.. image:: https://github.com/levitsky/pyteomics/workflows/tests/badge.svg
:target: https://github.com/levitsky/pyteomics/actions?query=workflow%3Atests
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:target: https://pypi.org/project/pyteomics/
:alt: PyPI
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:target: http://bioconda.github.io/recipes/pyteomics/README.html
:alt: conda
.. image:: https://img.shields.io/readthedocs/pyteomics.svg
:target: https://pyteomics.readthedocs.io/
:alt: Read the Docs (latest)
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:target: https://www.apache.org/licenses/LICENSE-2.0
:alt: Apache License
.. image:: https://img.shields.io/aur/version/python-pyteomics.svg
:target: https://aur.archlinux.org/packages/python-pyteomics/
:alt: python-pyteomics on AUR
.. image:: https://img.shields.io/badge/pyteomics-awesome-orange.svg
:alt: Pyteomics is awesome
What is Pyteomics?
------------------
Pyteomics is a collection of lightweight and handy tools for Python that help
to handle various sorts of proteomics data. Pyteomics provides a growing set of
modules to facilitate the most common tasks in proteomics data analysis, such as:
* calculation of basic physico-chemical properties of polypeptides:
* mass and isotopic distribution
* charge and pI
* chromatographic retention time
* access to common proteomics data:
* MS or LC-MS data
* FASTA databases
* search engines output
* easy manipulation of sequences of modified peptides and proteins
The goal of the Pyteomics project is to provide a versatile, reliable and
well-documented set of open tools for the wide proteomics community.
One of the project's key features is Python itself, an open source language
increasingly popular in scientific programming. The main
applications of the library are reproducible statistical data analysis and rapid
software prototyping.
Supported Python versions
-------------------------
Pyteomics supports Python 2.7 and Python 3.3+.
Install with pip
----------------
The main way to obtain Pyteomics is via `pip Python package manager <https://pip.pypa.io/>`_::
pip install pyteomics
Install with conda
------------------
You can also install Pyteomics from `Bioconda <https://bioconda.github.io/index.html>`_
using `conda <https://docs.conda.io/projects/conda/en/latest/index.html>`_::
conda install -c bioconda pyteomics
Arch-based distros
..................
On Arch Linux and related distros, you can install Pyteomics from AUR:
`python-pyteomics <https://aur.archlinux.org/packages/python-pyteomics/>`_
Project dependencies
--------------------
Some functionality in Pyteomics relies on other packages:
- `numpy <https://numpy.org/>`_;
- `matplotlib <https://matplotlib.org/>`_ (used by **pyteomics.pylab_aux**);
- `lxml <https://lxml.de/>`_ (used by XML parsing modules and **pyteomics.mass.mass.Unimod**);
- `pandas <https://pandas.pydata.org/>`_ (can be used with **pyteomics.pepxml**,
**pyteomics.tandem**, **pyteomics.mzid**, **pyteomics.auxiliary**);
- `sqlalchemy <https://www.sqlalchemy.org/>`_ (used by **pyteomics.mass.unimod**);
- `pynumpress <https://pypi.org/project/pynumpress/>`_ (adds support for Numpress compression in mzML);
- `h5py <https://www.h5py.org/>`_ and optionally `hdf5plugin <https://hdf5plugin.readthedocs.io/en/latest/>`_
(used by **pyteomics.mzmlb**);
- `psims <https://mobiusklein.github.io/psims/docs/build/html/>`_ (used py **pyteomics.proforma**);
- `spectrum_utils <https://spectrum-utils.readthedocs.io/en/latest/>`_ (optionally used for spectrum annotation in
**pyteomics.pylab_aux**).
All dependencies are optional.
Installing a subset of dependencies with pip
............................................
You can quickly install just the dependencies you need by specifying an
`"extra" <https://setuptools.pypa.io/en/latest/userguide/dependency_management.html#optional-dependencies>`_. For example::
pip install pyteomics[XML]
This will install Pyteomics, NumPy and lxml, which are needed to read XML format. Currently provided identifiers are:
`XML`, `TDA`, `graphics`, `DF`, `Unimod`, `numpress`, `mzMLb`, `proforma`.
You can also use these specs as dependencies in your own packages which require specific Pyteomics functionality.
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