Skip to main content

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.

Pyteomics is hosted at the following sites:

Feedback & Support

Please email to pyteomics@googlegroups.com with any questions about Pyteomics. You are welcome to use the BitBucket issue tracker to report bugs, request features, etc.

Required Python versions

Pyteomics supports Python 2.7 and Python 3. Python 2.6 and older are not supported.

Project dependencies

Pyteomics uses the following python packages:

  • numpy

  • matplotlib (used by pyteomics.pylab_aux)

  • lxml (used by pyteomics.mzml, pyteomics.pepxml, pyteomics.mzid)

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

Windows

  • Download pre-compiled binary packages for Pyteomics dependencies:

  • Download a pre-compiled binary Pyteomics package from the list.

OR

  • If you have Enthought Python Distribution / ActivePython, execute in the command line:

    easy_install pip
    pip install lxml numpy matplotlib pyteomics

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyteomics-2.1.4.tar.gz (70.6 kB view details)

Uploaded Source

Built Distributions

pyteomics-2.1.4.win-amd64_py3.3.exe (282.5 kB view details)

Uploaded Source

pyteomics-2.1.4.win-amd64_py3.2.exe (284.5 kB view details)

Uploaded Source

pyteomics-2.1.4.win-amd64_py2.7.exe (284.0 kB view details)

Uploaded Source

pyteomics-2.1.4.win32_py3.3.exe (251.2 kB view details)

Uploaded Source

pyteomics-2.1.4.win32_py3.2.exe (256.4 kB view details)

Uploaded Source

pyteomics-2.1.4.win32_py2.7.exe (256.4 kB view details)

Uploaded Source

File details

Details for the file pyteomics-2.1.4.tar.gz.

File metadata

  • Download URL: pyteomics-2.1.4.tar.gz
  • Upload date:
  • Size: 70.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for pyteomics-2.1.4.tar.gz
Algorithm Hash digest
SHA256 502f0dfe562cd2fc9bb74091cb092d5ad74e185f1c30edb2a874bce97efd8720
MD5 7a61b806b50788a7bb44fd111025e0cd
BLAKE2b-256 1a59a0e1bd31792b0cda45dcf2b939d4ee8639f7c70ff3b598fa142c9915733f

See more details on using hashes here.

File details

Details for the file pyteomics-2.1.4.win-amd64_py3.3.exe.

File metadata

File hashes

Hashes for pyteomics-2.1.4.win-amd64_py3.3.exe
Algorithm Hash digest
SHA256 6c5b95742acb6ff23bd5bb4fa8a4c6a372803b1e30fe87058d5df37cc0dd489a
MD5 d3f89d14a7043fc21ef091ba14431470
BLAKE2b-256 fc8e9225f83ffcb5147338e83b17962ff77660d86b977591b27b5606a121ea76

See more details on using hashes here.

File details

Details for the file pyteomics-2.1.4.win-amd64_py3.2.exe.

File metadata

File hashes

Hashes for pyteomics-2.1.4.win-amd64_py3.2.exe
Algorithm Hash digest
SHA256 519c676cb85346a8be7cb4bdde3298ce0f0bff8e83721c3fe1720eaf8ce4ce09
MD5 81d71e1dcfa36051c84e668c6ca8eaac
BLAKE2b-256 02f4a856a7d8883da55578fd571442dfb6ad2849a9c19c1cb731684bd72012a5

See more details on using hashes here.

File details

Details for the file pyteomics-2.1.4.win-amd64_py2.7.exe.

File metadata

File hashes

Hashes for pyteomics-2.1.4.win-amd64_py2.7.exe
Algorithm Hash digest
SHA256 4ca04a44c77786627321dd18daa6979f27956bfbaf5329ed8a19caf03f628553
MD5 03ddd2efb51afc7b4e796e99dffacea5
BLAKE2b-256 e034e96456c8ca8feca64ffe7dafc53f870989e60bee0946546ce5d56c36f0ed

See more details on using hashes here.

File details

Details for the file pyteomics-2.1.4.win32_py3.3.exe.

File metadata

File hashes

Hashes for pyteomics-2.1.4.win32_py3.3.exe
Algorithm Hash digest
SHA256 51b1903caf1fab1b77730029ae062fa70577605f19bbbbedaf7a2e99c6417fdb
MD5 0ae202a8f0502b2d6736f859a8fd8520
BLAKE2b-256 0a136c1d59271f226a95703a4f19ebeef49ff3018917c1c4790e1c346f40c406

See more details on using hashes here.

File details

Details for the file pyteomics-2.1.4.win32_py3.2.exe.

File metadata

File hashes

Hashes for pyteomics-2.1.4.win32_py3.2.exe
Algorithm Hash digest
SHA256 28b8edcf72f4298cc8f226e4e38520aad19e3f28ab80b32b1fd47dec85dca0e7
MD5 b19597beab8e3c7b2920096cce89c8cb
BLAKE2b-256 2b30fdb5962bff34dd6a12cf1e69e1580efc585e79e681c0d43078d59b20b7fd

See more details on using hashes here.

File details

Details for the file pyteomics-2.1.4.win32_py2.7.exe.

File metadata

File hashes

Hashes for pyteomics-2.1.4.win32_py2.7.exe
Algorithm Hash digest
SHA256 9c7f0fac9cae4d1a5b74d0e0686cccc67a3e1510878d7bf1b7286414cf6e8d68
MD5 60ed74073c8b8f1c4e8026efd60fa8c3
BLAKE2b-256 b368bd959f29e8091ccfe48118b6e2d58ded23768c915548ddc7d39bab337869

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page