<img src="https://github.com/compomics/ms2rescore/raw/main/img/ms2rescore_logo.png" width="150" height="150" alt="MS²Rescore"/>
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Modular and user-friendly platform for AI-assisted rescoring of peptide identifications
> ⚠️ Note: This is the documentation for the fully redeveloped version 3.0 of MS²Rescore. While
> MS²Rescore 3.0 has been drastically improved over the previous version, you might run into some
> unforeseen issues. Please report any issues you encounter on the [issue tracker][issues] or post
> your questions on the [GitHub Discussions][discussions] forum.
## About MS²Rescore
MS²Rescore performs ultra-sensitive peptide identification rescoring with LC-MS predictors such as
[MS²PIP][ms2pip] and [DeepLC][deeplc], and with ML-driven rescoring engines
[Percolator][percolator] or [Mokapot][mokapot]. This results in more confident peptide
identifications, which allows you to get **more peptide IDs** at the same false discovery rate
(FDR) threshold, or to set a **more stringent FDR threshold** while still retaining a similar
number of peptide IDs. MS²Rescore is **ideal for challenging proteomics identification workflows**,
such as proteogenomics, metaproteomics, or immunopeptidomics.
MS²Rescore can read peptide identifications in any format supported by [psm_utils][psm_utils]
(see [Supported file formats][file-formats]) and has been tested with various search engines output
files:
- [MS Amanda](http://ms.imp.ac.at/?goto=msamanda) `.csv`
- [Sage](https://github.com/lazear/sage) `.sage.tsv`
- [PeptideShaker](https://compomics.github.io/projects/peptide-shaker.html) `.mzid`
- [ProteomeDiscoverer](#)`.msf`
- [MSGFPlus](https://omics.pnl.gov/software/ms-gf) `.mzid`
- [Mascot](https://www.matrixscience.com/) `.mzid`
- [MaxQuant](https://www.maxquant.org/) `msms.txt`
- [X!Tandem](https://www.thegpm.org/tandem/) `.xml`
- [PEAKS](https://www.bioinfor.com/peaksdb/) `.mzid`
MS²Rescore is available as a [desktop application][desktop], a [command line tool][cli], and a
[modular Python API][python-package].
## Citing
**Latest MS²Rescore publication:**
> **MS²Rescore 3.0 is a modular, flexible, and user-friendly platform to boost peptide identifications, as showcased with MS Amanda 3.0.**
> Louise Marie Buur*, Arthur Declercq*, Marina Strobl, Robbin Bouwmeester, Sven Degroeve, Lennart Martens, Viktoria Dorfer*, and Ralf Gabriels*.
> _Journal of Proteome Research_ (2024) [doi:10.1021/acs.jproteome.3c00785](https://doi.org/10.1021/acs.jproteome.3c00785) <br/> \*contributed equally <span class="__dimensions_badge_embed__" data-doi="10.1021/acs.jproteome.3c00785" data-hide-zero-citations="true" data-style="small_rectangle"></span>
**MS²Rescore for immunopeptidomics:**
> **MS2Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates.**
> Arthur Declercq, Robbin Bouwmeester, Aurélie Hirschler, Christine Carapito, Sven Degroeve, Lennart Martens, and Ralf Gabriels.
> _Molecular & Cellular Proteomics_ (2021) [doi:10.1016/j.mcpro.2022.100266](https://doi.org/10.1016/j.mcpro.2022.100266) <span class="__dimensions_badge_embed__" data-doi="10.1016/j.mcpro.2022.100266" data-hide-zero-citations="true" data-style="small_rectangle"></span>
**Original publication describing the concept of rescoring with predicted spectra:**
> **Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions.**
> Ana S C Silva, Robbin Bouwmeester, Lennart Martens, and Sven Degroeve.
> _Bioinformatics_ (2019) [doi:10.1093/bioinformatics/btz383](https://doi.org/10.1093/bioinformatics/btz383) <span class="__dimensions_badge_embed__" data-doi="10.1093/bioinformatics/btz383" data-hide-zero-citations="true" data-style="small_rectangle"></span>
To replicate the experiments described in this article, check out the
[publication branch][publication-branch] of the repository.
## Getting started
The desktop application can be installed on Windows with a [one-click installer][desktop-installer].
The Python package and command line interface can be installed with `pip`, `conda`, or `docker`.
Check out the [full documentation][docs] to get started.
## Questions or issues?
Have questions on how to apply MS²Rescore on your data? Or ran into issues while using MS²Rescore?
Post your questions on the [GitHub Discussions][discussions] forum and we are happy to help!
## How to contribute
Bugs, questions or suggestions? Feel free to post an issue in the [issue tracker][issues] or to
make a [pull request][pr]!
[docs]: https://ms2rescore.readthedocs.io/
[issues]: https://github.com/compomics/ms2rescore/issues/
[discussions]: https://github.com/compomics/ms2rescore/discussions/
[pr]: https://github.com/compomics/ms2rescore/pulls/
[desktop]: https://ms2rescore.readthedocs.io/gui.html
[desktop-installer]: https://github.com/compomics/ms2rescore/releases/latest
[cli]: https://ms2rescore.readthedocs.io/cli/cli.html
[python-package]: https://ms2rescore.readthedocs.io/api/ms2rescore.html
[docker]: https://ms2rescore.readthedocs.io/installation.html#docker-container
[publication-branch]: https://github.com/compomics/ms2rescore/tree/pub
[ms2pip]: https://github.com/compomics/ms2pip
[deeplc]: https://github.com/compomics/deeplc
[percolator]: https://github.com/percolator/percolator/
[mokapot]: https://mokapot.readthedocs.io/
[psm_utils]: https://github.com/compomics/psm_utils
[file-formats]: https://psm-utils.readthedocs.io/en/stable/#supported-file-formats
Raw data
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"description": "<img src=\"https://github.com/compomics/ms2rescore/raw/main/img/ms2rescore_logo.png\" width=\"150\" height=\"150\" alt=\"MS\u00b2Rescore\"/>\n<br/><br/>\n\n[](https://github.com/compomics/ms2rescore/releases)\n[](https://pypi.org/project/ms2rescore/)\n[](https://github.com/compomics/ms2rescore/actions/)\n[](https://github.com/compomics/ms2rescore/issues)\n[](https://www.apache.org/licenses/LICENSE-2.0)\n[](https://github.com/compomics/ms2rescore/commits/)\n\nModular and user-friendly platform for AI-assisted rescoring of peptide identifications\n\n> \u26a0\ufe0f Note: This is the documentation for the fully redeveloped version 3.0 of MS\u00b2Rescore. While\n> MS\u00b2Rescore 3.0 has been drastically improved over the previous version, you might run into some\n> unforeseen issues. Please report any issues you encounter on the [issue tracker][issues] or post\n> your questions on the [GitHub Discussions][discussions] forum.\n\n## About MS\u00b2Rescore\n\nMS\u00b2Rescore performs ultra-sensitive peptide identification rescoring with LC-MS predictors such as\n[MS\u00b2PIP][ms2pip] and [DeepLC][deeplc], and with ML-driven rescoring engines\n[Percolator][percolator] or [Mokapot][mokapot]. This results in more confident peptide\nidentifications, which allows you to get **more peptide IDs** at the same false discovery rate\n(FDR) threshold, or to set a **more stringent FDR threshold** while still retaining a similar\nnumber of peptide IDs. MS\u00b2Rescore is **ideal for challenging proteomics identification workflows**,\nsuch as proteogenomics, metaproteomics, or immunopeptidomics.\n\n\n\nMS\u00b2Rescore can read peptide identifications in any format supported by [psm_utils][psm_utils]\n(see [Supported file formats][file-formats]) and has been tested with various search engines output\nfiles:\n\n- [MS Amanda](http://ms.imp.ac.at/?goto=msamanda) `.csv`\n- [Sage](https://github.com/lazear/sage) `.sage.tsv`\n- [PeptideShaker](https://compomics.github.io/projects/peptide-shaker.html) `.mzid`\n- [ProteomeDiscoverer](#)`.msf`\n- [MSGFPlus](https://omics.pnl.gov/software/ms-gf) `.mzid`\n- [Mascot](https://www.matrixscience.com/) `.mzid`\n- [MaxQuant](https://www.maxquant.org/) `msms.txt`\n- [X!Tandem](https://www.thegpm.org/tandem/) `.xml`\n- [PEAKS](https://www.bioinfor.com/peaksdb/) `.mzid`\n\nMS\u00b2Rescore is available as a [desktop application][desktop], a [command line tool][cli], and a\n[modular Python API][python-package].\n\n## Citing\n\n**Latest MS\u00b2Rescore publication:**\n\n> **MS\u00b2Rescore 3.0 is a modular, flexible, and user-friendly platform to boost peptide identifications, as showcased with MS Amanda 3.0.**\n> Louise Marie Buur*, Arthur Declercq*, Marina Strobl, Robbin Bouwmeester, Sven Degroeve, Lennart Martens, Viktoria Dorfer*, and Ralf Gabriels*.\n> _Journal of Proteome Research_ (2024) [doi:10.1021/acs.jproteome.3c00785](https://doi.org/10.1021/acs.jproteome.3c00785) <br/> \\*contributed equally <span class=\"__dimensions_badge_embed__\" data-doi=\"10.1021/acs.jproteome.3c00785\" data-hide-zero-citations=\"true\" data-style=\"small_rectangle\"></span>\n\n**MS\u00b2Rescore for immunopeptidomics:**\n\n> **MS2Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates.**\n> Arthur Declercq, Robbin Bouwmeester, Aur\u00e9lie Hirschler, Christine Carapito, Sven Degroeve, Lennart Martens, and Ralf Gabriels.\n> _Molecular & Cellular Proteomics_ (2021) [doi:10.1016/j.mcpro.2022.100266](https://doi.org/10.1016/j.mcpro.2022.100266) <span class=\"__dimensions_badge_embed__\" data-doi=\"10.1016/j.mcpro.2022.100266\" data-hide-zero-citations=\"true\" data-style=\"small_rectangle\"></span>\n\n**Original publication describing the concept of rescoring with predicted spectra:**\n\n> **Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions.**\n> Ana S C Silva, Robbin Bouwmeester, Lennart Martens, and Sven Degroeve.\n> _Bioinformatics_ (2019) [doi:10.1093/bioinformatics/btz383](https://doi.org/10.1093/bioinformatics/btz383) <span class=\"__dimensions_badge_embed__\" data-doi=\"10.1093/bioinformatics/btz383\" data-hide-zero-citations=\"true\" data-style=\"small_rectangle\"></span>\n\nTo replicate the experiments described in this article, check out the\n[publication branch][publication-branch] of the repository.\n\n## Getting started\n\nThe desktop application can be installed on Windows with a [one-click installer][desktop-installer].\nThe Python package and command line interface can be installed with `pip`, `conda`, or `docker`.\nCheck out the [full documentation][docs] to get started.\n\n## Questions or issues?\n\nHave questions on how to apply MS\u00b2Rescore on your data? Or ran into issues while using MS\u00b2Rescore?\nPost your questions on the [GitHub Discussions][discussions] forum and we are happy to help!\n\n## How to contribute\n\nBugs, questions or suggestions? 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