Skip to main content

MS2PIP: Accurate and versatile peptide fragmentation spectrum prediction.

Project description

https://github.com/compomics/ms2pip_c/raw/releases/img/ms2pip_logo_1000px.png

https://img.shields.io/github/v/release/compomics/ms2pip_c?include_prereleases&style=flat-square https://img.shields.io/pypi/v/ms2pip?style=flat-square https://img.shields.io/github/actions/workflow/status/compomics/ms2pip_c/test.yml?branch=releases&label=tests&style=flat-square https://img.shields.io/github/actions/workflow/status/compomics/ms2pip_c/build_and_publish.yml?style=flat-square https://img.shields.io/github/issues/compomics/ms2pip_c?style=flat-square https://img.shields.io/github/last-commit/compomics/ms2pip_c?style=flat-square https://img.shields.io/github/license/compomics/ms2pip_c?style=flat-square https://img.shields.io/twitter/follow/compomics?style=social

MS²PIP: MS2 Peak Intensity Prediction - Fast and accurate peptide fragmentation spectrum prediction for multiple fragmentation methods, instruments and labeling techniques.


About

MS²PIP is a tool to predict MS2 peak intensities from peptide sequences. The result is a predicted peptide fragmentation spectrum that accurately resembles its observed equivalent. These predictions can be used to validate peptide identifications, generate proteome-wide spectral libraries, or to select discriminative transitions for targeted proteomics. MS²PIP employs the XGBoost machine learning algorithm and is written in Python and C.

https://raw.githubusercontent.com/compomics/ms2pip/v4.0.0/img/mirror-DVAQIFNNILR-2.png

Mirror plot of an observed (top) and MS²PIP-predicted (bottom) spectrum for the peptide DVAQIFNNILR/2.

You can install MS²PIP on your machine by following the installation instructions. For a more user-friendly experience, go to the MS²PIP web server. There, you can easily upload a list of peptide sequences, after which the corresponding predicted MS2 spectra can be downloaded in multiple file formats. The web server can also be contacted through the RESTful API.

The MS³PIP Python application can perform the following tasks:

  • predict-single: Predict fragmentation spectrum for a single peptide and optionally visualize the spectrum.

  • predict-batch: Predict fragmentation spectra for a batch of peptides.

  • predict-library: Predict a spectral library from protein FASTA file.

  • correlate: Compare predicted and observed intensities and optionally compute correlations.

  • get-training-data: Extract feature vectors and target intensities from observed spectra for training.

  • annotate-spectra: Annotate peaks in observed spectra.

MS²PIP supports a wide range of PSM input formats and spectrum output formats, and includes pre-trained models for multiple fragmentation methods, instruments and labeling techniques. See Usage for more information.

Citations

If you use MS²PIP for your research, please cite the following publication:

  • Declercq, A., Bouwmeester, R., Chiva, C., Sabidó, E., Hirschler, A., Carapito, C., Martens, L., Degroeve, S., Gabriels, R. (2023). Updated MS²PIP web server supports cutting-edge proteomics applications. Nucleic Acids Research doi:10.1093/nar/gkad335

Prior MS²PIP publications:

  • Gabriels, R., Martens, L., & Degroeve, S. (2019). Updated MS²PIP web server delivers fast and accurate MS2 peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques. Nucleic Acids Research doi:10.1093/nar/gkz299

  • Degroeve, S., Maddelein, D., & Martens, L. (2015). MS²PIP prediction server: compute and visualize MS2 peak intensity predictions for CID and HCD fragmentation. _Nucleic Acids Research, 43(W1), W326–W330. doi:10.1093/nar/gkv542

  • Degroeve, S., & Martens, L. (2013). MS²PIP: a tool for MS/MS peak intensity prediction. Bioinformatics (Oxford, England), 29(24), 3199–203. doi:10.1093/bioinformatics/btt544

Please also take note of, and mention, the MS²PIP version you used.

Full documentation

The full documentation, including installation instructions, usage examples, and the command-line and Python API reference, can be found at ms2pip.readthedocs.io.

Contributing

Bugs, questions or suggestions? Feel free to post an issue in the issue tracker or to make a pull request. Any contribution, small or large, is welcome!

Project details


Download files

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

Source Distribution

ms2pip-4.0.0.tar.gz (5.5 MB view details)

Uploaded Source

Built Distributions

ms2pip-4.0.0-cp311-cp311-win_amd64.whl (13.6 MB view details)

Uploaded CPython 3.11 Windows x86-64

ms2pip-4.0.0-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.5 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.5+ x86-64

ms2pip-4.0.0-cp311-cp311-macosx_11_0_arm64.whl (14.0 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

ms2pip-4.0.0-cp311-cp311-macosx_10_9_x86_64.whl (14.0 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

ms2pip-4.0.0-cp310-cp310-win_amd64.whl (13.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

ms2pip-4.0.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.5 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.5+ x86-64

ms2pip-4.0.0-cp310-cp310-macosx_11_0_arm64.whl (14.0 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

ms2pip-4.0.0-cp310-cp310-macosx_10_9_x86_64.whl (14.0 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

ms2pip-4.0.0-cp39-cp39-win_amd64.whl (13.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

ms2pip-4.0.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.5+ x86-64

ms2pip-4.0.0-cp39-cp39-macosx_11_0_arm64.whl (14.0 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

ms2pip-4.0.0-cp39-cp39-macosx_10_9_x86_64.whl (14.0 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

ms2pip-4.0.0-cp38-cp38-win_amd64.whl (13.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

ms2pip-4.0.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.5 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.5+ x86-64

ms2pip-4.0.0-cp38-cp38-macosx_11_0_arm64.whl (14.0 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

ms2pip-4.0.0-cp38-cp38-macosx_10_9_x86_64.whl (14.0 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

File details

Details for the file ms2pip-4.0.0.tar.gz.

File metadata

  • Download URL: ms2pip-4.0.0.tar.gz
  • Upload date:
  • Size: 5.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.8.10

File hashes

Hashes for ms2pip-4.0.0.tar.gz
Algorithm Hash digest
SHA256 b2329700e8c2af5c64d8fa0ef62dbc6ee52c60d7b18e5d860a4d9194c225ec29
MD5 425276f69ca4dca8648e6442a979020a
BLAKE2b-256 0a3900be1aa38c97ec455d9dded6ea6cc47fd9f0960f2adec86cb498ef4a679e

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: ms2pip-4.0.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 13.6 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.8.10

File hashes

Hashes for ms2pip-4.0.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 9fe8c9c97c46835d7cfcd34b185a154553b19f080d9b24d383a096a8809c868b
MD5 765b62168b84f43eb940a0bf100ac9ae
BLAKE2b-256 f2f431f3599da7c05cea8dc4c71cad7c12d0a4be7c8719515be9e328d138c045

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ms2pip-4.0.0-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 95a555df666a20dce23c06371b7ecac8a31f3d540500bef9f2f26692d1aa7129
MD5 eba28390fe355a500c70340649735e96
BLAKE2b-256 7df9f5596a292a12982702b433051e8ac6ccd5b3a23a5594d1b39158b0d20d6e

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ms2pip-4.0.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 20a3f3f428288863f68e872bc16a72610984863c87565033817e6afdd7d2f045
MD5 1d8b67726dfe4ba7014ca271fe6c3919
BLAKE2b-256 4bf565802038f416bc3137bd9fc20d42afbf75ef968b83dd8c92e7bd332ee0aa

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for ms2pip-4.0.0-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 d88a90a054337707b9ead5ba4f30d724545c0f0178520a008321090b4224a840
MD5 3d5a23bc5a6cd879c3eed6728e8433c3
BLAKE2b-256 05137f0411112ffc61727d2fc35ff9928dfc878ef795c480099f0bda3fc26006

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: ms2pip-4.0.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 13.6 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.8.10

File hashes

Hashes for ms2pip-4.0.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 467b6be24765cdd4a72f46ce1be73dce0ad87a0abf2cad3b93c95ef1c31d85b5
MD5 a3d5ced2d5197823d9e8656d8af9e861
BLAKE2b-256 30b095023b286d7e4f13dc3e3ccafe0d319e4e5df97b0705d0d497e57e56e525

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ms2pip-4.0.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6836963a4e99bb9addf9d3c334eea2f899f8f563c8e4769e76344e63684c8e3b
MD5 57e775de5ed3b175b5f5640d010d848e
BLAKE2b-256 f9e5dc479a6c36f9e8c17f06249e17d9ab2760ee0618453837cc1653707c7d4b

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ms2pip-4.0.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c58467a57ec645487ebd97834821ffc06b985c39de7b7e4e9a8a44ff7ab8e207
MD5 4161084ff63c0e55e32d22215bf54e1a
BLAKE2b-256 4f35795d55500f5697687d81fe7b60de2edd7fbc1e3eaa8f4316247ab0ee6cfd

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for ms2pip-4.0.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 280a7e528fcb429adf90d96e96bf902b8b66f78ecf64b23dc2808f4d2176c62f
MD5 02730cd9b562c89b1aa9ed6f3f523798
BLAKE2b-256 20f80f50b24510abab22e573c0970d3a9f29f48e8ae3143df6790c428d3f65fc

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: ms2pip-4.0.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 13.6 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.8.10

File hashes

Hashes for ms2pip-4.0.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 3c1c0d6efc06ec708af0fb785c7dd9c11ed35cd7d913c0d346a9c5ede8eccf3b
MD5 ff3df877d2883e1be9351dd23a9883e7
BLAKE2b-256 318d88723337b4a0d0c6a76b360d1b3813d9d9694267301458844fe156940b03

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ms2pip-4.0.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8217e490d4e693b23b93c371651ef8e01fdb2d23bdf454c9b34d8dc7bb56a5ad
MD5 076061c566144e8db22c884c6728ac4c
BLAKE2b-256 ff8d905f191fecc5b103a5cccf23244cb56ebca12512b1381e2087d7c750e8e5

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ms2pip-4.0.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 d68dd95e5f62d0f259459247339bd52a3680ceb2d1b4dff676cc5bf99bc659b9
MD5 d783fc23fcb5763abafdf4e21c2ca307
BLAKE2b-256 9c8000b20a84466f2cfbcb2a5ca2c63d2929abbcad8f7c44b938d7035a8e0083

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for ms2pip-4.0.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 f9ce440d697dbde8a5720f515ef0f9bb60be055412951dcf6eb5ecdecd7b6e38
MD5 5e3f3f2dd4aafb801863b1526883fff2
BLAKE2b-256 9877f1db9eaf792d665d2fa77858bba8476af84529a9dfa10a88591719981ffc

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: ms2pip-4.0.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 13.6 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.8.10

File hashes

Hashes for ms2pip-4.0.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 a4c75fadb65375cc0040ff13a3a68b654c4db17c0933f8e0eae430437d79680d
MD5 2874ab6df620929f1ec6cf6c0e5df812
BLAKE2b-256 840509fc63e68c77bbc0f095e17d74dd3855acacd5515a6a816243bab065d251

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ms2pip-4.0.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 59e4c55c053fb6e9e4ed786915cd91fc9e1a1d06643abe5ab369f65d3dcdab56
MD5 ea7a0d043942feabe7630abb94c72814
BLAKE2b-256 24506b1ab485107464b50587ddb995e418d70084b6bbd9308813a7c3d903b1ef

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ms2pip-4.0.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 0b6e78426e51accb0b896c7966fb6d4872ff8c4d3dc17e59ed88e72ecec2a9ce
MD5 a2036a8b6f3ae6f5d654b8ac363f64ed
BLAKE2b-256 c30ffe1c4d60d54d402935bcbfd0882ecd1cd87f534ade9d121a860126032a67

See more details on using hashes here.

File details

Details for the file ms2pip-4.0.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for ms2pip-4.0.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 4f6ea79266d98d16f8a9635886338eb3f2a4742db64302d791e39c73dc681f0d
MD5 26afd092b207599a936514fd10b504c4
BLAKE2b-256 82568b0d3bed5d2c73d20ed4cb45403cd00fc1e657fe4e905e5286446d18a6a8

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