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MS²PIP: MS2 Peak Intensity Prediction - Fast and accurate peptide fragmentation spectrum prediction for multiple fragmentation methods, instruments and labeling techniques.
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 <https://xgboost.readthedocs.io/en/stable/>
_ 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 MS²PIP-predicted (bottom) spectrum for the peptide
DVAQIFNNILR/2
.
You can install MS²PIP on your machine by following the
installation instructions <https://ms2pip.readthedocs.io/installation/>
. For a more
user-friendly experience, go to the MS²PIP web server <https://iomics.ugent.be/ms2pip>
. 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 <https://iomics.ugent.be/ms2pip/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 <https://ms2pip.readthedocs.io/en/latest/usage>
_ for more information.
MS²Rescore <https://github.com/compomics/ms2rescore/>
_: Use MS²PIP and other peptide prediction
tools to boost peptide identification results.DeepLC <https://github.com/compomics/deeplc/>
_: Retention time prediction for (modified)
peptides using deep learning.IM2Deep <https://github.com/compomics/im2deep>
_: Ion mobility prediction for (modified)
peptides using deep learning.psm_utils <https://github.com/compomics/psm_utils/>
_: Common utilities for parsing and handling
peptide-spectrum matches and search engine results in PythonIf you use MS²PIP for your research, please cite the following publication:
Nucleic Acids Research
doi:10.1093/nar/gkad335 <https://doi.org/10.1093/nar/gkad335>
_Prior MS²PIP publications:
Nucleic Acids Research
doi:10.1093/nar/gkz299 <https://doi.org/10.1093/nar/gkz299>
__Nucleic Acids Research
, 43(W1), W326–W330.
doi:10.1093/nar/gkv542 <https://doi.org/10.1093/nar/gkv542>
_Bioinformatics (Oxford, England)
, 29(24), 3199–203.
doi:10.1093/bioinformatics/btt544 <https://doi.org/10.1093/bioinformatics/btt544>
_Please also take note of, and mention, the MS²PIP version you used.
The full documentation, including installation instructions, usage examples,
and the command-line and Python API reference, can be found at
ms2pip.readthedocs.io <https://ms2pip.readthedocs.io>
_.
Bugs, questions or suggestions? Feel free to post an issue in the
issue tracker <https://github.com/compomics/ms2pip/issues/>
_ or to make a pull
request. Any contribution, small or large, is welcome!