A framework for proteomics data analysis.
Project description
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+.
Project dependencies
Pyteomics uses the following Python packages:
matplotlib (used by pyteomics.pylab_aux)
lxml (used by XML parsing modules)
pandas (can be used with pyteomics.pepxml, pyteomics.tandem, pyteomics.mzid, pyteomics.auxiliary)
sqlalchemy (used by pyteomics.mass.unimod)
pynumpress (adds support for Numpress compression)
All dependencies are optional.
GNU/Linux
The preferred way to obtain Pyteomics is via pip Python package manager. The shell code for a freshly installed Ubuntu system:
sudo apt-get install python-setuptools python-dev build-essential sudo easy_install pip sudo pip install lxml numpy matplotlib pyteomics
Arch-based distros
On Arch Linux and related distros, you can install Pyteomics from AUR:
Windows
Get pip, if you don’t have it yet.
Install Pyteomics and its dependencies:
pip install lxml numpy matplotlib pyteomics
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