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        "author": "Anton Goloborodko & Lev Levitsky",
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            "Programming Language :: Python :: 2.7",
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            "Topic :: Education",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
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        "description": ".. image:: https://img.shields.io/pypi/v/pyteomics.svg\n    :target: https://pypi.org/project/pyteomics/\n    :alt: PyPI\n\n.. image:: https://img.shields.io/readthedocs/pyteomics.svg\n    :target: https://pyteomics.readthedocs.io/\n    :alt: Read the Docs (latest)\n\n.. image:: https://img.shields.io/aur/license/python-pyteomics.svg\n    :target: https://www.apache.org/licenses/LICENSE-2.0\n    :alt: Apache License\n\n.. image:: https://img.shields.io/aur/version/python-pyteomics.svg\n    :target: https://aur.archlinux.org/packages/python-pyteomics/\n    :alt: python-pyteomics on AUR\n\n.. image:: https://img.shields.io/badge/pyteomics-awesome-orange.svg\n    :alt: Pyteomics is awesome\n\n\nWhat is Pyteomics?\n------------------\n\nPyteomics is a collection of lightweight and handy tools for Python that help\nto handle various sorts of proteomics data. Pyteomics provides a growing set of\nmodules to facilitate the most common tasks in proteomics data analysis, such as:\n\n* calculation of basic physico-chemical properties of polypeptides:\n\n  * mass and isotopic distribution\n  * charge and pI\n  * chromatographic retention time\n\n* access to common proteomics data:\n\n  * MS or LC-MS data\n  * FASTA databases\n  * search engines output\n\n* easy manipulation of sequences of modified peptides and proteins\n\nThe goal of the Pyteomics project is to provide a versatile, reliable and\nwell-documented set of open tools for the wide proteomics community.\nOne of the project's key features is Python itself, an open source language\nincreasingly popular in scientific programming. The main\napplications of the library are reproducible statistical data analysis and rapid\nsoftware prototyping.\n\nSupported Python versions\n-------------------------\n\nPyteomics supports Python 2.7 and Python 3.3+.\n\nProject dependencies\n--------------------\n\nPyteomics uses the following Python packages:\n\n - `numpy <http://pypi.python.org/pypi/numpy>`_\n - `matplotlib <http://sourceforge.net/projects/matplotlib/files/matplotlib/>`_\n   (used by **pyteomics.pylab_aux**)\n - `lxml <http://pypi.python.org/pypi/lxml>`_ (used by XML parsing modules)\n - `pandas <http://pandas.pydata.org/>`_ (can be used with **pyteomics.pepxml**,\n   **pyteomics.tandem**, **pyteomics.mzid**, **pyteomics.auxiliary**)\n - `sqlalchemy <http://www.sqlalchemy.org/>`_ (used by **pyteomics.mass.unimod**)\n\nAll dependencies are optional.\n\nGNU/Linux\n---------\n\nThe preferred way to obtain Pyteomics is via `pip Python package manager <https://pip.pypa.io/>`_.\nThe shell code for a freshly installed Ubuntu system::\n\n    sudo apt-get install python-setuptools python-dev build-essential\n    sudo easy_install pip\n    sudo pip install lxml numpy matplotlib pyteomics\n\nArch-based distros\n..................\n\nOn Arch Linux and related distros, you can install Pyteomics from AUR:\n\n - `python-pyteomics <https://aur.archlinux.org/packages/python-pyteomics/>`_\n - `python2-pyteomics <https://aur.archlinux.org/packages/python2-pyteomics/>`_\n\n\nWindows\n-------\n\n- `Get pip <https://pip.pypa.io/en/stable/installing/>`_, if you don't have it yet.\n\n- Install Pyteomics and its dependencies::\n\n    pip install lxml numpy matplotlib pyteomics\n\n\n\n",
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