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Introducing the Socket Python SDK
The initial version of the Socket Python SDK is now on PyPI, enabling developers to more easily interact with the Socket REST API in Python projects.
.. image:: https://github.com/levitsky/pyteomics/workflows/tests/badge.svg :target: https://github.com/levitsky/pyteomics/actions?query=workflow%3Atests :alt: Test status
.. image:: https://img.shields.io/pypi/v/pyteomics.svg :target: https://pypi.org/project/pyteomics/ :alt: PyPI
.. image:: https://img.shields.io/conda/vn/bioconda/pyteomics :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)
.. image:: https://img.shields.io/github/license/levitsky/pyteomics :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
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:
access to common proteomics data:
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.
Pyteomics supports Python 2.7 and Python 3.3+.
The main way to obtain Pyteomics is via pip Python package manager <https://pip.pypa.io/>
_::
pip install pyteomics
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/>
_
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.
FAQs
A framework for proteomics data analysis.
We found that pyteomics demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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