environments ................................. To install, we strongly recommend having a :code:`conda` environment set up (the scope of :code:`conda` setup is beyond that of this documentation). :code:`conda` environments let you define a specific Python version and set of local packages that help isolate software tools away from your main system's Python version. For more information there are many `examples of conda introduction tutorials online, like this one here `_. Demos and examples -------------------- Once installed, SHEPHARD makes it very easy to work with large protein datasets. As an example, we have a collection of basic notebooks showing functionality located at on the `SHEPHARD GitHub supporting data page `_. Colab notebook -------------------- If you don't want to install SHEPHARD on your local computer, we also provide several Google colab notebooks with SHEPHARD pre-installed. Check out our `colab notebook repository on GitHub `_. About --------- SHEPHARD was developed and written by Garrett Ginell and Alex Holehouse in the Holehouse lab. For issues, bugs, or feature requests `please raise an issue on GitHub `_. Documentation index ---------------------- .. toctree:: :maxdepth: 2 :caption: Contents: overview getting_started proteome protein domain site track shephard_file_types interfaces tools apis code_convention Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search`