Metadata-Version: 2.1
Name: ms2rescore
Version: 2.1.1
Summary: MS²Rescore: Sensitive PSM rescoring with predicted MS² peak intensities and retention times.
Home-page: https://compomics.github.io/projects/ms2rescore/
Author: Ralf Gabriels, Arthur Declercq, Ana Sílvia C. Silva, Tim Van Den Bossche
Author-email: compomics.list@gmail.com
License: apache-2.0
Project-URL: Documentation, http://compomics.github.io/projects/ms2rescore
Project-URL: Source, https://github.com/compomics/ms2rescore
Project-URL: Tracker, https://github.com/compomics/ms2rescore/issues
Project-URL: Publication, https://doi.org/10.1093/bioinformatics/btz383
Description: <img src="https://github.com/compomics/ms2rescore/raw/master/img/ms2rescore_logo.png" width="150" height="150" />
        <br/><br/>
        
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        Sensitive peptide identification rescoring with predicted spectra using
        [MS²PIP](https://github.com/compomics/ms2pip_c),
        [DeepLC](https://github.com/compomics/deeplc), and
        [Percolator](https://github.com/percolator/percolator/).
        
        ---
        
        - [About MS²Rescore](#about-msrescore)
        - [Installation](#installation)
          - [Python package](#python-package)
          - [Windows installer](#windows-installer)
        - [Usage](#usage)
          - [GUI](#gui)
          - [Command line interface](#command-line-interface)
          - [Configuration file](#configuration-file)
          - [Notes for specific search engines](#notes-for-specific-search-engines)
          - [Output](#output)
        - [Contributing](#contributing)
        
        ---
        
        ## About MS²Rescore
        
        MS²Rescore performs sensitive peptide identification rescoring with predicted
        spectra using [MS²PIP](https://github.com/compomics/ms2pip_c),
        [DeepLC](https://github.com/compomics/deeplc), and
        [Percolator](https://github.com/percolator/percolator/). 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 uses identifications from a
        [Percolator IN (PIN) file](https://github.com/percolator/percolator/wiki/Interface#tab-delimited-file-format),
        or from the output of one of these search engines:
        
        - [MaxQuant](https://www.maxquant.org/): Start from `msms.txt` identification
          file and directory with `.mgf` files.
        - [MSGFPlus](https://omics.pnl.gov/software/ms-gf): Start with an `.mzid`
          identification file and corresponding `.mgf`.
        - [X!Tandem](https://www.thegpm.org/tandem/): Start with an X!Tandem `.xml`
          identification file and corresponding `.mgf`.
        - [PEAKS](https://www.bioinfor.com/peaksdb/): Start with an `.mzid` identification
          file and directory with `.mgf` files.
        - [PeptideShaker](http://compomics.github.io/projects/peptide-shaker): Start with a
          PeptideShaker Extended PSM Report and corresponding `.mgf` file.
        
        <br>
        
        If you use MS²Rescore, please cite the following article:
        > **MS2Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates.**  \
        Arthur Declercq, Robbin Bouwmeester, Sven Degroeve, Lennart Martens, and Ralf Gabriels.  \
        _bioRxiv_ (2021) [doi:10.1101/2021.11.02.466886](https://doi.org/10.1101/2021.11.02.466886)
        
        <br>
        
        The concept of rescoring with predicted spectrum features was first described in:
        
        > **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)
        
        To replicate the experiments described in this article, check out the
        [pub branch](https://github.com/compomics/ms2rescore/tree/pub) of this repository.
        
        ---
        
        ## Installation
        
        ### Python package
        
        [![install pip](https://flat.badgen.net/badge/install%20with/pip/green?icon=pypi)](https://pypi.org/project/ms2rescore/)
        
        
        MS²Rescore requires:
        - Python 3.7 or 3.8 on Linux, macOS, or Windows
        - If the option `run_percolator` is set to `True`,
        [Percolator](https://github.com/percolator/percolator/) needs to be installed and
        callable with the `percolator` command (tested with v3.02.1)
        - Some pipelines require the Percolator converters, such as `tandem2pin`, as well. These
        are usually installed alongside Percolator.
        
        Minimal installation:
        ```sh
        pip install ms2rescore
        ```
        
        Installation including dependencies for the graphical user interface:
        ```sh
        pip install ms2rescore[gui]
        ```
        
        We highly recommend using a [venv](https://docs.python.org/3/library/venv.html) or
        [conda](https://docs.conda.io/en/latest/) virtual environment.
        
        
        ### Windows installer
        [![get for windows](https://flat.badgen.net/badge/install%20for/windows/blue?icon=windows)](https://github.com/compomics/ms2rescore/releases)
        
        1. Download and install [Percolator](https://github.com/percolator/percolator/releases/latest)
        and the percolator-converters. Make sure to select "Add percolator to the system PATH
        for all users" during setup.
        2. Download the zip file from the [latest release](https://github.com/compomics/ms2rescore/releases)
        and unzip.
        3. Run `install-gui-windows.bat` to install Python and MS²Rescore.
        4. Run `start-gui-windows.bat` to start the MS²Rescore GUI.
        
        If Microsoft Defender SmartScreen displays a warning, click "More info" and then click
        "Run anyway". When starting the GUI, don't mind the terminal window that opens next
        to the GUI.
        
        To install a newer version of MS²Rescore, run `upgrade-gui-windows.bat`.
        
        ---
        
        ## Usage
        
        ### GUI
        
        Run `start-gui-windows.bat` or run `python -m ms2rescore.gui` in your terminal to start
        the graphical user interface. Most common settings can be configured through the UI.
        For some advanced settings, see [Configuration file](#configuration-file).
        
        <img src="img/gui-screenshot.png" height=480 />
        
        
        ### Command line interface
        
        Run MS²Rescore from the command line as follows:
        
        ```
        ms2rescore -c <path-to-config-file> -m <path-to-mgf> <path-to-identification-file>
        ```
        
        Run `ms2rescore --help` to see all command line options.
        
        ### Configuration file
        
        Although most options can be configered through the CLI and the GUI, MS²Rescore can be
        further configured through a **JSON configuration file**. A correct configuration is
        required to, for example, correctly parse the peptide modifications from the search
        engine output. If no configuration file is passed, or some options are not configured,
        the
        [default values](https://github.com/compomics/ms2rescore/blob/master/ms2rescore/package_data/config_default.json)
        for these settings will be used. Options passed from the CLI and the GUI will override
        the configuration file. The full configuration is validated against a
        [JSON Schema](https://github.com/compomics/ms2rescore/blob/master/ms2rescore/package_data/config_schema.json).
        
        A full example configuration file can be found in
        [ms2rescore/package_data/config_default.json](https://github.com/compomics/ms2rescore/blob/master/ms2rescore/package_data/config_default.json).
        
        The config file contains three top level categories (`general`, `ms2pip` and
        `percolator`) and an additional categories for specific search engines
        (e.g. `maxquant`). The most important options in `general` are:
        - **`pipeline`** *(string)*: Pipeline to use, depending on input format. Must be one of:
        `['infer', 'pin', 'tandem', 'maxquant', 'msgfplus', 'peptideshaker']`. Default: `infer`.
        - **`feature_sets`** *(array)*: Feature sets for which to generate PIN files and optionally run Percolator. Default: `['searchengine', 'rt', 'ms2pip']`.
            - **Items** *(array)*
              - **Items** *(string)*: Must be one of: `['searchengine', 'rt', 'ms2pip']`.
        
        An overview of all options can be found in [configuration.md](https://github.com/compomics/ms2rescore/blob/master/configuration.md)
        
        ### Notes for specific search engines
        
        - **MSGFPlus:** Run MSGFPlus in a concatenated target-decoy search, with the
        `-addFeatures 1` flag.
        - **MaxQuant:**
          - Run MaxQuant without FDR filtering (set to 1)
          - MaxQuant requires additional options in the configuration file:
            - `modification_mapping`: Maps MaxQuant output to MS²PIP modifications list.
        Keys must contain MaxQuant's two-letter modification codes and values must match
        one of the modifications listed in the MS²PIP configuration (see
        [MS2PIP config](#MS2PIP)).
            - `fixed_modifications`: Must list all modifications set as fixed during the
        MaxQuant search (as this is not denoted in the msms.txt file). Keys refer to the
        amino acid, values to the modification name used in the MS²PIP configuration.
            - The maxquant specific configuration could for example be:
              ```json
              "maxquant_to_rescore": {
                "modification_mapping":{
                  "ox":"Oxidation",
                  "cm":"Carbamidomethyl"
                },
                "fixed_modifications":{
                  "C":"Carbamidomethyl"
                }
              ```
        
        As a general rule, MS²Rescore always needs access to all target and decoy PSMs, not
        only the FDR-filtered targets.
        
        
        ### Output
        Several intermediate files are created when the entire pipeline is run. These can be
        accessed by specifying the `tmp_dir` or `Temporary file directory` option. Depending on whether or not Percolator is
        run, the following output files can be expected:
        
        For each feature set combination (e.g. [`rt`, `ms2pip`, `searchengine`]):
        - `<file>.pin` Percolator IN file
        - `<file>.pout` Percolator OUT file with target PSMs
        - `<file>.pout_dec` Percolator OUT file with decoy PSMs
        - `<file>.weights` Internal feature weights used by Percolator's scoring function.
        
        ---
        
        ## Contributing
        Bugs, questions or suggestions? Feel free to post an issue in the
        [issue tracker](https://github.com/compomics/ms2rescore/issues/) or to make a pull
        request!
        
Keywords: MS2Rescore,MS2PIP,DeepLC,Percolator,Proteomics,peptide,peak intensity prediction,spectrum,machine learning
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Development Status :: 4 - Beta
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: gui
