ms2pip

MS²PIP: Fast and accurate peptide spectrum prediction for multiple fragmentation methods, instruments, and labeling techniques.

https://github.com/compomics/ms2pip

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MS²PIP: Fast and accurate peptide spectrum prediction for multiple fragmentation methods, instruments, and labeling techniques.

README.rst

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

---------------------------------------------------------------------------------------------------

About
-----

MSPIP 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. MSPIP employs the
`XGBoost `_ machine learning algorithm and is written in
Python and C.

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

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

You can install MSPIP on your machine by following the
`installation instructions `_. For a more
user-friendly experience, go to the `MSPIP 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 MSPIP 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.
- ``correlate-single``: Compare predicted and observed intensities for a single peptide spectrum.
- ``get-training-data``: Extract feature vectors and target intensities from observed spectra for
  training.
- ``annotate-spectra``: Annotate peaks in observed spectra.

MSPIP 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.

Related projects
----------------

- `MSRescore `_: Use MSPIP and other peptide prediction
  tools to boost peptide identification results.
- `DeepLC `_: Retention time prediction for (modified)
  peptides using deep learning.
- `IM2Deep `_: Ion mobility prediction for (modified)
  peptides using deep learning.
- `psm_utils `_: Common utilities for parsing and handling
  peptide-spectrum matches and search engine results in Python

Citations
---------

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

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

Prior MSPIP publications:

- Gabriels, R., Martens, L., & Degroeve, S. (2019). Updated MSPIP 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). MSPIP prediction server:
  compute and visualize MS2 peak intensity predictions for CID and HCD
  fragmentation. `_Nucleic Acids Research`, 43(W1), W326W330.
  `doi:10.1093/nar/gkv542 `_
- Degroeve, S., & Martens, L. (2013). MSPIP: a tool for MS/MS peak intensity
  prediction. `Bioinformatics (Oxford, England)`, 29(24), 3199203.
  `doi:10.1093/bioinformatics/btt544 `_

Please also take note of, and mention, the MSPIP 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!

Owner

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: "Gabriels"
    given-names: "Ralf"
    orcid: "https://orcid.org/0000-0002-1679-1711"
  - family-names: "Velghe"
    given-names: "Kevin"
    orcid: "https://orcid.org/0000-0002-9968-6043"
  - family-names: "Martens"
    given-names: "Lennart"
    orcid: "https://orcid.org/0000-0003-4277-658X"
  - family-names: "Degroeve"
    given-names: "Sven"
    orcid: "https://orcid.org/0000-0001-8349-3370"
title: "MS²PIP"
url: "https://github.com/compomics/ms2pip"
license: "Apache-2.0"
preferred-citation:
  type: article
  doi: "10.1093/nar/gkad335"
  journal: "Nucleic Acids Research"
  title: "Updated MS²PIP web server supports cutting-edge proteomics applications"
  year: 2023
  abstract: "MS²PIP is a data-driven tool that accurately predicts peak intensities for a given peptide's fragmentation mass spectrum. Since the release of the MS²PIP web server in 2015, we have brought significant updates to both the tool and the web server. In addition to the original models for CID and HCD fragmentation, we have added specialized models for the TripleTOF 5600+ mass spectrometer, for TMT-labeled peptides, for iTRAQ-labeled peptides, and for iTRAQ-labeled phosphopeptides. Because the fragmentation pattern is heavily altered in each of these cases, these additional models greatly improve the prediction accuracy for their corresponding data types. We have also substantially reduced the computational resources required to run MS²PIP, and have completely rebuilt the web server, which now allows predictions of up to 100 000 peptide sequences in a single request. The MS²PIP web server is freely available at https://iomics.ugent.be/ms2pip/."
  authors:
    - family-names: "Declercq"
      given-names: "Arthur"
      orcid: "https://orcid.org/0000-0002-9376-1399"
    - family-names: "Bouwmeester"
      given-names: "Robbin"
      orcid: "https://orcid.org/0000-0001-6807-7029"
    - family-names: "Cristina"
      given-names: "Chiva"
      orcid: "https://orcid.org/0000-0001-8150-6203"
    - family-names: "Sabidó"
      given-names: "Eduard"
      orcid: "https://orcid.org/0000-0001-6506-7714"
    - family-names: "Hirschler"
      given-names: "Aurélie"
      orcid: "https://orcid.org/0000-0001-5066-6263"
    - family-names: "Carapito"
      given-names: "Christine"
      orcid: "https://orcid.org/0000-0002-0079-319X"
    - family-names: "Martens"
      given-names: "Lennart"
      orcid: "https://orcid.org/0000-0003-4277-658X"
    - family-names: "Degroeve"
      given-names: "Sven"
      orcid: "https://orcid.org/0000-0001-8349-3370"
    - family-names: "Gabriels"
      given-names: "Ralf"
      orcid: "https://orcid.org/0000-0002-1679-1711"

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