Merge pull request #264 from CompOmics/percolator-removal

Percolator removal

Author: ArthurDeclercq

README.md

@@ -13,8 +13,8 @@ Modular and user-friendly platform for AI-assisted rescoring of peptide identifi
 ## 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
+[MS²PIP][ms2pip] and [DeepLC][deeplc], using ML-driven rescoring engines
+like [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**,
@@ -106,7 +106,6 @@ make a [pull request][pr]!
 [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

docs/source/_static/img/percolator-install-path.png

@@ -12,11 +12,3 @@ Mokapot
 
 .. automodule:: ms2rescore.rescoring_engines.mokapot
    :members:
-
-
-
-Percolator
-##########
-
-.. automodule:: ms2rescore.rescoring_engines.percolator
-   :members:

docs/source/api/ms2rescore.rescoring_engines.rst

@@ -14,7 +14,6 @@
     - **`ms2`**: Refer to *[#/definitions/ms2](#definitions/ms2)*.
   - **`rescoring_engine`** *(object)*: Rescoring engine to use and its configuration. Leave empty to skip rescoring and write features to file. Default: `{"mokapot": {}}`.
     - **`.*`**: Refer to *[#/definitions/rescoring_engine](#definitions/rescoring_engine)*.
-    - **`percolator`**: Refer to *[#/definitions/percolator](#definitions/percolator)*.
     - **`mokapot`**: Refer to *[#/definitions/mokapot](#definitions/mokapot)*.
   - **`psm_generator`** *(object)*: PSM generator and their configuration.
     - **`.*`**: Refer to *[#/definitions/psm_generator](#definitions/psm_generator)*.
@@ -124,8 +123,3 @@
   - **`train_fdr`** *(number)*: FDR threshold for training Mokapot. Minimum: `0`. Maximum: `1`. Default: `0.01`.
   - **`write_weights`** *(boolean)*: Write Mokapot weights to a text file. Default: `false`.
   - **`write_txt`** *(boolean)*: Write Mokapot results to a text file. Default: `false`.
-- <a id="definitions/percolator"></a>**`percolator`** *(object)*: Percolator rescoring engine configuration. Can contain additional properties. Refer to *[#/definitions/rescoring_engine](#definitions/rescoring_engine)*.
-  - **`init-weights`**: Weights file for scoring function. Default: `false`.
-    - **One of**
-      - *string*
-      - *null*

docs/source/config_schema.md

@@ -60,23 +60,6 @@ files, ``<tag>`` is the container version tag, and ``<ms2rescore-arguments>`` ar
 command line options (see :ref:`Command line interface`).
 
 
-Installing Percolator
-=====================
-
-To use :ref:`percolator` as rescoring engine, it must be installed separately. Percolator is
-available for most platforms and can be downloaded from the
-`GitHub releases page <https://github.com/percolator/percolator/releases/latest>`_. Ensure that
-the ``percolator`` executable is in your ``PATH``. On Windows, this can be done by checking the
-``Add percolator to the system PATH for current user`` option during installation:
-
-.. figure:: ../_static/img/percolator-install-path.png
-   :width: 60%
-   :alt: Percolator installation on Windows
-
-.. note::
-   Alternatively, :ref:`mokapot` can be used as rescoring engine, which does not require a separate
-   installation.
-
 For development
 ===============
 

docs/source/installation.rst

@@ -5103,14 +5103,36 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### Parsing modification names\n\nWhile `psm_utils` could take care of all file parsing, we must still map the amino acid modification names that were used by the search engine to ones that are recognized by tools such as MS²PiP and DeepLC. This includes:\n\n- Names as used in the Unimod or PSI-MOD databases\n- Accession numbers as used in the Unimod or PSI-MOD databases\n- Chemical formulas\n- Mass shifts in Da\n\nNote that, for instance DeepLC, requires a chemical formula to encode modifications. It will not be able to correctly encode modifications if only the mass shift is provided. It is therefore always preferred to provide a name/accession of a database where this information can be retrieved, or provide the chemical formula directly.\n\nIf a chemical formula is provided, other tools, such as MS²PIP, can use it to derive the correct mass shift.\n\nTo map modification names, simply provide a dictionary to the `psm_list.map_modifications` method."
+   "source": [
+    "### Parsing modification names\n",
+    "\n",
+    "While `psm_utils` could take care of all file parsing, we must still map the amino acid modification names that were used by the search engine to ones that are recognized by tools such as MS²PiP and DeepLC. This includes:\n",
+    "\n",
+    "- Names as used in the Unimod or PSI-MOD databases\n",
+    "- Accession numbers as used in the Unimod or PSI-MOD databases\n",
+    "- Chemical formulas\n",
+    "- Mass shifts in Da\n",
+    "\n",
+    "Note that, for instance DeepLC, requires a chemical formula to encode modifications. It will not be able to correctly encode modifications if only the mass shift is provided. It is therefore always preferred to provide a name/accession of a database where this information can be retrieved, or provide the chemical formula directly.\n",
+    "\n",
+    "If a chemical formula is provided, other tools, such as MS²PIP, can use it to derive the correct mass shift.\n",
+    "\n",
+    "To map modification names, simply provide a dictionary to the `psm_list.map_modifications` method."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "psm_list.rename_modifications({\n    \"gl\": \"Gln->pyro-Glu\",\n    \"ox\": \"Oxidation\",\n    \"ac\": \"Acetylation\",\n    \"de\": \"Deamidation\",\n})"
+   "source": [
+    "psm_list.rename_modifications({\n",
+    "    \"gl\": \"Gln->pyro-Glu\",\n",
+    "    \"ox\": \"Oxidation\",\n",
+    "    \"ac\": \"Acetylation\",\n",
+    "    \"de\": \"Deamidation\",\n",
+    "})"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -5785,7 +5807,15 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "#### Configuring MS²PIP\n\nIn contrast to the basic feature generator, MS²PIP requires some parameters to be set and requires access to the original observed peptide spectra:\n\n- `model`: Name of the prediction model to be used. This strongly depends on the dataset your are rescoring. A list of all MS²PIP models is available on https://ms2pip.readthedocs.io/en/latest/prediction-models/. \n- `ms2_tolerance`: As MS²PIP must reannotate the observed MS2 spectra, a mass tolerance must be set. For MS²PIP, this is configured in Dalton. A good value for Orbitrap spectra, for example, is `0.02`.\n- `processes`: Number of CPU processes to use for parallel processing. Note that higher values can lead to memory issues."
+   "source": [
+    "#### Configuring MS²PIP\n",
+    "\n",
+    "In contrast to the basic feature generator, MS²PIP requires some parameters to be set and requires access to the original observed peptide spectra:\n",
+    "\n",
+    "- `model`: Name of the prediction model to be used. This strongly depends on the dataset your are rescoring. A list of all MS²PIP models is available on https://ms2pip.readthedocs.io/en/latest/prediction-models/. \n",
+    "- `ms2_tolerance`: As MS²PIP must reannotate the observed MS2 spectra, a mass tolerance must be set. For MS²PIP, this is configured in Dalton. A good value for Orbitrap spectra, for example, is `0.02`.\n",
+    "- `processes`: Number of CPU processes to use for parallel processing. Note that higher values can lead to memory issues."
+   ]
   },
   {
    "cell_type": "code",
@@ -74922,7 +74952,7 @@
     "Using mokapot directly allows us to quickly generate `LinearPsmDataset` objects with different feature sets, for instance:\n",
     "\n",
     "- Only basic features\n",
-    "- Basic + MaxQuant-derived features (mimicking a traditional Percolator rescoring run)\n",
+    "- Basic + MaxQuant-derived features\n",
     "- Basic + MaxQuant-derived + MS²PIP features\n",
     "- Basic + MaxQuant-derived + MS²PIP + DeepLC features\n"
    ]
@@ -111537,4 +111567,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

docs/source/tutorials/in-depth-python-api.ipynb

@@ -287,9 +287,9 @@ the following configuration can be used:
 Configuring rescoring engines
 =============================
 
-MS²Rescore supports multiple rescoring engines, such as Mokapot and Percolator. The rescoring
-engine can be selected and configured with the ``rescoring_engine`` option. For example, to use
-Mokapot with a custom train_fdr of 0.1%, the following configuration can be used:
+MS²Rescore uses Mokapot as its rescoring engine. It can be configured with the
+``rescoring_engine`` option. For example, to use Mokapot with a custom train_fdr of 0.1%, the
+following configuration can be used:
 
 .. tab:: JSON
 

docs/source/userguide/configuration.rst

@@ -38,26 +38,25 @@ Log and configuration files:
 
 Rescoring engine files:
 
-+-------------------------------------------------------------+-------------------------------------------------------------+
-| File                                                        | Description                                                 |
-+=============================================================+=============================================================+
-| ``<prefix>.<mokapot/percolator>.psms.txt``                  | PSMs and their new scores at PSM-level FDR.                 |
-+-------------------------------------------------------------+-------------------------------------------------------------+
-| ``<prefix>.<mokapot/percolator>.peptides.txt``              | Peptides and their new scores at peptide-level FDR.         |
-+-------------------------------------------------------------+-------------------------------------------------------------+
-| ``<prefix>.<mokapot/percolator>.proteins.txt``              | Proteins and their new scores at protein-level FDR.         |
-+-------------------------------------------------------------+-------------------------------------------------------------+
-| ``<prefix>.<mokapot/percolator>.decoy.psms.txt``            | Decoy PSMs and their new scores at PSM-level FDR.           |
-+-------------------------------------------------------------+-------------------------------------------------------------+
-| ``<prefix>.<mokapot/percolator>.decoy.peptides.txt``        | Decoy peptides and their new scores at peptide-level FDR.   |
-+-------------------------------------------------------------+-------------------------------------------------------------+
-| ``<prefix>.<mokapot/percolator>.decoy.proteins.txt``        | Decoy proteins and their new scores at protein-level FDR.   |
-+-------------------------------------------------------------+-------------------------------------------------------------+
-| ``<prefix>.<mokapot/percolator>.weights.txt``               | Feature weights, showing feature usage in the rescoring run |
-+-------------------------------------------------------------+-------------------------------------------------------------+
++-----------------------------------------+-------------------------------------------------------------+
+| File                                    | Description                                                 |
++=========================================+=============================================================+
+| ``<prefix>.mokapot.psms.txt``           | PSMs and their new scores at PSM-level FDR.                 |
++-----------------------------------------+-------------------------------------------------------------+
+| ``<prefix>.mokapot.peptides.txt``       | Peptides and their new scores at peptide-level FDR.         |
++-----------------------------------------+-------------------------------------------------------------+
+| ``<prefix>.mokapot.proteins.txt``       | Proteins and their new scores at protein-level FDR.         |
++-----------------------------------------+-------------------------------------------------------------+
+| ``<prefix>.mokapot.decoy.psms.txt``     | Decoy PSMs and their new scores at PSM-level FDR.           |
++-----------------------------------------+-------------------------------------------------------------+
+| ``<prefix>.mokapot.decoy.peptides.txt`` | Decoy peptides and their new scores at peptide-level FDR.   |
++-----------------------------------------+-------------------------------------------------------------+
+| ``<prefix>.mokapot.decoy.proteins.txt`` | Decoy proteins and their new scores at protein-level FDR.   |
++-----------------------------------------+-------------------------------------------------------------+
+| ``<prefix>.mokapot.weights.txt``        | Feature weights, showing feature usage in the rescoring run |
++-----------------------------------------+-------------------------------------------------------------+
 
-If no rescoring engine is selected, if Percolator was selected, or in DEBUG mode, the following
-files will also be written:
+If no rescoring engine is selected or in DEBUG mode, the following files will also be written:
 
 +-------------------------------------------------------------+-----------------------------------------------------------+
 | File                                                        | Description                                               |

docs/source/userguide/output-files.rst

@@ -15,7 +15,7 @@
 from ms2rescore.parse_spectra import MSDataType, add_precursor_values, annotate_spectra
 from ms2rescore.report import generate
 from ms2rescore.report.data import ReportData
-from ms2rescore.rescoring_engines import mokapot, percolator
+from ms2rescore.rescoring_engines import mokapot
 from ms2rescore.rescoring_engines.mokapot import (
     add_peptide_confidence,
     add_psm_confidence,
@@ -197,15 +197,7 @@ def rescore(configuration: Dict, psm_list: Optional[PSMList] = None) -> None:
     # Rescore PSMs
     feature_weights = None
     try:
-        if "percolator" in config["rescoring_engine"]:
-            percolator.rescore(
-                psm_list,
-                output_file_root=output_file_root,
-                log_level=config["log_level"],
-                processes=config["processes"],
-                percolator_kwargs=config["rescoring_engine"]["percolator"],
-            )
-        elif "mokapot" in config["rescoring_engine"]:
+        if "mokapot" in config["rescoring_engine"]:
             if "fasta_file" not in config["rescoring_engine"]["mokapot"]:
                 config["rescoring_engine"]["mokapot"]["fasta_file"] = config["fasta_file"]
             if "protein_kwargs" in config["rescoring_engine"]["mokapot"]:

ms2rescore/core.py

@@ -69,10 +69,6 @@
         "Mokapot: Fondrie et al. JPR (2021)",
         "https://doi.org/10.1021/acs.jproteome.0c01010",
     ),
-    (
-        "Percolator: Käll et al. Nat Methods (2007)",
-        "https://doi.org/10.1038/nmeth1113",
-    ),
 ]
 LINKS = [
     (
@@ -671,30 +667,12 @@ def __init__(self, *args, **kwargs):
         self.configure(fg_color="transparent")
         self.grid_columnconfigure(0, weight=1)
 
-        self.radio_button = widgets.LabeledRadioButtons(
-            self,
-            label="Rescoring engine",
-            options=["Mokapot", "Percolator"],
-            default_value="Mokapot",
-        )
-        self.radio_button.grid(row=0, column=0, pady=(0, 10), sticky="nsew")
-
         self.mokapot_config = MokapotRescoringConfiguration(self)
-        self.mokapot_config.grid(row=1, column=0, pady=(0, 10), sticky="nsew")
-
-        self.percolator_config = PercolatorRescoringConfiguration(self)
-        self.percolator_config.grid(row=2, column=0, pady=(0, 10), sticky="nsew")
+        self.mokapot_config.grid(row=0, column=0, pady=(0, 10), sticky="nsew")
 
     def get(self) -> Dict:
         """Return the configuration as a dictionary."""
-        if self.radio_button.get().lower() == "mokapot":
-            return {self.radio_button.get().lower(): self.mokapot_config.get()}
-        elif self.radio_button.get().lower() == "percolator":
-            return {self.radio_button.get().lower(): self.percolator_config.get()}
-        else:
-            raise MS2RescoreConfigurationError(
-                f"Unknown rescoring engine: {self.radio_button.get().lower()}"
-            )
+        return {"mokapot": self.mokapot_config.get()}
 
 
 class MokapotRescoringConfiguration(ctk.CTkFrame):
@@ -757,28 +735,6 @@ def _parse_protein_kwargs(table_output):
         return protein_kwargs
 
 
-class PercolatorRescoringConfiguration(ctk.CTkFrame):
-    def __init__(self, *args, **kwargs):
-        """Rescoring engine configuration frame."""
-        super().__init__(*args, **kwargs)
-
-        self.configure(fg_color="transparent")
-        self.grid_columnconfigure(0, weight=1)
-
-        self.title = widgets._Heading(self, text="Percolator coffeeguration")
-        self.title.grid(row=0, column=0, columnspan=2, pady=(0, 5), sticky="ew")
-
-        self.weights_file = widgets.LabeledFileSelect(
-            self, label="Pretrained Percolator model weights", file_option="openfile"
-        )
-        self.weights_file.grid(row=1, column=0, columnspan=2, sticky="nsew")
-
-    def get(self) -> Dict:
-        """Return the configuration as a dictionary."""
-        config = {"init-weights": self.weights_file.get()}
-        return config
-
-
 class UpdateDialog(PopupWindow):
     def __init__(self, master, current: str, latest: str, url: str):
         msg = (