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-2.1.1.tar.gz (68.6 kB view details)

Uploaded Source

Built Distributions

pyteomics-2.1.1.win-amd64_py3.3.exe (281.2 kB view details)

Uploaded Source

pyteomics-2.1.1.win-amd64_py3.2.exe (283.2 kB view details)

Uploaded Source

pyteomics-2.1.1.win-amd64_py2.7.exe (282.7 kB view details)

Uploaded Source

pyteomics-2.1.1.win32_py3.3.exe (250.0 kB view details)

Uploaded Source

pyteomics-2.1.1.win32_py3.2.exe (255.1 kB view details)

Uploaded Source

pyteomics-2.1.1.win32_py2.7.exe (255.1 kB view details)

Uploaded Source

File details

Details for the file pyteomics-2.1.1.tar.gz.

File metadata

  • Download URL: pyteomics-2.1.1.tar.gz
  • Upload date:
  • Size: 68.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for pyteomics-2.1.1.tar.gz
Algorithm Hash digest
SHA256 602b305c1d60e91aedea65f853f1b0b241ef69d35a3f6bbfb80b241888c3df3c
MD5 9f8f336aa656736557accf58d2ac4fb0
BLAKE2b-256 2ef5bfbf90c50c2c3a495d4ecebdf6726b0a4aa4230aba2979b7b68aab8fede8

See more details on using hashes here.

File details

Details for the file pyteomics-2.1.1.win-amd64_py3.3.exe.

File metadata

File hashes

Hashes for pyteomics-2.1.1.win-amd64_py3.3.exe
Algorithm Hash digest
SHA256 22cc58afa47af97a1dc285b5f55fe42afd39d84b1fe5eca23173798ae968e0e0
MD5 a77da6a9af260b537deb20c3dd638aeb
BLAKE2b-256 abff2f3d3bf61b13708a44f3dc1a9e32e35a2e73e2bf3243612bdac4bcc4365f

See more details on using hashes here.

File details

Details for the file pyteomics-2.1.1.win-amd64_py3.2.exe.

File metadata

File hashes

Hashes for pyteomics-2.1.1.win-amd64_py3.2.exe
Algorithm Hash digest
SHA256 15d02332d8e9d8ecb112a629527dc29054934be2d29eed29429ab171343658bc
MD5 99adc10f623b07479bcf461c6960968d
BLAKE2b-256 7b3fa0f4b12492ece3af5ffa25f5b93b19fba4e1a151fa9355e4e5cad57f9042

See more details on using hashes here.

File details

Details for the file pyteomics-2.1.1.win-amd64_py2.7.exe.

File metadata

File hashes

Hashes for pyteomics-2.1.1.win-amd64_py2.7.exe
Algorithm Hash digest
SHA256 4f2cbb29c2ac1488a35894a33cb8c93a1e4bc412d40aa1d78a6697ae89ec53ae
MD5 b10218967388279215aa2f100194684a
BLAKE2b-256 cc89760da1b5eea2c7f8d0e3af96093c54af56296ab3f0056cacd293dd6fa876

See more details on using hashes here.

File details

Details for the file pyteomics-2.1.1.win32_py3.3.exe.

File metadata

File hashes

Hashes for pyteomics-2.1.1.win32_py3.3.exe
Algorithm Hash digest
SHA256 3e9e19c6df1c080651387683a93c5677b7f553805280f96b9389c7a9429f20c6
MD5 6ae63720627f5fcc218c218ae324be38
BLAKE2b-256 e627f7bad0d6aa92c0482414cf47178e4285183619dedc55b7960e911271be0f

See more details on using hashes here.

File details

Details for the file pyteomics-2.1.1.win32_py3.2.exe.

File metadata

File hashes

Hashes for pyteomics-2.1.1.win32_py3.2.exe
Algorithm Hash digest
SHA256 589a05f3e5b7db3600fa888b46422ec3004145360065ddbbd1cda3bcc7a32525
MD5 750a867760399a70d519de20cc46b1cb
BLAKE2b-256 02eec93cc8253fed77e9d57d898582c7e82075092911338b7fd9c4b71cf61af9

See more details on using hashes here.

File details

Details for the file pyteomics-2.1.1.win32_py2.7.exe.

File metadata

File hashes

Hashes for pyteomics-2.1.1.win32_py2.7.exe
Algorithm Hash digest
SHA256 0c67b97a11ca873d2788d5385bafb92eb4855d0133cf8c9dc01214827d5c8dfa
MD5 ecee21cdb1c9538cd86e5fcb53f57c91
BLAKE2b-256 5dfe091b03b2f4571687e914081d41de4847896af4a7a0bffc3517715da79e5c

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