diff --git a/.gitattributes b/.gitattributes
index 4d9c78d8..5fa60135 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1 +1 @@
-ms2pip/models/**/*.c filter=lfs diff=lfs merge=lfs -text
+ms2pip/_models_c/**/*.c filter=lfs diff=lfs merge=lfs -text
diff --git a/.github/workflows/build_and_publish.yml b/.github/workflows/build_and_publish.yml
index a7c25263..78d7e392 100644
--- a/.github/workflows/build_and_publish.yml
+++ b/.github/workflows/build_and_publish.yml
@@ -9,61 +9,54 @@ jobs:
name: Build source distribution
runs-on: ubuntu-latest
steps:
- - uses: actions/checkout@v2
+ - uses: actions/checkout@v4
with:
- lfs: 'true'
+ lfs: "true"
+
- name: Set up Python
- uses: actions/setup-python@v2
+ uses: actions/setup-python@v5
with:
- python-version: '3.10'
+ python-version: "3.11"
+
- name: Install dependencies
run: |
python -m pip install --upgrade pip
- pip install setuptools oldest-supported-numpy cython flake8
- - name: Check for syntax errors
- run: |
- flake8 ./ms2pip ./fasta2speclib --count --select=E9,F63,F7,F82 --show-source --statistics
+ pip install build
+
- name: Build sdist
- run: python setup.py sdist
- - uses: actions/upload-artifact@v2
+ run: python -m build --sdist --outdir dist
+
+ - uses: actions/upload-artifact@v4
with:
- name: dist
- path: dist/*.tar.gz
+ name: dist-source
+ path: dist
build-wheels:
name: Build wheels on ${{ matrix.os }}
runs-on: ${{ matrix.os }}
strategy:
matrix:
- os: [ubuntu-latest, windows-latest, macos-latest]
+ os: [ubuntu-latest, windows-latest, macos-13, macos-14] # macos-13 for intel, macos-14 for apple silicon
steps:
- - uses: actions/checkout@v2
+ - uses: actions/checkout@v4
with:
- lfs: 'true'
- - uses: actions/setup-python@v2
- name: Install Python
+ lfs: "true"
+
+ - uses: actions/setup-python@v5
+ name: Set up Python
with:
- python-version: '3.10'
- - name: Check for syntax errors
- run: |
- python -m pip install flake8
- flake8 ./ms2pip ./fasta2speclib --count --select=E9,F63,F7,F82 --show-source --statistics
+ python-version: "3.11"
+
- name: Install cibuildwheel
- run: python -m pip install cibuildwheel==2.3.1
+ run: python -m pip install cibuildwheel>=2
+
- name: Build wheels
run: python -m cibuildwheel --output-dir dist
- env:
- # No XGBoost wheels for 32bit Windows
- CIBW_BUILD: "cp3*-manylinux_x86_64 cp3*-win_amd64 cp3*-macosx_x86_64"
- CIBW_SKIP: "cp36-* cp39-macosx_x86_64" # EOL & build issue #216
- CIBW_BEFORE_ALL_MACOS: "brew install libomp"
- CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014"
- CIBW_TEST_REQUIRES: "pytest"
- CIBW_TEST_COMMAND: "pytest {project}/tests"
- - uses: actions/upload-artifact@v2
+
+ - uses: actions/upload-artifact@v4
with:
- name: dist
- path: dist/ms2pip-*.whl
+ name: dist-${{ matrix.os }}
+ path: dist
publish-to-pypi:
needs: [build-sdist, build-wheels]
@@ -74,10 +67,6 @@ jobs:
permissions:
id-token: write
steps:
- - uses: actions/download-artifact@v2
- with:
- name: dist
- path: dist
+ - uses: actions/download-artifact@v4
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@release/v1
-
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 9a3c051d..133da30e 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -5,42 +5,42 @@ on:
branches:
- releases
pull_request:
+ workflow_dispatch:
jobs:
build:
-
runs-on: ubuntu-latest
strategy:
max-parallel: 4
matrix:
- python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
+ python-version: ["3.8", "3.9", "3.10", "3.11"]
steps:
- - uses: actions/checkout@v2
- with:
- lfs: 'true'
- - name: Set up Python ${{ matrix.python-version }}
- uses: actions/setup-python@v2
- with:
- python-version: ${{ matrix.python-version }}
- - name: Install dependencies
- run: |
- python -m pip install --upgrade pip
- pip install wheel flake8 pytest oldest-supported-numpy cython
- - name: Lint with flake8
- run: |
- # stop the build if there are Python syntax errors or undefined names
- flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
- # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
- flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
- - name: Build and install ms2pip
- run: |
- python setup.py bdist_wheel
- pip install dist/ms2pip*.whl
- - name: Test with pytest
- run: |
- pytest
- - uses: actions/upload-artifact@v2
- with:
- name: wheels
- path: dist/ms2pip*.whl
+ - uses: actions/checkout@v4
+ with:
+ lfs: "true"
+
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v5
+ with:
+ python-version: ${{ matrix.python-version }}
+
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install ruff
+
+ - name: Check with Ruff
+ run: ruff check --output-format=github ./ms2pip
+
+ - name: Build and install ms2pip
+ run: |
+ pip install .[dev]
+
+ - name: Test with pytest
+ run: |
+ pytest
+
+ - name: Test installation
+ run: |
+ ms2pip --help
diff --git a/.readthedocs.yml b/.readthedocs.yml
new file mode 100644
index 00000000..8badf360
--- /dev/null
+++ b/.readthedocs.yml
@@ -0,0 +1,34 @@
+version: 2
+
+build:
+ os: "ubuntu-20.04"
+ tools:
+ python: "3.8"
+ jobs:
+ post_checkout:
+ # Download and uncompress the binary
+ # https://git-lfs.github.com/
+ - wget https://github.com/git-lfs/git-lfs/releases/download/v3.1.4/git-lfs-linux-amd64-v3.1.4.tar.gz
+ - tar xvfz git-lfs-linux-amd64-v3.1.4.tar.gz
+ # Modify LFS config paths to point where git-lfs binary was downloaded
+ - git config filter.lfs.process "`pwd`/git-lfs filter-process"
+ - git config filter.lfs.smudge "`pwd`/git-lfs smudge -- %f"
+ - git config filter.lfs.clean "`pwd`/git-lfs clean -- %f"
+ # Make LFS available in current repository
+ - ./git-lfs install
+ # Download content from remote
+ - ./git-lfs fetch
+ # Make local files to have the real content on them
+ - ./git-lfs checkout
+ post_install:
+ - rm -r ms2pip
+
+python:
+ install:
+ - method: pip
+ path: .
+ extra_requirements:
+ - docs
+
+sphinx:
+ builder: dirhtml
diff --git a/CITATION.cff b/CITATION.cff
index a145f3cf..8f288b40 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -1,40 +1,53 @@
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_c"
-license: "Apache-2.0"
-preferred-citation:
- type: article
- 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"
- doi: "10.1093/nar/gkz299"
+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"
- month: 9
- start: W295
- end: W299
- title: "Updated MS²PIP web server delivers fast and accurate MS2 peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques"
- issue: 1
- volume: 47
- year: 2019
+ 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"
diff --git a/MANIFEST.in b/MANIFEST.in
index b444026c..248bf4ca 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,3 +1,3 @@
-include ms2pip/cython_modules/*.c
-include ms2pip/models/*/*.c
-include ms2pip/models/*.h
+include ms2pip/_cython_modules/*.c
+include ms2pip/_models_c/*/*.c
+include ms2pip/_models_c/*.h
diff --git a/README.md b/README.md
deleted file mode 100644
index f29a3640..00000000
--- a/README.md
+++ /dev/null
@@ -1,350 +0,0 @@
-[](https://iomics.ugent.be/ms2pip/)
-
-
-[![GitHub release](https://img.shields.io/github/v/release/compomics/ms2pip_c?include_prereleases&style=flat-square)](https://github.com/compomics/ms2pip_c/releases/latest/)
-[![PyPI](https://img.shields.io/pypi/v/ms2pip?style=flat-square)](https://pypi.org/project/ms2pip/)
-[![Tests](https://img.shields.io/github/actions/workflow/status/compomics/ms2pip_c/test.yml?branch=releases&label=tests&style=flat-square)](https://github.com/compomics/ms2pip_c/actions/workflows/test.yml)
-[![Build](https://img.shields.io/github/actions/workflow/status/compomics/ms2pip_c/build_and_publish.yml?style=flat-square)](https://github.com/compomics/ms2pip_c/actions/workflows/build_and_publish.yml)
-[![Open issues](https://img.shields.io/github/issues/compomics/ms2pip_c?style=flat-square)](https://github.com/compomics/ms2pip_c/issues/)
-[![Last commit](https://img.shields.io/github/last-commit/compomics/ms2pip_c?style=flat-square)](https://github.com/compomics/ms2pip_c/commits/releases/)
-[![GitHub](https://img.shields.io/github/license/compomics/ms2pip_c?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0)
-[![Twitter](https://img.shields.io/twitter/follow/compomics?style=social)](https://twitter.com/compomics)
-
-MS²PIP: MS² Peak Intensity Prediction - Fast and accurate peptide fragmention
-spectrum prediction for multiple fragmentation methods, instruments and labeling techniques.
-
----
-
-- [Introduction](#introduction)
-- [Installation](#installation)
-- [Usage](#usage)
-- [Specialized prediction models](#specialized-prediction-models)
-
----
-
-## Introduction
-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 machine learning algorithm and is written in
-Python.
-
-You can install MS²PIP on your machine by following the
-[installation instructions](#installation) below. 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/).
-
-To generate a predicted spectral library starting from a FASTA file, we
-developed a pipeline called fasta2speclib. Usage of this pipeline is described
-on the
-[fasta2speclib wiki page](http://compomics.github.io/projects/ms2pip_c/wiki/fasta2speclib).
-Fasta2speclib was developed in collaboration with the ProGenTomics group for the
-[MS²PIP for DIA](https://github.com/brvpuyve/MS2PIP-for-DIA) project.
-
-To improve the sensitivity of your peptide identification pipeline with MS²PIP
-predictions, check out [MS²Rescore](https://github.com/compomics/ms2rescore/).
-
-If you use MS²PIP for your research, please cite the following publication:
-- Gabriels, R., Martens, L., & Degroeve, S. (2019). Updated MS²PIP 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](https://doi.org/10.1093/nar/gkz299)
-
-Prior MS²PIP publications:
-- Degroeve, S., Maddelein, D., & Martens, L. (2015). MS²PIP prediction server:
-compute and visualize MS² peak intensity predictions for CID and HCD
-fragmentation. *Nucleic Acids Research*, 43(W1), W326–W330.
-[doi:10.1093/nar/gkv542](https://doi.org/10.1093/nar/gkv542)
-- Degroeve, S., & Martens, L. (2013). MS²PIP: a tool for MS/MS peak intensity
-prediction. *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.
-
----
-
-## Installation
-
-[![install pip](https://flat.badgen.net/badge/install%20with/pip/green)](https://pypi.org/project/ms2pip/)
-[![install bioconda](https://flat.badgen.net/badge/install%20with/bioconda/green)](https://bioconda.github.io/recipes/ms2pip/README.html)
-[![container](https://flat.badgen.net/badge/pull/biocontainer/blue?icon=docker)](https://quay.io/repository/biocontainers/ms2pip)
-
-#### Pip package
-
-With Python 3.6 or higher, run:
-```
-pip install ms2pip
-```
-
-Compiled wheels are available for Python 3.6, 3.7, and 3.8, on 64bit Linux,
-Windows, and macOS. This should install MS²PIP in a few seconds. For other
-platforms, MS²PIP can be built from source, although it can take a while
-to compile the large prediction models.
-
-We recommend using a [venv](https://docs.python.org/3/library/venv.html) or
-[conda](https://docs.conda.io/en/latest/) virtual environment.
-
-#### Conda package
-
-Install with activated bioconda and conda-forge channels:
-```
-conda install -c defaults -c bioconda -c conda-forge ms2pip
-```
-
-Bioconda packages are only available for Linux and macOS.
-
-#### Docker container
-First check the latest version tag on [biocontainers/ms2pip/tags](https://quay.io/repository/biocontainers/ms2pip?tab=tags). Then pull and run the container with
-```
-docker container run -v :/data -w /data quay.io/biocontainers/ms2pip: ms2pip
-```
-where `` is the absolute path to the directory with your MS²PIP input files, `` is the container version tag, and `` are the ms2pip command line options (see [Command line interface](#command-line-interface)).
-
-#### For development
-
-Clone this repository and use pip to install an editable version:
-```
-pip install --editable .
-```
-
----
-
-## Usage
-
-1. [Fast prediction of large amounts of peptide spectra](#fast-prediction-of-large-amounts-of-peptide-spectra)
- 1. [Command line interface](#command-line-interface)
- 2. [Python API](#python-api)
- 3. [Input files](#input-files)
- 1. [Config file](#config-file)
- 2. [PEPREC file](#peprec-file)
- 3. [Spectrum file (optional)](#spectrum-file-optional)
- 4. [Examples](#examples)
- 4. [Output](#output)
-2. [Predict and plot a single peptide spectrum](#predict-and-plot-a-single-peptide-spectrum)
-
-
-### Fast prediction of large amounts of peptide spectra
-
-MS²PIP comes with [pre-trained models](#specialized-prediction-models) for a
-variety of fragmentation methods and modifications. These models can easily be
-applied by configuring MS²PIP in the [config file](#config-file) and providing a
-list of peptides in the form of a [PEPREC file](#peprec-file). Optionally,
-MS²PIP predictions can be compared to observed spectra in an
-[MGF or mzmL file](#spectrum-file-optional).
-
-#### Command line interface
-
-To predict a large amount of peptide spectra, use `ms2pip`:
-```
-usage: ms2pip [-h] -c CONFIG_FILE [-s SPECTRUM_FILE] [-w FEATURE_VECTOR_OUTPUT]
- [-r] [-x] [-m] [-t] [-n NUM_CPU]
- [--sqldb-uri SQLDB_URI]
-
-
-positional arguments:
- list of peptides
-
-optional arguments:
- -h, --help show this help message and exit
- -c, --config-file Configuration file: text-based (extensions `.txt`,
- `.config`, or `.ms2pip`) or TOML (extension `.toml`).
- -s, --spectrum-file MGF or mzML spectrum file (optional)
- -w, --vector-file write feature vectors to FILE.{pkl,h5} (optional)
- -r, --retention-time add retention time predictions (requires DeepLC python package)
- -x, --correlations calculate correlations (if spectrum file is given)
- -m, --match-spectra match peptides to spectra based on predicted spectra (if spectrum file is given)
- -n, --num-cpu number of CPUs to use (default: all available)
- --sqldb-uri use sql database of observed spectra instead of spectrum files
- --model-dir custom directory for downloaded XGBoost model files. By default, `~/.ms2pip` is used.
-```
-
-#### Python API
-
-The `MS2PIP` class can be imported from `ms2pip.ms2pipC` and run as follows:
-```python
->>> from ms2pip.ms2pipC import MS2PIP
->>> params = {
-... "ms2pip": {
-... "ptm": [
-... "Oxidation,15.994915,opt,M",
-... "Carbamidomethyl,57.021464,opt,C",
-... "Acetyl,42.010565,opt,N-term",
-... ],
-... "frag_method": "HCD",
-... "frag_error": 0.02,
-... "out": "csv",
-... "sptm": [], "gptm": [],
-... }
-... }
->>> ms2pip = MS2PIP("test.peprec", params=params, return_results=True)
->>> predictions = ms2pip.run()
-```
-
-#### Input files
-##### Config file
-Several MS²PIP options need to be set in this config file.
-- `model=X` where X is one of the currently supported MS²PIP models (see
-[Specialized prediction models](#specialized-prediction-models)).
-- `frag_error=X` where is X is the fragmentation spectrum mass tolerance in Da
-(only relevant if a spectrum file is passed).
-- `out=X` where X is a comma-separated list of a selection of the currently
-supported output file formats: `csv`, `mgf`, `msp`, `spectronaut`, or
-`bibliospec` (SSL/MS2, also for Skyline). For example: `out=csv,msp`.
-- `ptm=X,Y,opt,Z` for every peptide modification where:
- - `X` is the PTM name and needs to match the names that are used in the
- [PEPREC file](#peprec-file)). If the `--retention_time` option is used, PTM names must
- match the PSI-MOD/Unimod names embedded in DeepLC (see
- [DeepLC documentation](https://github.com/compomics/DeepLC)).
- - `Y` is the mass shift in Da associated with the PTM.
- - `Z` is the one-letter code of the amino acid AA that is modified by the PTM.
-For N- and C-terminal modifications, `Z` should be `N-term` or `C-term`,
-respectively.
-
-##### PEPREC file
-To apply the pre-trained models you need to pass *only* a `` to
-MS²PIP. This file contains the peptide sequences for which you want to predict
-peak intensities. The file is space separated and contains at least the
-following four columns:
-
-- `spec_id`: unique id (string) for the peptide/spectrum. This must match the
-`TITLE` field in the corresponding MGF file, or `nativeID` (MS:1000767) in the
-corresponding mzML file, if given.
-- `modifications`: Amino acid modifications for the given peptide. Every
-modification is listed as `location|name`, separated by a pipe (`|`) between the
-location, the name, and other modifications. `location` is an integer counted
-starting at `1` for the first AA. `0` is reserved for N-terminal modifications,
-`-1` for C-terminal modifications. `name` has to correspond to a modification
-listed in the [Config file](#config-file). Unmodified peptides are marked with
-a hyphen (`-`).
-- `peptide`: the unmodified amino acid sequence.
-- `charge`: precursor charge state as an integer (without `+`).
-
-Peptides must be strictly longer than 2 and shorter than 100 amino acids and
-cannot contain the following amino acid one-letter codes: B, J, O, U, X or Z.
-Peptides not fulfilling these requirements will be filtered out and will not be
-reported in the output.
-
-In the [conversion_tools](https://github.com/compomics/ms2pip_c/tree/releases/conversion_tools)
-folder, we provide a host of Python scripts to convert common search engine
-output files to a PEPREC file.
-
-To start from a FASTA file, see [fasta2speclib](http://compomics.github.io/projects/ms2pip_c/wiki/fasta2speclib).
-
-
-##### Spectrum file (optional)
-Optionally, an MGF or mzML file with measured spectra can be passed to MS²PIP. In this
-case, MS²PIP will calculate correlations between the measured and predicted
-peak intensities. Make sure that the PEPREC `spec_id` matches the MGF `TITLE`
-field or mzML `nativeID`. Spectra present in the spectrum file, but missing in the
-PEPREC file (and vice versa) will be skipped.
-
-##### Examples
-Suppose the **config file** contains the following lines
-```
-model=HCD
-frag_error=0.02
-out=csv,mgf,msp
-ptm=Carbamidomethyl,57.02146,opt,C
-ptm=Acetyl,42.010565,opt,N-term
-ptm=Glyloss,-58.005479,opt,C-term
-```
-then the **PEPREC file** could look like this:
-```
-spec_id modifications peptide charge
-peptide1 - ACDEK 2
-peptide2 2|Carbamidomethyl ACDEFGR 3
-peptide3 0|Acetyl|2|Carbamidomethyl ACDEFGHIK 2
-```
-In this example, `peptide3` is N-terminally acetylated and carries a
-carbamidomethyl on its second amino acid.
-
-The corresponding (optional) **MGF file** can contain the following spectrum:
-```
-BEGIN IONS
-TITLE=peptide1
-PEPMASS=283.11849750978325
-CHARGE=2+
-72.04434967 0.00419513
-147.11276245 0.17418982
-175.05354309 0.03652963
-...
-END IONS
-```
-
-#### Output
-The predictions are saved in the output file(s) specified in the
-[config file](#config-file). Note that the normalization of intensities depends
-on the output file format. In the CSV file output, intensities are
-log2-transformed. To "unlog" the intensities, use the following formula:
-`intensity = (2 ** log2_intensity) - 0.001`.
-
-
-### Predict and plot a single peptide spectrum
-With `ms2pip-single-prediction` a single peptide spectrum can be predicted with MS²PIP
-and plotted with [spectrum_utils](https://spectrum-utils.readthedocs.io/). For instance,
-
-```sh
-ms2pip-single-prediction "PGAQANPYSR" "-" 3 --model TMT
-```
-
-results in:
-
-![Predicted spectrum](img/PGAQANPYSR-3-TMT.png)
-
-Run `ms2pip-single-prediction --help` for more details.
-
----
-
-## Specialized prediction models
-MS²PIP contains multiple specialized prediction models, fit for peptide spectra
-with different properties. These properties include fragmentation method,
-instrument, labeling techniques and modifications. As all of these properties
-can influence fragmentation patterns, it is important to match the MS²PIP model
-to the properties of your experimental dataset.
-
-Currently the following models are supported in MS²PIP: `HCD`, `CID`, `iTRAQ`,
-`iTRAQphospho`, `TMT`, `TTOF5600`, `HCDch2` and `CIDch2`. The last two "ch2"
-models also include predictions for doubly charged fragment ions (b++ and y++),
-next to the predictions for singly charged b- and y-ions.
-
-### MS² acquisition information and peptide properties of the models' training datasets
-
-| Model | Fragmentation method | MS² mass analyzer | Peptide properties |
-| - | - | - | - |
-| HCD2019 | HCD | Orbitrap | Tryptic digest |
-| HCD2021 | HCD | Orbitrap | Tryptic/ Chymotrypsin digest |
-| CID | CID | Linear ion trap | Tryptic digest |
-| iTRAQ | HCD | Orbitrap | Tryptic digest, iTRAQ-labeled |
-| iTRAQphospho | HCD | Orbitrap | Tryptic digest, iTRAQ-labeled, enriched for phosphorylation |
-| TMT | HCD | Orbitrap | Tryptic digest, TMT-labeled |
-| TTOF5600 | CID | Quadrupole Time-of-Flight | Tryptic digest |
-| HCDch2 | HCD | Orbitrap | Tryptic digest |
-| CIDch2 | CID | Linear ion trap | Tryptic digest |
-| Immuno-HCD | HCD | Orbitrap | Immunopeptides |
-| CID-TMT | CID | Linear ion trap | Tryptic digest, TMT-labeled |
-| timsTOF2023 | CID | Ion mobility quadrupole time-of-flight | Tryptic-, elastase digest, immuno class 1 |
-| timsTOF2024 | CID | Ion mobility quadrupole time-of-flight | Tryptic-, elastase digest, immuno class 1 & class 2 |
-
-### Models, version numbers, and the train and test datasets used to create each model
-
-| Model | Current version | Train-test dataset (unique peptides) | Evaluation dataset (unique peptides) | Median Pearson correlation on evaluation dataset |
-| - | - | - | - | - |
-| HCD2019 | v20190107 | [MassIVE-KB](https://doi.org/10.1016/j.cels.2018.08.004) (1 623 712) | [PXD008034](https://doi.org/10.1016/j.jprot.2017.12.006) (35 269) | 0.903786 |
-| CID | v20190107 | [NIST CID Human](https://chemdata.nist.gov/) (340 356) | [NIST CID Yeast](https://chemdata.nist.gov/) (92 609) | 0.904947 |
-| iTRAQ | v20190107 | [NIST iTRAQ](https://chemdata.nist.gov/) (704 041) | [PXD001189](https://doi.org/10.1182/blood-2016-05-714048) (41 502) | 0.905870 |
-| iTRAQphospho | v20190107 | [NIST iTRAQ phospho](https://chemdata.nist.gov/) (183 383) | [PXD001189](https://doi.org/10.1182/blood-2016-05-714048) (9 088) | 0.843898 |
-| TMT | v20190107 | [Peng Lab TMT Spectral Library](https://doi.org/10.1021/acs.jproteome.8b00594) (1 185 547) | [PXD009495](https://doi.org/10.15252/msb.20188242) (36 137) | 0.950460 |
-| TTOF5600 | v20190107 | [PXD000954](https://doi.org/10.1038/sdata.2014.31) (215 713) | [PXD001587](https://doi.org/10.1038/nmeth.3255) (15 111) | 0.746823 |
-| HCDch2 | v20190107 | [MassIVE-KB](https://doi.org/10.1016/j.cels.2018.08.004) (1 623 712) | [PXD008034](https://doi.org/10.1016/j.jprot.2017.12.006) (35 269) | 0.903786 (+) and 0.644162 (++) |
-| CIDch2 | v20190107 | [NIST CID Human](https://chemdata.nist.gov/) (340 356) | [NIST CID Yeast](https://chemdata.nist.gov/) (92 609) | 0.904947 (+) and 0.813342 (++) |
-| HCD2021 | v20210416 | [Combined dataset] (520 579) | [PXD008034](https://doi.org/10.1016/j.jprot.2017.12.006) (35 269) | 0.932361
-| Immuno-HCD | v20210316 | [Combined dataset] (460 191) | [PXD005231 (HLA-I)](https://doi.org/10.1101/098780) (46 753) [PXD020011 (HLA-II)](https://doi.org/10.3389/fimmu.2020.01981 ) (23 941) | 0.963736 0.942383
-| CID-TMT | v20220104 | [in-house dataset] (72 138) | [PXD005890](https://doi.org/10.1021/acs.jproteome.7b00091) (69 768) | 0.851085
-| timsTOF2023 | v20230912 | [Combined dataset] (234 973) | PXD043026 PXD046535 PXD046543 (13 012) | 0.892540 (tryptic) 0.871258 (elastase) 0.899834 (class I) 0.635548 (class II)
-| timsTOF2024 | v20240105 | [Combined dataset] (480 024) | PXD043026 PXD046535 PXD046543 PXD038782 (25 265) | 0.883270 (tryptic) 0.814374 (elastase) 0.887192 (class I) 0.847951 (class II)
-
-
-To train custom MS²PIP models, please refer to [Training new MS²PIP models](http://compomics.github.io/projects/ms2pip_c/wiki/Training-new-MS2PIP-models.html) on our Wiki pages.
diff --git a/README.rst b/README.rst
new file mode 100644
index 00000000..24146e4f
--- /dev/null
+++ b/README.rst
@@ -0,0 +1,117 @@
+.. image:: https://github.com/compomics/ms2pip_c/raw/releases/img/ms2pip_logo_1000px.png
+ :width: 150px
+ :height: 150px
+
+|
+
+.. image:: https://img.shields.io/github/v/release/compomics/ms2pip_c?include_prereleases&style=flat-square
+ :target: https://github.com/compomics/ms2pip_c/releases/latest/
+.. image:: https://img.shields.io/pypi/v/ms2pip?style=flat-square
+ :target: https://pypi.org/project/ms2pip/
+.. image:: https://img.shields.io/github/actions/workflow/status/compomics/ms2pip_c/test.yml?branch=releases&label=tests&style=flat-square
+ :target: https://github.com/compomics/ms2pip_c/actions/workflows/test.yml
+.. image:: https://img.shields.io/github/actions/workflow/status/compomics/ms2pip_c/build_and_publish.yml?style=flat-square
+ :target: https://github.com/compomics/ms2pip_c/actions/workflows/build_and_publish.yml
+.. image:: https://img.shields.io/github/issues/compomics/ms2pip_c?style=flat-square
+ :target: https://github.com/compomics/ms2pip_c/issues/
+.. image:: https://img.shields.io/github/last-commit/compomics/ms2pip_c?style=flat-square
+ :target: https://github.com/compomics/ms2pip_c/commits/releases/
+.. image:: https://img.shields.io/github/license/compomics/ms2pip_c?style=flat-square
+ :target: https://www.apache.org/licenses/LICENSE-2.0
+.. image:: https://img.shields.io/twitter/follow/compomics?style=social
+ :target: https://twitter.com/compomics
+
+---------------------------------------------------------------------------------------------------
+
+MS²PIP: MS2 Peak Intensity Prediction - Fast and accurate peptide fragmentation
+spectrum prediction for multiple fragmentation methods, instruments and labeling techniques.
+
+---------------------------------------------------------------------------------------------------
+
+About
+-----
+
+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 `_ 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 `_. For a more
+user-friendly experience, go to the `MS²PIP 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 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 `_ for more information.
+
+Related projects
+----------------
+
+- `MS²Rescore `_: Use MS²PIP 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 MS²PIP for your research, please cite the following publication:
+
+- Declercq, A., Bouwmeester, R., Chiva, C., Sabidó, E., Hirschler, A., Carapito, C., Martens, L.,
+ Degroeve, S., Gabriels, R. (2023). Updated MS²PIP web server supports cutting-edge proteomics
+ applications. `Nucleic Acids Research` `doi:10.1093/nar/gkad335 `_
+
+Prior MS²PIP publications:
+
+- Gabriels, R., Martens, L., & Degroeve, S. (2019). Updated MS²PIP 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). MS²PIP prediction server:
+ compute and visualize MS2 peak intensity predictions for CID and HCD
+ fragmentation. `_Nucleic Acids Research`, 43(W1), W326–W330.
+ `doi:10.1093/nar/gkv542 `_
+- Degroeve, S., & Martens, L. (2013). MS²PIP: a tool for MS/MS peak intensity
+ prediction. `Bioinformatics (Oxford, England)`, 29(24), 3199–203.
+ `doi:10.1093/bioinformatics/btt544 `_
+
+Please also take note of, and mention, the MS²PIP 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!
diff --git a/config.toml b/config.toml
deleted file mode 100644
index 38b0014a..00000000
--- a/config.toml
+++ /dev/null
@@ -1,31 +0,0 @@
-[ms2pip]
-model = "HCD"
-frag_error = "0.02"
-out = "csv"
-ptm = [
- "PhosphoS,79.966331,opt,S",
- "PhosphoT,79.966331,opt,T",
- "PhosphoY,79.966331,opt,Y",
- "Oxidation,15.994915,opt,M",
- "Carbamidomethyl,57.021464,opt,C",
- "CAM,57.021464,opt,C",
- "Glu->pyro-Glu,-18.010565,opt,E",
- "Gln->pyro-Glu,-17.026549,opt,Q",
- "Pyro-cmC,39.994915,opt,C",
- "Deamidated,0.984016,opt,N",
- "iTRAQ,144.102063,opt,N-term",
- "Acetyl,42.010565,opt,N-term",
- "TMT6plexN,229.162932,opt,N-term",
- "TMT6plex,229.162932,opt,K"
-]
-sptm = []
-gptm = []
-
-# Only applicable if --retention-time option is used
-# deeplc settings are passed to DeepLC() object as keyword arguments
-[deeplc]
-calibration_file = false
-verbose = false
-path_model = false # Path to DeepLC model
-split_cal = 25 # Can maximally be number of calibration peptides - 1
-batch_num = 350000 # Reduce to limit memory consumption
diff --git a/config.txt b/config.txt
deleted file mode 100644
index 653aa34c..00000000
--- a/config.txt
+++ /dev/null
@@ -1,18 +0,0 @@
-model=HCD
-frag_error=0.02
-out=csv
-
-ptm=PhosphoS,79.966331,opt,S
-ptm=PhosphoT,79.966331,opt,T
-ptm=PhosphoY,79.966331,opt,Y
-ptm=Oxidation,15.994915,opt,M
-ptm=Carbamidomethyl,57.021464,opt,C
-ptm=CAM,57.021464,opt,C
-ptm=Glu->pyro-Glu,-18.010565,opt,E
-ptm=Gln->pyro-Glu,-17.026549,opt,Q
-ptm=Pyro-cmC,39.994915,opt,C
-ptm=Deamidated,0.984016,opt,N
-ptm=iTRAQ,144.102063,opt,N-term
-ptm=Acetyl,42.010565,opt,N-term
-ptm=TMT6plexN,229.162932,opt,N-term
-ptm=TMT6plex,229.162932,opt,K
diff --git a/config_unimod.txt b/config_unimod.txt
deleted file mode 100644
index 13e077e5..00000000
--- a/config_unimod.txt
+++ /dev/null
@@ -1,1725 +0,0 @@
-model=HCD
-frag_error=0.02
-ptm=[1]Acetyl,42.010565,opt,T,Post-translational
-ptm=[1]Acetyl,42.010565,opt,N-term,Post-translational
-ptm=[1]Acetyl,42.010565,opt,S,Post-translational
-ptm=[1]Acetyl,42.010565,opt,C,Post-translational
-ptm=[1]Acetyl,42.010565,opt,K,Multiple
-ptm=[1]Acetyl,42.010565,opt,Y,Chemical derivative
-ptm=[1]Acetyl,42.010565,opt,H,Chemical derivative
-ptm=[1]Acetyl,42.010565,opt,R,Artefact
-ptm=[2]Amidated,-0.984016,opt,C-term,Artefact
-ptm=[3]Biotin,226.077598,opt,N-term,Chemical derivative
-ptm=[3]Biotin,226.077598,opt,K,Post-translational
-ptm=[4]Carbamidomethyl,57.021464,opt,Y,Artefact
-ptm=[4]Carbamidomethyl,57.021464,opt,T,Artefact
-ptm=[4]Carbamidomethyl,57.021464,opt,S,Artefact
-ptm=[4]Carbamidomethyl,57.021464,opt,E,Artefact
-ptm=[4]Carbamidomethyl,57.021464,opt,D,Artefact
-ptm=[4]Carbamidomethyl,57.021464,opt,H,Artefact
-ptm=[4]Carbamidomethyl,57.021464,opt,N-term,Artefact
-ptm=[4]Carbamidomethyl,57.021464,opt,K,Artefact
-ptm=[4]Carbamidomethyl,57.021464,opt,C,Chemical derivative
-ptm=[4]Carbamidomethyl,57.021464,opt,U,Chemical derivative
-ptm=[4]Carbamidomethyl,57.021464,opt,M,Chemical derivative
-ptm=[5]Carbamyl,43.005814,opt,Y,Chemical derivative
-ptm=[5]Carbamyl,43.005814,opt,T,Chemical derivative
-ptm=[5]Carbamyl,43.005814,opt,S,Chemical derivative
-ptm=[5]Carbamyl,43.005814,opt,M,Artefact
-ptm=[5]Carbamyl,43.005814,opt,C,Artefact
-ptm=[5]Carbamyl,43.005814,opt,R,Artefact
-ptm=[5]Carbamyl,43.005814,opt,N-term,Multiple
-ptm=[5]Carbamyl,43.005814,opt,K,Multiple
-ptm=[6]Carboxymethyl,58.005479,opt,N-term,Artefact
-ptm=[6]Carboxymethyl,58.005479,opt,K,Artefact
-ptm=[6]Carboxymethyl,58.005479,opt,C,Chemical derivative
-ptm=[6]Carboxymethyl,58.005479,opt,W,Chemical derivative
-ptm=[6]Carboxymethyl,58.005479,opt,U,Chemical derivative
-ptm=[7]Deamidated,0.984016,opt,Q,Artefact
-ptm=[7]Deamidated,0.984016,opt,R,Post-translational
-ptm=[7]Deamidated,0.984016,opt,N,Artefact
-ptm=[7]Deamidated,0.984016,opt,F,Post-translational
-ptm=[8]ICAT-G,486.251206,opt,C,Isotopic label
-ptm=[9]ICAT-G:2H(8),494.30142,opt,C,Isotopic label
-ptm=[10]Met->Hse,-29.992806,opt,M,Chemical derivative
-ptm=[11]Met->Hsl,-48.003371,opt,M,Chemical derivative
-ptm=[12]ICAT-D:2H(8),450.275205,opt,C,Isotopic label
-ptm=[13]ICAT-D,442.224991,opt,C,Isotopic label
-ptm=[17]NIPCAM,99.068414,opt,C,Chemical derivative
-ptm=[20]PEO-Iodoacetyl-LC-Biotin,414.193691,opt,C,Chemical derivative
-ptm=[21]Phospho,79.966331,opt,C,Post-translational
-ptm=[21]Phospho,79.966331,opt,H,Post-translational
-ptm=[21]Phospho,79.966331,opt,D,Post-translational
-ptm=[21]Phospho,79.966331,opt,Y,Post-translational
-ptm=[21]Phospho,79.966331,opt,T,Post-translational
-ptm=[21]Phospho,79.966331,opt,S,Post-translational
-ptm=[21]Phospho,79.966331,opt,R,Post-translational
-ptm=[23]Dehydrated,-18.010565,opt,D,Chemical derivative
-ptm=[23]Dehydrated,-18.010565,opt,Y,Post-translational
-ptm=[23]Dehydrated,-18.010565,opt,T,Post-translational
-ptm=[23]Dehydrated,-18.010565,opt,S,Post-translational
-ptm=[23]Dehydrated,-18.010565,opt,N,Post-translational
-ptm=[23]Dehydrated,-18.010565,opt,Q,Post-translational
-ptm=[23]Dehydrated,-18.010565,opt,C,Artefact
-ptm=[24]Propionamide,71.037114,opt,C,Artefact
-ptm=[24]Propionamide,71.037114,opt,K,Chemical derivative
-ptm=[24]Propionamide,71.037114,opt,N-term,Chemical derivative
-ptm=[25]Pyridylacetyl,119.037114,opt,N-term,Chemical derivative
-ptm=[25]Pyridylacetyl,119.037114,opt,K,Chemical derivative
-ptm=[26]Pyro-carbamidomethyl,39.994915,opt,C,Artefact
-ptm=[27]Glu->pyro-Glu,-18.010565,opt,E,Artefact
-ptm=[28]Gln->pyro-Glu,-17.026549,opt,Q,Artefact
-ptm=[29]SMA,127.063329,opt,N-term,Chemical derivative
-ptm=[29]SMA,127.063329,opt,K,Chemical derivative
-ptm=[30]Cation:Na,21.981943,opt,D,Artefact
-ptm=[30]Cation:Na,21.981943,opt,C-term,Artefact
-ptm=[30]Cation:Na,21.981943,opt,E,Artefact
-ptm=[31]Pyridylethyl,105.057849,opt,C,Chemical derivative
-ptm=[34]Methyl,14.01565,opt,E,Post-translational
-ptm=[34]Methyl,14.01565,opt,D,Post-translational
-ptm=[34]Methyl,14.01565,opt,C-term,Multiple
-ptm=[34]Methyl,14.01565,opt,N-term,Post-translational
-ptm=[34]Methyl,14.01565,opt,L,Post-translational
-ptm=[34]Methyl,14.01565,opt,I,Post-translational
-ptm=[34]Methyl,14.01565,opt,R,Post-translational
-ptm=[34]Methyl,14.01565,opt,Q,Post-translational
-ptm=[34]Methyl,14.01565,opt,N,Post-translational
-ptm=[34]Methyl,14.01565,opt,K,Post-translational
-ptm=[34]Methyl,14.01565,opt,H,Post-translational
-ptm=[34]Methyl,14.01565,opt,C,Post-translational
-ptm=[34]Methyl,14.01565,opt,S,Post-translational
-ptm=[34]Methyl,14.01565,opt,T,Post-translational
-ptm=[35]Oxidation,15.994915,opt,T,Chemical derivative
-ptm=[35]Oxidation,15.994915,opt,E,Chemical derivative
-ptm=[35]Oxidation,15.994915,opt,S,Chemical derivative
-ptm=[35]Oxidation,15.994915,opt,Q,Chemical derivative
-ptm=[35]Oxidation,15.994915,opt,L,Chemical derivative
-ptm=[35]Oxidation,15.994915,opt,I,Chemical derivative
-ptm=[35]Oxidation,15.994915,opt,U,Multiple
-ptm=[35]Oxidation,15.994915,opt,G,Pre-translational
-ptm=[35]Oxidation,15.994915,opt,W,Artefact
-ptm=[35]Oxidation,15.994915,opt,C,Post-translational
-ptm=[35]Oxidation,15.994915,opt,H,Artefact
-ptm=[35]Oxidation,15.994915,opt,V,Chemical derivative
-ptm=[35]Oxidation,15.994915,opt,R,Post-translational
-ptm=Oxidation,15.994915,opt,M,Artefact
-ptm=[35]Oxidation,15.994915,opt,Y,Post-translational
-ptm=[35]Oxidation,15.994915,opt,F,Artefact
-ptm=[35]Oxidation,15.994915,opt,P,Post-translational
-ptm=[35]Oxidation,15.994915,opt,N,Post-translational
-ptm=[35]Oxidation,15.994915,opt,K,Post-translational
-ptm=[35]Oxidation,15.994915,opt,D,Post-translational
-ptm=[36]Dimethyl,28.0313,opt,N-term,Isotopic label
-ptm=[36]Dimethyl,28.0313,opt,P,Post-translational
-ptm=[36]Dimethyl,28.0313,opt,N,Post-translational
-ptm=[36]Dimethyl,28.0313,opt,K,Multiple
-ptm=[36]Dimethyl,28.0313,opt,R,Post-translational
-ptm=[37]Trimethyl,42.04695,opt,A,Post-translational
-ptm=[37]Trimethyl,42.04695,opt,R,Chemical derivative
-ptm=[37]Trimethyl,42.04695,opt,K,Post-translational
-ptm=[39]Methylthio,45.987721,opt,C,Multiple
-ptm=[39]Methylthio,45.987721,opt,N,Post-translational
-ptm=[39]Methylthio,45.987721,opt,D,Post-translational
-ptm=[39]Methylthio,45.987721,opt,K,Artefact
-ptm=[39]Methylthio,45.987721,opt,N-term,Artefact
-ptm=[40]Sulfo,79.956815,opt,S,Post-translational
-ptm=[40]Sulfo,79.956815,opt,T,Post-translational
-ptm=[40]Sulfo,79.956815,opt,Y,Post-translational
-ptm=[40]Sulfo,79.956815,opt,C,Post-translational
-ptm=[42]Lipoyl,188.032956,opt,K,Post-translational
-ptm=[44]Farnesyl,204.187801,opt,C,Post-translational
-ptm=[45]Myristoyl,210.198366,opt,C,Post-translational
-ptm=[45]Myristoyl,210.198366,opt,K,Post-translational
-ptm=[45]Myristoyl,210.198366,opt,G,Post-translational
-ptm=[46]PyridoxalPhosphate,229.014009,opt,K,Post-translational
-ptm=[47]Palmitoyl,238.229666,opt,T,Post-translational
-ptm=[47]Palmitoyl,238.229666,opt,S,Post-translational
-ptm=[47]Palmitoyl,238.229666,opt,K,Post-translational
-ptm=[47]Palmitoyl,238.229666,opt,C,Post-translational
-ptm=[47]Palmitoyl,238.229666,opt,N-term,Post-translational
-ptm=[48]GeranylGeranyl,272.250401,opt,C,Post-translational
-ptm=[49]Phosphopantetheine,340.085794,opt,S,Post-translational
-ptm=[50]FAD,783.141486,opt,Y,Post-translational
-ptm=[50]FAD,783.141486,opt,H,Post-translational
-ptm=[50]FAD,783.141486,opt,C,Post-translational
-ptm=[51]Tripalmitate,788.725777,opt,C,Post-translational
-ptm=[52]Guanidinyl,42.021798,opt,K,Chemical derivative
-ptm=[52]Guanidinyl,42.021798,opt,N-term,Chemical derivative
-ptm=[53]HNE,156.11503,opt,K,Post-translational
-ptm=[53]HNE,156.11503,opt,H,Post-translational
-ptm=[53]HNE,156.11503,opt,C,Post-translational
-ptm=[53]HNE,156.11503,opt,A,Post-translational
-ptm=[53]HNE,156.11503,opt,L,Post-translational
-ptm=[55]Glutathione,305.068156,opt,C,Post-translational
-ptm=[56]Acetyl:2H(3),45.029395,opt,T,Isotopic label
-ptm=[56]Acetyl:2H(3),45.029395,opt,S,Isotopic label
-ptm=[56]Acetyl:2H(3),45.029395,opt,H,Isotopic label
-ptm=[56]Acetyl:2H(3),45.029395,opt,N-term,Isotopic label
-ptm=[56]Acetyl:2H(3),45.029395,opt,K,Isotopic label
-ptm=[56]Acetyl:2H(3),45.029395,opt,Y,Isotopic label
-ptm=[58]Propionyl,56.026215,opt,N-term,Isotopic label
-ptm=[58]Propionyl,56.026215,opt,K,Isotopic label
-ptm=[58]Propionyl,56.026215,opt,S,Chemical derivative
-ptm=[58]Propionyl,56.026215,opt,T,Isotopic label
-ptm=[59]Propionyl:13C(3),59.036279,opt,N-term,Isotopic label
-ptm=[59]Propionyl:13C(3),59.036279,opt,K,Isotopic label
-ptm=[60]GIST-Quat,127.099714,opt,N-term,Isotopic label
-ptm=[60]GIST-Quat,127.099714,opt,K,Isotopic label
-ptm=[61]GIST-Quat:2H(3),130.118544,opt,N-term,Isotopic label
-ptm=[61]GIST-Quat:2H(3),130.118544,opt,K,Isotopic label
-ptm=[62]GIST-Quat:2H(6),133.137375,opt,N-term,Isotopic label
-ptm=[62]GIST-Quat:2H(6),133.137375,opt,K,Isotopic label
-ptm=[63]GIST-Quat:2H(9),136.156205,opt,N-term,Isotopic label
-ptm=[63]GIST-Quat:2H(9),136.156205,opt,K,Isotopic label
-ptm=[64]Succinyl,100.016044,opt,N-term,Post-translational
-ptm=[64]Succinyl,100.016044,opt,K,Isotopic label
-ptm=[65]Succinyl:2H(4),104.041151,opt,N-term,Isotopic label
-ptm=[65]Succinyl:2H(4),104.041151,opt,K,Isotopic label
-ptm=[66]Succinyl:13C(4),104.029463,opt,N-term,Isotopic label
-ptm=[66]Succinyl:13C(4),104.029463,opt,K,Isotopic label
-ptm=[357]probiotinhydrazide,258.115047,opt,P,Chemical derivative
-ptm=[359]Pro->pyro-Glu,13.979265,opt,P,Chemical derivative
-ptm=[350]Trp->Hydroxykynurenin,19.989829,opt,W,Chemical derivative
-ptm=[368]Cys->Dha,-33.987721,opt,C,Chemical derivative
-ptm=[344]Arg->GluSA,-43.053433,opt,R,Chemical derivative
-ptm=[345]Trioxidation,47.984744,opt,Y,Chemical derivative
-ptm=[345]Trioxidation,47.984744,opt,W,Chemical derivative
-ptm=[345]Trioxidation,47.984744,opt,C,Chemical derivative
-ptm=[345]Trioxidation,47.984744,opt,F,Artefact
-ptm=[89]Iminobiotin,225.093583,opt,N-term,Chemical derivative
-ptm=[89]Iminobiotin,225.093583,opt,K,Chemical derivative
-ptm=[90]ESP,338.177647,opt,N-term,Isotopic label
-ptm=[90]ESP,338.177647,opt,K,Isotopic label
-ptm=[91]ESP:2H(10),348.240414,opt,N-term,Isotopic label
-ptm=[91]ESP:2H(10),348.240414,opt,K,Isotopic label
-ptm=[92]NHS-LC-Biotin,339.161662,opt,N-term,Chemical derivative
-ptm=[92]NHS-LC-Biotin,339.161662,opt,K,Chemical derivative
-ptm=[93]EDT-maleimide-PEO-biotin,601.206246,opt,T,Chemical derivative
-ptm=[93]EDT-maleimide-PEO-biotin,601.206246,opt,S,Chemical derivative
-ptm=[94]IMID,68.037448,opt,K,Isotopic label
-ptm=[95]IMID:2H(4),72.062555,opt,K,Isotopic label
-ptm=[353]Lysbiotinhydrazide,241.088497,opt,K,Chemical derivative
-ptm=[97]Propionamide:2H(3),74.055944,opt,C,Isotopic label
-ptm=[354]Nitro,44.985078,opt,Y,Chemical derivative
-ptm=[354]Nitro,44.985078,opt,W,Chemical derivative
-ptm=[354]Nitro,44.985078,opt,F,Artefact
-ptm=[105]ICAT-C,227.126991,opt,C,Isotopic label
-ptm=[351]Trp->Kynurenin,3.994915,opt,W,Chemical derivative
-ptm=[352]Lys->Allysine,-1.031634,opt,K,Post-translational
-ptm=[106]ICAT-C:13C(9),236.157185,opt,C,Isotopic label
-ptm=[107]FormylMet,159.035399,opt,N-term,Pre-translational
-ptm=[108]Nethylmaleimide,125.047679,opt,C,Chemical derivative
-ptm=[112]OxLysBiotinRed,354.172562,opt,K,Chemical derivative
-ptm=[119]IBTP,316.138088,opt,C,Chemical derivative
-ptm=[113]OxLysBiotin,352.156911,opt,K,Chemical derivative
-ptm=[114]OxProBiotinRed,371.199111,opt,P,Chemical derivative
-ptm=[115]OxProBiotin,369.183461,opt,P,Chemical derivative
-ptm=[116]OxArgBiotin,310.135113,opt,R,Chemical derivative
-ptm=[117]OxArgBiotinRed,312.150763,opt,R,Chemical derivative
-ptm=[118]EDT-iodoacetyl-PEO-biotin,490.174218,opt,T,Chemical derivative
-ptm=[118]EDT-iodoacetyl-PEO-biotin,490.174218,opt,S,Chemical derivative
-ptm=[121]GG,114.042927,opt,N-term,Post-translational
-ptm=[122]Formyl,27.994915,opt,N-term,Post-translational
-ptm=[122]Formyl,27.994915,opt,T,Artefact
-ptm=[122]Formyl,27.994915,opt,K,Artefact
-ptm=[122]Formyl,27.994915,opt,S,Artefact
-ptm=[123]ICAT-H,345.097915,opt,C,Isotopic label
-ptm=[124]ICAT-H:13C(6),351.118044,opt,C,Isotopic label
-ptm=[530]Cation:K,37.955882,opt,C-term,Artefact
-ptm=[530]Cation:K,37.955882,opt,E,Artefact
-ptm=[530]Cation:K,37.955882,opt,D,Artefact
-ptm=[126]Xlink:DTSSP[88],87.998285,opt,N-term,Chemical derivative
-ptm=[126]Xlink:DTSSP[88],87.998285,opt,K,Chemical derivative
-ptm=[1897]Xlink:EGS[226],226.047738,opt,K,Chemical derivative
-ptm=[1897]Xlink:EGS[226],226.047738,opt,N-term,Chemical derivative
-ptm=[127]Fluoro,17.990578,opt,A,Chemical derivative
-ptm=[128]Fluorescein,387.074287,opt,C,Chemical derivative
-ptm=[129]Iodo,125.896648,opt,H,Chemical derivative
-ptm=[129]Iodo,125.896648,opt,Y,Chemical derivative
-ptm=[130]Diiodo,251.793296,opt,Y,Chemical derivative
-ptm=[130]Diiodo,251.793296,opt,H,Chemical derivative
-ptm=[131]Triiodo,377.689944,opt,Y,Chemical derivative
-ptm=[134]Myristoleyl,208.182715,opt,G,Co-translational
-ptm=[360]Pro->Pyrrolidinone,-30.010565,opt,P,Chemical derivative
-ptm=[135]Myristoyl+Delta:H(-4),206.167065,opt,G,Co-translational
-ptm=[136]Benzoyl,104.026215,opt,N-term,Isotopic label
-ptm=[136]Benzoyl,104.026215,opt,K,Isotopic label
-ptm=[139]Dansyl,233.051049,opt,N-term,Chemical derivative
-ptm=[139]Dansyl,233.051049,opt,K,Chemical derivative
-ptm=[141]Amidine,41.026549,opt,N-term,Chemical derivative
-ptm=[141]Amidine,41.026549,opt,K,Chemical derivative
-ptm=[171]NBS:13C(6),159.008578,opt,W,Chemical derivative
-ptm=[329]Methyl:2H(3)13C(1),18.037835,opt,K,Isotopic label
-ptm=[329]Methyl:2H(3)13C(1),18.037835,opt,R,Isotopic label
-ptm=[329]Methyl:2H(3)13C(1),18.037835,opt,N-term,Isotopic label
-ptm=[330]Dimethyl:2H(6)13C(2),36.07567,opt,N-term,Isotopic label
-ptm=[330]Dimethyl:2H(6)13C(2),36.07567,opt,R,Isotopic label
-ptm=[330]Dimethyl:2H(6)13C(2),36.07567,opt,K,Isotopic label
-ptm=[172]NBS,152.988449,opt,W,Chemical derivative
-ptm=[170]Delta:H(1)N(-1)18O(1),2.988261,opt,N,Isotopic label
-ptm=[195]QAT,171.149738,opt,C,Chemical derivative
-ptm=[327]Delta:H(4)C(2)O(-1)S(1),44.008456,opt,S,Chemical derivative
-ptm=[178]DAET,87.050655,opt,T,Chemical derivative
-ptm=[178]DAET,87.050655,opt,S,Chemical derivative
-ptm=[369]Pro->Pyrrolidone,-27.994915,opt,P,Chemical derivative
-ptm=[184]Label:13C(9),9.030193,opt,Y,Isotopic label
-ptm=[184]Label:13C(9),9.030193,opt,F,Isotopic label
-ptm=[185]Label:13C(9)+Phospho,88.996524,opt,Y,Isotopic label
-ptm=[188]Label:13C(6),6.020129,opt,I,Isotopic label
-ptm=[188]Label:13C(6),6.020129,opt,L,Isotopic label
-ptm=[188]Label:13C(6),6.020129,opt,K,Isotopic label
-ptm=[188]Label:13C(6),6.020129,opt,R,Isotopic label
-ptm=[186]HPG,132.021129,opt,R,Chemical derivative
-ptm=[187]2HPG,282.052824,opt,R,Chemical derivative
-ptm=[196]QAT:2H(3),174.168569,opt,C,Isotopic label
-ptm=[193]Label:18O(2),4.008491,opt,C-term,Isotopic label
-ptm=[194]AccQTag,170.048013,opt,N-term,Chemical derivative
-ptm=[194]AccQTag,170.048013,opt,K,Chemical derivative
-ptm=[199]Dimethyl:2H(4),32.056407,opt,N-term,Isotopic label
-ptm=[199]Dimethyl:2H(4),32.056407,opt,K,Isotopic label
-ptm=[199]Dimethyl:2H(4),32.056407,opt,R,Isotopic label
-ptm=[197]EQAT,184.157563,opt,C,Chemical derivative
-ptm=[198]EQAT:2H(5),189.188947,opt,C,Isotopic label
-ptm=[200]Ethanedithiol,75.980527,opt,T,Chemical derivative
-ptm=[200]Ethanedithiol,75.980527,opt,S,Chemical derivative
-ptm=[212]NEIAA:2H(5),90.084148,opt,Y,Isotopic label
-ptm=[212]NEIAA:2H(5),90.084148,opt,C,Isotopic label
-ptm=[206]Delta:H(4)C(3)O(1),56.026215,opt,R,Artefact
-ptm=[211]NEIAA,85.052764,opt,Y,Isotopic label
-ptm=[211]NEIAA,85.052764,opt,C,Isotopic label
-ptm=[214]iTRAQ4plex,144.102063,opt,C,Isotopic label
-ptm=[214]iTRAQ4plex,144.102063,opt,T,Isotopic label
-ptm=[214]iTRAQ4plex,144.102063,opt,N-term,Isotopic label
-ptm=[214]iTRAQ4plex,144.102063,opt,S,Isotopic label
-ptm=[214]iTRAQ4plex,144.102063,opt,H,Isotopic label
-ptm=[214]iTRAQ4plex,144.102063,opt,Y,Isotopic label
-ptm=[214]iTRAQ4plex,144.102063,opt,K,Isotopic label
-ptm=[340]Bromo,77.910511,opt,F,Post-translational
-ptm=[340]Bromo,77.910511,opt,H,Post-translational
-ptm=[340]Bromo,77.910511,opt,W,Post-translational
-ptm=[340]Bromo,77.910511,opt,Y,Artefact
-ptm=[342]Amino,15.010899,opt,Y,Chemical derivative
-ptm=[343]Argbiotinhydrazide,199.066699,opt,R,Chemical derivative
-ptm=[258]Label:18O(1),2.004246,opt,Y,Isotopic label
-ptm=[258]Label:18O(1),2.004246,opt,T,Isotopic label
-ptm=[258]Label:18O(1),2.004246,opt,S,Isotopic label
-ptm=[258]Label:18O(1),2.004246,opt,C-term,Isotopic label
-ptm=[259]Label:13C(6)15N(2),8.014199,opt,K,Isotopic label
-ptm=[261]SPITC,214.971084,opt,K,Chemical derivative
-ptm=[261]SPITC,214.971084,opt,N-term,Chemical derivative
-ptm=[243]IGBP,296.016039,opt,C,Isotopic label
-ptm=[270]Cytopiloyne,362.136553,opt,Y,Chemical derivative
-ptm=[270]Cytopiloyne,362.136553,opt,S,Chemical derivative
-ptm=[270]Cytopiloyne,362.136553,opt,R,Chemical derivative
-ptm=[270]Cytopiloyne,362.136553,opt,P,Chemical derivative
-ptm=[270]Cytopiloyne,362.136553,opt,N-term,Chemical derivative
-ptm=[270]Cytopiloyne,362.136553,opt,K,Chemical derivative
-ptm=[270]Cytopiloyne,362.136553,opt,C,Chemical derivative
-ptm=[271]Cytopiloyne+water,380.147118,opt,Y,Chemical derivative
-ptm=[271]Cytopiloyne+water,380.147118,opt,T,Chemical derivative
-ptm=[271]Cytopiloyne+water,380.147118,opt,S,Chemical derivative
-ptm=[271]Cytopiloyne+water,380.147118,opt,R,Chemical derivative
-ptm=[271]Cytopiloyne+water,380.147118,opt,N-term,Chemical derivative
-ptm=[271]Cytopiloyne+water,380.147118,opt,K,Chemical derivative
-ptm=[271]Cytopiloyne+water,380.147118,opt,C,Chemical derivative
-ptm=[267]Label:13C(6)15N(4),10.008269,opt,R,Isotopic label
-ptm=[269]Label:13C(9)15N(1),10.027228,opt,F,Isotopic label
-ptm=[262]Label:2H(3),3.01883,opt,L,Isotopic label
-ptm=[262]Label:2H(3),3.01883,opt,M,Isotopic label
-ptm=[268]Label:13C(5)15N(1),6.013809,opt,M,Isotopic label
-ptm=[268]Label:13C(5)15N(1),6.013809,opt,P,Isotopic label
-ptm=[268]Label:13C(5)15N(1),6.013809,opt,V,Isotopic label
-ptm=[268]Label:13C(5)15N(1),6.013809,opt,E,Isotopic label
-ptm=[264]PET,121.035005,opt,T,Chemical derivative
-ptm=[264]PET,121.035005,opt,S,Chemical derivative
-ptm=[272]CAF,135.983029,opt,N-term,Chemical derivative
-ptm=[1905]Xlink:BS2G[96],96.021129,opt,N-term,Chemical derivative
-ptm=[1905]Xlink:BS2G[96],96.021129,opt,K,Chemical derivative
-ptm=[275]Nitrosyl,28.990164,opt,C,Post-translational
-ptm=[276]AEBS,183.035399,opt,Y,Artefact
-ptm=[276]AEBS,183.035399,opt,S,Artefact
-ptm=[276]AEBS,183.035399,opt,N-term,Artefact
-ptm=[276]AEBS,183.035399,opt,K,Artefact
-ptm=[276]AEBS,183.035399,opt,H,Artefact
-ptm=[278]Ethanolyl,44.026215,opt,K,Chemical derivative
-ptm=[278]Ethanolyl,44.026215,opt,C,Chemical derivative
-ptm=[278]Ethanolyl,44.026215,opt,R,Chemical derivative
-ptm=[987]Label:13C(6)15N(2)+Dimethyl,36.045499,opt,K,Isotopic label
-ptm=[371]HMVK,86.036779,opt,C,Chemical derivative
-ptm=[280]Ethyl,28.0313,opt,C-term,Chemical derivative
-ptm=[280]Ethyl,28.0313,opt,N-term,Chemical derivative
-ptm=[280]Ethyl,28.0313,opt,E,Artefact
-ptm=[280]Ethyl,28.0313,opt,K,Multiple
-ptm=[280]Ethyl,28.0313,opt,D,Chemical derivative
-ptm=[281]CoenzymeA,765.09956,opt,C,Post-translational
-ptm=[528]Methyl+Deamidated,14.999666,opt,Q,Post-translational
-ptm=[528]Methyl+Deamidated,14.999666,opt,N,Chemical derivative
-ptm=[529]Delta:H(5)C(2),29.039125,opt,P,Post-translational
-ptm=[284]Methyl:2H(2),16.028204,opt,K,Isotopic label
-ptm=[284]Methyl:2H(2),16.028204,opt,N-term,Isotopic label
-ptm=[285]SulfanilicAcid,155.004099,opt,E,Isotopic label
-ptm=[285]SulfanilicAcid,155.004099,opt,D,Isotopic label
-ptm=[285]SulfanilicAcid,155.004099,opt,C-term,Isotopic label
-ptm=[286]SulfanilicAcid:13C(6),161.024228,opt,E,Chemical derivative
-ptm=[286]SulfanilicAcid:13C(6),161.024228,opt,D,Chemical derivative
-ptm=[286]SulfanilicAcid:13C(6),161.024228,opt,C-term,Chemical derivative
-ptm=[289]Biotin-PEO-Amine,356.188212,opt,D,Chemical derivative
-ptm=[289]Biotin-PEO-Amine,356.188212,opt,C-term,Chemical derivative
-ptm=[289]Biotin-PEO-Amine,356.188212,opt,E,Chemical derivative
-ptm=[288]Trp->Oxolactone,13.979265,opt,W,Chemical derivative
-ptm=[290]Biotin-HPDP,428.191582,opt,C,Chemical derivative
-ptm=[291]Delta:Hg(1),201.970617,opt,C,Chemical derivative
-ptm=[292]IodoU-AMP,322.020217,opt,Y,Chemical derivative
-ptm=[292]IodoU-AMP,322.020217,opt,W,Chemical derivative
-ptm=[292]IodoU-AMP,322.020217,opt,F,Chemical derivative
-ptm=[293]CAMthiopropanoyl,145.019749,opt,N-term,Chemical derivative
-ptm=[293]CAMthiopropanoyl,145.019749,opt,K,Chemical derivative
-ptm=[294]IED-Biotin,326.141261,opt,C,Chemical derivative
-ptm=[298]Methyl:2H(3),17.03448,opt,C-term,Isotopic label
-ptm=[298]Methyl:2H(3),17.03448,opt,D,Isotopic label
-ptm=[298]Methyl:2H(3),17.03448,opt,E,Isotopic label
-ptm=[298]Methyl:2H(3),17.03448,opt,K,Isotopic label
-ptm=[298]Methyl:2H(3),17.03448,opt,R,Isotopic label
-ptm=[299]Carboxy,43.989829,opt,E,Post-translational
-ptm=[299]Carboxy,43.989829,opt,D,Post-translational
-ptm=[299]Carboxy,43.989829,opt,K,Post-translational
-ptm=[299]Carboxy,43.989829,opt,W,Chemical derivative
-ptm=[299]Carboxy,43.989829,opt,M,Post-translational
-ptm=[301]Bromobimane,190.074228,opt,C,Chemical derivative
-ptm=[302]Menadione,170.036779,opt,K,Chemical derivative
-ptm=[302]Menadione,170.036779,opt,C,Chemical derivative
-ptm=[303]DeStreak,75.998285,opt,C,Chemical derivative
-ptm=[312]Cysteinyl,119.004099,opt,C,Multiple
-ptm=[313]Lys-loss,-128.094963,opt,K,Post-translational
-ptm=[314]Nmethylmaleimide,111.032028,opt,K,Chemical derivative
-ptm=[314]Nmethylmaleimide,111.032028,opt,C,Chemical derivative
-ptm=[494]CyDye-Cy3,672.298156,opt,C,Chemical derivative
-ptm=[316]DimethylpyrroleAdduct,78.04695,opt,K,Chemical derivative
-ptm=[318]Delta:H(2)C(5),62.01565,opt,K,Chemical derivative
-ptm=[319]Delta:H(2)C(3)O(1),54.010565,opt,K,Chemical derivative
-ptm=[319]Delta:H(2)C(3)O(1),54.010565,opt,R,Chemical derivative
-ptm=[320]Nethylmaleimide+water,143.058243,opt,K,Chemical derivative
-ptm=[320]Nethylmaleimide+water,143.058243,opt,C,Chemical derivative
-ptm=[768]Methyl+Acetyl:2H(3),59.045045,opt,K,Isotopic label
-ptm=[323]Xlink:B10621,713.093079,opt,C,Chemical derivative
-ptm=[324]Xlink:DTBP[87],87.01427,opt,N-term,Chemical derivative
-ptm=[324]Xlink:DTBP[87],87.01427,opt,K,Chemical derivative
-ptm=[325]FP-Biotin,572.316129,opt,K,Chemical derivative
-ptm=[325]FP-Biotin,572.316129,opt,T,Chemical derivative
-ptm=[325]FP-Biotin,572.316129,opt,Y,Chemical derivative
-ptm=[325]FP-Biotin,572.316129,opt,S,Chemical derivative
-ptm=[332]Thiophos-S-S-biotin,525.142894,opt,Y,Chemical derivative
-ptm=[332]Thiophos-S-S-biotin,525.142894,opt,T,Chemical derivative
-ptm=[332]Thiophos-S-S-biotin,525.142894,opt,S,Chemical derivative
-ptm=[333]Can-FP-biotin,447.195679,opt,T,Chemical derivative
-ptm=[333]Can-FP-biotin,447.195679,opt,Y,Chemical derivative
-ptm=[333]Can-FP-biotin,447.195679,opt,S,Chemical derivative
-ptm=[335]HNE+Delta:H(2),158.13068,opt,K,Chemical derivative
-ptm=[335]HNE+Delta:H(2),158.13068,opt,H,Chemical derivative
-ptm=[335]HNE+Delta:H(2),158.13068,opt,C,Chemical derivative
-ptm=[361]Thrbiotinhydrazide,240.104482,opt,T,Chemical derivative
-ptm=[337]Methylamine,13.031634,opt,T,Artefact
-ptm=[337]Methylamine,13.031634,opt,S,Artefact
-ptm=[362]Diisopropylphosphate,164.060231,opt,K,Chemical derivative
-ptm=[362]Diisopropylphosphate,164.060231,opt,Y,Chemical derivative
-ptm=[362]Diisopropylphosphate,164.060231,opt,T,Chemical derivative
-ptm=[362]Diisopropylphosphate,164.060231,opt,S,Chemical derivative
-ptm=[362]Diisopropylphosphate,164.060231,opt,N-term,Chemical derivative
-ptm=[363]Isopropylphospho,122.013281,opt,Y,Chemical derivative
-ptm=[363]Isopropylphospho,122.013281,opt,T,Chemical derivative
-ptm=[363]Isopropylphospho,122.013281,opt,S,Chemical derivative
-ptm=[364]ICPL:13C(6),111.041593,opt,N-term,Isotopic label
-ptm=[364]ICPL:13C(6),111.041593,opt,K,Isotopic label
-ptm=[893]CarbamidomethylDTT,209.018035,opt,C,Artefact
-ptm=[365]ICPL,105.021464,opt,N-term,Isotopic label
-ptm=[365]ICPL,105.021464,opt,K,Isotopic label
-ptm=[366]Deamidated:18O(1),2.988261,opt,Q,Isotopic label
-ptm=[366]Deamidated:18O(1),2.988261,opt,N,Isotopic label
-ptm=[372]Arg->Orn,-42.021798,opt,R,Artefact
-ptm=[531]Cation:Cu[I],61.921774,opt,C-term,Artefact
-ptm=[531]Cation:Cu[I],61.921774,opt,E,Artefact
-ptm=[531]Cation:Cu[I],61.921774,opt,D,Artefact
-ptm=[531]Cation:Cu[I],61.921774,opt,H,Artefact
-ptm=[374]Dehydro,-1.007825,opt,C,Multiple
-ptm=[375]Diphthamide,142.110613,opt,H,Post-translational
-ptm=[376]Hydroxyfarnesyl,220.182715,opt,C,Post-translational
-ptm=[377]Diacylglycerol,576.511761,opt,C,Post-translational
-ptm=[378]Carboxyethyl,72.021129,opt,K,Post-translational
-ptm=[378]Carboxyethyl,72.021129,opt,H,Chemical derivative
-ptm=[379]Hypusine,87.068414,opt,K,Post-translational
-ptm=[380]Retinylidene,266.203451,opt,K,Post-translational
-ptm=[381]Lys->AminoadipicAcid,14.96328,opt,K,Post-translational
-ptm=[382]Cys->PyruvicAcid,-33.003705,opt,C,Post-translational
-ptm=[385]Ammonia-loss,-17.026549,opt,C,Artefact
-ptm=[385]Ammonia-loss,-17.026549,opt,S,Post-translational
-ptm=[385]Ammonia-loss,-17.026549,opt,T,Post-translational
-ptm=[385]Ammonia-loss,-17.026549,opt,N,Chemical derivative
-ptm=[387]Phycocyanobilin,586.279135,opt,C,Post-translational
-ptm=[388]Phycoerythrobilin,588.294785,opt,C,Post-translational
-ptm=[389]Phytochromobilin,584.263485,opt,C,Post-translational
-ptm=[390]Heme,616.177295,opt,H,Post-translational
-ptm=[390]Heme,616.177295,opt,C,Post-translational
-ptm=[391]Molybdopterin,521.884073,opt,C,Post-translational
-ptm=[392]Quinone,29.974179,opt,W,Post-translational
-ptm=[392]Quinone,29.974179,opt,Y,Post-translational
-ptm=[394]GPIanchor,123.00853,opt,C-term,Post-translational
-ptm=[395]PhosphoribosyldephosphoCoA,881.146904,opt,S,Post-translational
-ptm=[396]GlycerylPE,197.04531,opt,E,Post-translational
-ptm=[397]Triiodothyronine,469.716159,opt,Y,Post-translational
-ptm=[398]Thyroxine,595.612807,opt,Y,Post-translational
-ptm=[400]Tyr->Dha,-94.041865,opt,Y,Post-translational
-ptm=[401]Didehydro,-2.01565,opt,S,Post-translational
-ptm=[401]Didehydro,-2.01565,opt,Y,Post-translational
-ptm=[401]Didehydro,-2.01565,opt,T,Chemical derivative
-ptm=[401]Didehydro,-2.01565,opt,K,Artefact
-ptm=[402]Cys->Oxoalanine,-17.992806,opt,C,Post-translational
-ptm=[403]Ser->LacticAcid,-15.010899,opt,S,Post-translational
-ptm=[451]GluGlu,258.085186,opt,E,Post-translational
-ptm=[451]GluGlu,258.085186,opt,C-term,Post-translational
-ptm=[405]Phosphoadenosine,329.05252,opt,H,Post-translational
-ptm=[405]Phosphoadenosine,329.05252,opt,T,Post-translational
-ptm=[405]Phosphoadenosine,329.05252,opt,K,Post-translational
-ptm=[405]Phosphoadenosine,329.05252,opt,Y,Post-translational
-ptm=[450]Glu,129.042593,opt,E,Post-translational
-ptm=[450]Glu,129.042593,opt,C-term,Chemical derivative
-ptm=[407]Hydroxycinnamyl,146.036779,opt,C,Post-translational
-ptm=[409]FMNH,454.088965,opt,H,Post-translational
-ptm=[409]FMNH,454.088965,opt,C,Post-translational
-ptm=[410]Archaeol,634.662782,opt,C,Post-translational
-ptm=[411]Phenylisocyanate,119.037114,opt,N-term,Chemical derivative
-ptm=[412]Phenylisocyanate:2H(5),124.068498,opt,N-term,Chemical derivative
-ptm=[413]Phosphoguanosine,345.047435,opt,H,Post-translational
-ptm=[413]Phosphoguanosine,345.047435,opt,K,Post-translational
-ptm=[414]Hydroxymethyl,30.010565,opt,N,Post-translational
-ptm=[415]MolybdopterinGD+Delta:S(-1)Se(1),1620.930224,opt,C,Post-translational
-ptm=[416]Dipyrrolylmethanemethyl,418.137616,opt,C,Post-translational
-ptm=[417]PhosphoUridine,306.025302,opt,H,Post-translational
-ptm=[417]PhosphoUridine,306.025302,opt,Y,Post-translational
-ptm=[419]Glycerophospho,154.00311,opt,S,Post-translational
-ptm=[420]Carboxy->Thiocarboxy,15.977156,opt,G,Post-translational
-ptm=[421]Sulfide,31.972071,opt,D,Post-translational
-ptm=[421]Sulfide,31.972071,opt,C,Post-translational
-ptm=[421]Sulfide,31.972071,opt,W,Chemical derivative
-ptm=[422]PyruvicAcidIminyl,70.005479,opt,K,Post-translational
-ptm=[422]PyruvicAcidIminyl,70.005479,opt,V,Post-translational
-ptm=[422]PyruvicAcidIminyl,70.005479,opt,C,Post-translational
-ptm=[423]Delta:Se(1),79.91652,opt,C,Post-translational
-ptm=[424]MolybdopterinGD,1572.985775,opt,D,Post-translational
-ptm=[424]MolybdopterinGD,1572.985775,opt,C,Post-translational
-ptm=[424]MolybdopterinGD,1572.985775,opt,U,Post-translational
-ptm=[425]Dioxidation,31.989829,opt,U,Multiple
-ptm=[425]Dioxidation,31.989829,opt,C,Post-translational
-ptm=[425]Dioxidation,31.989829,opt,W,Chemical derivative
-ptm=[425]Dioxidation,31.989829,opt,Y,Post-translational
-ptm=[425]Dioxidation,31.989829,opt,F,Chemical derivative
-ptm=[425]Dioxidation,31.989829,opt,M,Post-translational
-ptm=[425]Dioxidation,31.989829,opt,K,Post-translational
-ptm=[425]Dioxidation,31.989829,opt,R,Post-translational
-ptm=[425]Dioxidation,31.989829,opt,P,Post-translational
-ptm=[425]Dioxidation,31.989829,opt,E,Chemical derivative
-ptm=[425]Dioxidation,31.989829,opt,I,Chemical derivative
-ptm=[425]Dioxidation,31.989829,opt,L,Chemical derivative
-ptm=[425]Dioxidation,31.989829,opt,V,Chemical derivative
-ptm=[426]Octanoyl,126.104465,opt,T,Post-translational
-ptm=[426]Octanoyl,126.104465,opt,S,Post-translational
-ptm=[426]Octanoyl,126.104465,opt,C,Post-translational
-ptm=[431]Palmitoleyl,236.214016,opt,C,Post-translational
-ptm=[431]Palmitoleyl,236.214016,opt,S,Post-translational
-ptm=[431]Palmitoleyl,236.214016,opt,T,Pre-translational
-ptm=[432]Cholesterol,368.344302,opt,C-term,Post-translational
-ptm=[433]Didehydroretinylidene,264.187801,opt,K,Post-translational
-ptm=[434]CHDH,294.183109,opt,D,Post-translational
-ptm=[435]Methylpyrroline,109.052764,opt,K,Post-translational
-ptm=[436]Hydroxyheme,614.161645,opt,E,Post-translational
-ptm=[437]MicrocinC7,386.110369,opt,C-term,Post-translational
-ptm=[438]Cyano,24.995249,opt,C,Post-translational
-ptm=[439]Diironsubcluster,342.786916,opt,C,Post-translational
-ptm=[440]Amidino,42.021798,opt,C,Post-translational
-ptm=[442]FMN,438.094051,opt,S,Post-translational
-ptm=[442]FMN,438.094051,opt,T,Post-translational
-ptm=[443]FMNC,456.104615,opt,C,Post-translational
-ptm=[444]CuSMo,922.834855,opt,C,Post-translational
-ptm=[445]Hydroxytrimethyl,59.04969,opt,K,Post-translational
-ptm=[447]Deoxy,-15.994915,opt,T,Chemical derivative
-ptm=[447]Deoxy,-15.994915,opt,D,Post-translational
-ptm=[447]Deoxy,-15.994915,opt,S,Chemical derivative
-ptm=[448]Microcin,831.197041,opt,C-term,Post-translational
-ptm=[449]Decanoyl,154.135765,opt,T,Post-translational
-ptm=[449]Decanoyl,154.135765,opt,S,Post-translational
-ptm=[452]GluGluGlu,387.127779,opt,C-term,Post-translational
-ptm=[452]GluGluGlu,387.127779,opt,E,Post-translational
-ptm=[453]GluGluGluGlu,516.170373,opt,C-term,Post-translational
-ptm=[453]GluGluGluGlu,516.170373,opt,E,Post-translational
-ptm=[455]Xlink:DMP[154],154.110613,opt,N-term,Chemical derivative
-ptm=[455]Xlink:DMP[154],154.110613,opt,K,Chemical derivative
-ptm=[457]NDA,175.042199,opt,N-term,Chemical derivative
-ptm=[457]NDA,175.042199,opt,K,Chemical derivative
-ptm=[464]SPITC:13C(6),220.991213,opt,N-term,Chemical derivative
-ptm=[464]SPITC:13C(6),220.991213,opt,K,Chemical derivative
-ptm=[477]TMAB:2H(9),137.16403,opt,N-term,Isotopic label
-ptm=[477]TMAB:2H(9),137.16403,opt,K,Isotopic label
-ptm=[476]TMAB,128.107539,opt,N-term,Isotopic label
-ptm=[476]TMAB,128.107539,opt,K,Isotopic label
-ptm=[478]FTC,421.073241,opt,S,Chemical derivative
-ptm=[478]FTC,421.073241,opt,R,Chemical derivative
-ptm=[478]FTC,421.073241,opt,P,Chemical derivative
-ptm=[478]FTC,421.073241,opt,K,Chemical derivative
-ptm=[478]FTC,421.073241,opt,C,Chemical derivative
-ptm=[472]AEC-MAEC,59.019355,opt,T,Chemical derivative
-ptm=[472]AEC-MAEC,59.019355,opt,S,Chemical derivative
-ptm=[481]Label:2H(4),4.025107,opt,K,Isotopic label
-ptm=[481]Label:2H(4),4.025107,opt,F,Isotopic label
-ptm=[481]Label:2H(4),4.025107,opt,Y,Isotopic label
-ptm=[495]CyDye-Cy5,684.298156,opt,C,Chemical derivative
-ptm=[488]DHP,118.065674,opt,C,Chemical derivative
-ptm=[499]IGBP:13C(2),298.022748,opt,C,Isotopic label
-ptm=[500]Nmethylmaleimide+water,129.042593,opt,C,Chemical derivative
-ptm=[501]PyMIC,134.048013,opt,N-term,Chemical derivative
-ptm=[503]LG-lactam-K,332.19876,opt,N-term,Post-translational
-ptm=[503]LG-lactam-K,332.19876,opt,K,Post-translational
-ptm=[519]BisANS,594.091928,opt,K,Chemical derivative
-ptm=[520]Piperidine,68.0626,opt,N-term,Chemical derivative
-ptm=[520]Piperidine,68.0626,opt,K,Chemical derivative
-ptm=[518]Diethyl,56.0626,opt,N-term,Chemical derivative
-ptm=[518]Diethyl,56.0626,opt,K,Chemical derivative
-ptm=[504]LG-Hlactam-K,348.193674,opt,N-term,Post-translational
-ptm=[504]LG-Hlactam-K,348.193674,opt,K,Post-translational
-ptm=[510]Dimethyl:2H(4)13C(2),34.063117,opt,N-term,Isotopic label
-ptm=[510]Dimethyl:2H(4)13C(2),34.063117,opt,R,Isotopic label
-ptm=[510]Dimethyl:2H(4)13C(2),34.063117,opt,K,Isotopic label
-ptm=[513]C8-QAT,227.224915,opt,N-term,Chemical derivative
-ptm=[513]C8-QAT,227.224915,opt,K,Chemical derivative
-ptm=[505]LG-lactam-R,290.176961,opt,R,Post-translational
-ptm=[1036]Withaferin,470.266839,opt,C,Chemical derivative
-ptm=[1037]Biotin:Thermo-88317,443.291294,opt,S,Chemical derivative
-ptm=[1037]Biotin:Thermo-88317,443.291294,opt,Y,Chemical derivative
-ptm=[525]CLIP_TRAQ_2,141.098318,opt,N-term,Isotopic label
-ptm=[525]CLIP_TRAQ_2,141.098318,opt,K,Isotopic label
-ptm=[525]CLIP_TRAQ_2,141.098318,opt,Y,Isotopic label
-ptm=[506]LG-Hlactam-R,306.171876,opt,R,Post-translational
-ptm=[522]Maleimide-PEO2-Biotin,525.225719,opt,C,Chemical derivative
-ptm=[523]Sulfo-NHS-LC-LC-Biotin,452.245726,opt,N-term,Chemical derivative
-ptm=[523]Sulfo-NHS-LC-LC-Biotin,452.245726,opt,K,Chemical derivative
-ptm=[515]FNEM,427.069202,opt,C,Chemical derivative
-ptm=[514]PropylNAGthiazoline,232.064354,opt,C,Chemical derivative
-ptm=[526]Dethiomethyl,-48.003371,opt,M,Artefact
-ptm=[532]iTRAQ4plex114,144.105918,opt,Y,Isotopic label
-ptm=[532]iTRAQ4plex114,144.105918,opt,N-term,Isotopic label
-ptm=[532]iTRAQ4plex114,144.105918,opt,K,Isotopic label
-ptm=[532]iTRAQ4plex114,144.105918,opt,C,Isotopic label
-ptm=[533]iTRAQ4plex115,144.099599,opt,Y,Isotopic label
-ptm=[533]iTRAQ4plex115,144.099599,opt,N-term,Isotopic label
-ptm=[533]iTRAQ4plex115,144.099599,opt,K,Isotopic label
-ptm=[533]iTRAQ4plex115,144.099599,opt,C,Isotopic label
-ptm=[534]Dibromo,155.821022,opt,Y,Chemical derivative
-ptm=[535]LRGG,383.228103,opt,K,Chemical derivative
-ptm=[536]CLIP_TRAQ_3,271.148736,opt,Y,Isotopic label
-ptm=[536]CLIP_TRAQ_3,271.148736,opt,N-term,Isotopic label
-ptm=[536]CLIP_TRAQ_3,271.148736,opt,K,Isotopic label
-ptm=[537]CLIP_TRAQ_4,244.101452,opt,N-term,Isotopic label
-ptm=[537]CLIP_TRAQ_4,244.101452,opt,K,Isotopic label
-ptm=[537]CLIP_TRAQ_4,244.101452,opt,Y,Isotopic label
-ptm=[698]dNIC,109.048119,opt,N-term,Isotopic label
-ptm=[698]dNIC,109.048119,opt,K,Isotopic label
-ptm=[697]NIC,105.021464,opt,N-term,Isotopic label
-ptm=[697]NIC,105.021464,opt,K,Isotopic label
-ptm=[684]BDMAPP,253.010225,opt,W,Artefact
-ptm=[684]BDMAPP,253.010225,opt,Y,Artefact
-ptm=[684]BDMAPP,253.010225,opt,N-term,Chemical derivative
-ptm=[684]BDMAPP,253.010225,opt,K,Chemical derivative
-ptm=[684]BDMAPP,253.010225,opt,H,Artefact
-ptm=[685]NA-LNO2,325.225309,opt,C,Post-translational
-ptm=[685]NA-LNO2,325.225309,opt,H,Post-translational
-ptm=[686]NA-OA-NO2,327.240959,opt,C,Post-translational
-ptm=[686]NA-OA-NO2,327.240959,opt,H,Post-translational
-ptm=[687]ICPL:2H(4),109.046571,opt,N-term,Isotopic label
-ptm=[687]ICPL:2H(4),109.046571,opt,K,Isotopic label
-ptm=[894]CarboxymethylDTT,210.00205,opt,C,Artefact
-ptm=[730]iTRAQ8plex,304.20536,opt,N-term,Isotopic label
-ptm=[730]iTRAQ8plex,304.20536,opt,T,Isotopic label
-ptm=[730]iTRAQ8plex,304.20536,opt,S,Isotopic label
-ptm=[730]iTRAQ8plex,304.20536,opt,H,Isotopic label
-ptm=[730]iTRAQ8plex,304.20536,opt,Y,Isotopic label
-ptm=[730]iTRAQ8plex,304.20536,opt,K,Isotopic label
-ptm=[730]iTRAQ8plex,304.20536,opt,C,Isotopic label
-ptm=[695]Label:13C(6)15N(1),7.017164,opt,I,Isotopic label
-ptm=[695]Label:13C(6)15N(1),7.017164,opt,L,Isotopic label
-ptm=[696]Label:2H(9)13C(6)15N(2),17.07069,opt,K,Isotopic label
-ptm=[720]HNE-Delta:H(2)O,138.104465,opt,K,Chemical derivative
-ptm=[720]HNE-Delta:H(2)O,138.104465,opt,H,Chemical derivative
-ptm=[720]HNE-Delta:H(2)O,138.104465,opt,C,Chemical derivative
-ptm=[721]4-ONE,154.09938,opt,K,Chemical derivative
-ptm=[721]4-ONE,154.09938,opt,H,Chemical derivative
-ptm=[721]4-ONE,154.09938,opt,C,Chemical derivative
-ptm=[723]O-Dimethylphosphate,107.997631,opt,Y,Chemical derivative
-ptm=[723]O-Dimethylphosphate,107.997631,opt,T,Chemical derivative
-ptm=[723]O-Dimethylphosphate,107.997631,opt,S,Chemical derivative
-ptm=[724]O-Methylphosphate,93.981981,opt,Y,Chemical derivative
-ptm=[724]O-Methylphosphate,93.981981,opt,T,Chemical derivative
-ptm=[724]O-Methylphosphate,93.981981,opt,S,Chemical derivative
-ptm=[725]Diethylphosphate,136.028931,opt,N-term,Chemical derivative
-ptm=[725]Diethylphosphate,136.028931,opt,H,Chemical derivative
-ptm=[725]Diethylphosphate,136.028931,opt,C,Chemical derivative
-ptm=[725]Diethylphosphate,136.028931,opt,K,Chemical derivative
-ptm=[725]Diethylphosphate,136.028931,opt,Y,Chemical derivative
-ptm=[725]Diethylphosphate,136.028931,opt,T,Chemical derivative
-ptm=[725]Diethylphosphate,136.028931,opt,S,Chemical derivative
-ptm=[726]Ethylphosphate,107.997631,opt,N-term,Chemical derivative
-ptm=[726]Ethylphosphate,107.997631,opt,K,Chemical derivative
-ptm=[726]Ethylphosphate,107.997631,opt,Y,Chemical derivative
-ptm=[726]Ethylphosphate,107.997631,opt,T,Chemical derivative
-ptm=[726]Ethylphosphate,107.997631,opt,S,Chemical derivative
-ptm=[727]O-pinacolylmethylphosphonate,162.080967,opt,T,Chemical derivative
-ptm=[727]O-pinacolylmethylphosphonate,162.080967,opt,S,Chemical derivative
-ptm=[727]O-pinacolylmethylphosphonate,162.080967,opt,K,Chemical derivative
-ptm=[727]O-pinacolylmethylphosphonate,162.080967,opt,Y,Chemical derivative
-ptm=[727]O-pinacolylmethylphosphonate,162.080967,opt,H,Chemical derivative
-ptm=[728]Methylphosphonate,77.987066,opt,Y,Chemical derivative
-ptm=[728]Methylphosphonate,77.987066,opt,T,Chemical derivative
-ptm=[728]Methylphosphonate,77.987066,opt,S,Chemical derivative
-ptm=[729]O-Isopropylmethylphosphonate,120.034017,opt,Y,Chemical derivative
-ptm=[729]O-Isopropylmethylphosphonate,120.034017,opt,T,Chemical derivative
-ptm=[729]O-Isopropylmethylphosphonate,120.034017,opt,S,Chemical derivative
-ptm=[731]iTRAQ8plex:13C(6)15N(2),304.19904,opt,Y,Isotopic label
-ptm=[731]iTRAQ8plex:13C(6)15N(2),304.19904,opt,N-term,Isotopic label
-ptm=[731]iTRAQ8plex:13C(6)15N(2),304.19904,opt,K,Isotopic label
-ptm=[731]iTRAQ8plex:13C(6)15N(2),304.19904,opt,C,Isotopic label
-ptm=[735]BEMAD_ST,136.001656,opt,T,Chemical derivative
-ptm=[735]BEMAD_ST,136.001656,opt,S,Chemical derivative
-ptm=[734]Ethanolamine,43.042199,opt,D,Chemical derivative
-ptm=[734]Ethanolamine,43.042199,opt,C-term,Chemical derivative
-ptm=[734]Ethanolamine,43.042199,opt,E,Chemical derivative
-ptm=[734]Ethanolamine,43.042199,opt,C,Chemical derivative
-ptm=[737]TMT6plex,229.162932,opt,T,Isotopic label
-ptm=[737]TMT6plex,229.162932,opt,S,Isotopic label
-ptm=[737]TMT6plex,229.162932,opt,H,Isotopic label
-ptm=[737]TMT6plex,229.162932,opt,N-term,Isotopic label
-ptm=[737]TMT6plex,229.162932,opt,K,Isotopic label
-ptm=[736]BEMAD_C,120.0245,opt,C,Chemical derivative
-ptm=[738]TMT2plex,225.155833,opt,H,Isotopic label
-ptm=[738]TMT2plex,225.155833,opt,S,Isotopic label
-ptm=[738]TMT2plex,225.155833,opt,T,Isotopic label
-ptm=[738]TMT2plex,225.155833,opt,N-term,Isotopic label
-ptm=[738]TMT2plex,225.155833,opt,K,Isotopic label
-ptm=[739]TMT,224.152478,opt,N-term,Chemical derivative
-ptm=[739]TMT,224.152478,opt,K,Chemical derivative
-ptm=[739]TMT,224.152478,opt,H,Isotopic label
-ptm=[739]TMT,224.152478,opt,S,Isotopic label
-ptm=[739]TMT,224.152478,opt,T,Isotopic label
-ptm=[740]ExacTagThiol,972.365219,opt,C,Isotopic label
-ptm=[741]ExacTagAmine,1046.347854,opt,K,Isotopic label
-ptm=[744]NO_SMX_SEMD,252.044287,opt,C,Chemical derivative
-ptm=[743]4-ONE+Delta:H(-2)O(-1),136.088815,opt,K,Chemical derivative
-ptm=[743]4-ONE+Delta:H(-2)O(-1),136.088815,opt,H,Chemical derivative
-ptm=[743]4-ONE+Delta:H(-2)O(-1),136.088815,opt,C,Chemical derivative
-ptm=[745]NO_SMX_SMCT,268.039202,opt,C,Chemical derivative
-ptm=[746]NO_SMX_SIMD,267.031377,opt,C,Chemical derivative
-ptm=[747]Malonyl,86.000394,opt,C,Chemical derivative
-ptm=[747]Malonyl,86.000394,opt,S,Chemical derivative
-ptm=[747]Malonyl,86.000394,opt,K,Post-translational
-ptm=[748]3sulfo,183.983029,opt,N-term,Chemical derivative
-ptm=[751]TNBS,210.986535,opt,N-term,Chemical derivative
-ptm=[751]TNBS,210.986535,opt,K,Chemical derivative
-ptm=[774]Biotin-phenacyl,626.263502,opt,C,Chemical derivative
-ptm=[774]Biotin-phenacyl,626.263502,opt,H,Chemical derivative
-ptm=[774]Biotin-phenacyl,626.263502,opt,S,Chemical derivative
-ptm=[764]BEMAD_C:2H(6),126.062161,opt,C,Isotopic label
-ptm=[771]lapachenole,240.11503,opt,C,Chemical derivative
-ptm=[772]Label:13C(5),5.016774,opt,P,Isotopic label
-ptm=[773]maleimide,97.016378,opt,K,Chemical derivative
-ptm=[773]maleimide,97.016378,opt,C,Chemical derivative
-ptm=[762]IDEnT,214.990469,opt,C,Isotopic label
-ptm=[763]BEMAD_ST:2H(6),142.039317,opt,T,Isotopic label
-ptm=[763]BEMAD_ST:2H(6),142.039317,opt,S,Isotopic label
-ptm=[765]Met-loss,-131.040485,opt,M,Co-translational
-ptm=[766]Met-loss+Acetyl,-89.02992,opt,M,Co-translational
-ptm=[767]Menadione-HQ,172.05243,opt,K,Chemical derivative
-ptm=[767]Menadione-HQ,172.05243,opt,C,Chemical derivative
-ptm=[775]Carboxymethyl:13C(2),60.012189,opt,C,Chemical derivative
-ptm=[776]NEM:2H(5),130.079062,opt,C,Chemical derivative
-ptm=[822]Gly-loss+Amide,-58.005479,opt,G,Post-translational
-ptm=[827]TMPP-Ac,572.181134,opt,N-term,Chemical derivative
-ptm=[799]Label:13C(6)+GG,120.063056,opt,K,Isotopic label
-ptm=[837]Arg->Npo,80.985078,opt,R,Chemical derivative
-ptm=[834]Label:2H(4)+Acetyl,46.035672,opt,K,Isotopic label
-ptm=[801]Pentylamine,85.089149,opt,Q,Chemical derivative
-ptm=[800]Biotin:Thermo-21345,311.166748,opt,Q,Chemical derivative
-ptm=[830]Dihydroxyimidazolidine,72.021129,opt,R,Multiple
-ptm=[825]Xlink:DFDNB,163.985807,opt,N,Chemical derivative
-ptm=[825]Xlink:DFDNB,163.985807,opt,Q,Chemical derivative
-ptm=[825]Xlink:DFDNB,163.985807,opt,R,Chemical derivative
-ptm=[825]Xlink:DFDNB,163.985807,opt,K,Chemical derivative
-ptm=[821]Cy3b-maleimide,682.24612,opt,C,Chemical derivative
-ptm=[792]AEC-MAEC:2H(4),63.044462,opt,S,Isotopic label
-ptm=[792]AEC-MAEC:2H(4),63.044462,opt,T,Isotopic label
-ptm=[824]Xlink:BMOE,220.048407,opt,C,Chemical derivative
-ptm=[811]Biotin:Thermo-21360,487.246455,opt,C-term,Chemical derivative
-ptm=[835]Label:13C(6)+Acetyl,48.030694,opt,K,Isotopic label
-ptm=[836]Label:13C(6)15N(2)+Acetyl,50.024764,opt,K,Isotopic label
-ptm=[849]cGMP,343.031785,opt,S,Post-translational
-ptm=[849]cGMP,343.031785,opt,C,Post-translational
-ptm=[851]cGMP+RMP-loss,150.041585,opt,C,Post-translational
-ptm=[851]cGMP+RMP-loss,150.041585,opt,S,Post-translational
-ptm=[888]mTRAQ,140.094963,opt,Y,Isotopic label
-ptm=[888]mTRAQ,140.094963,opt,N-term,Isotopic label
-ptm=[888]mTRAQ,140.094963,opt,K,Isotopic label
-ptm=[888]mTRAQ,140.094963,opt,H,Isotopic label
-ptm=[888]mTRAQ,140.094963,opt,S,Isotopic label
-ptm=[888]mTRAQ,140.094963,opt,T,Isotopic label
-ptm=[848]Arg2PG,266.057909,opt,R,Chemical derivative
-ptm=[853]Label:2H(4)+GG,118.068034,opt,K,Post-translational
-ptm=[1420]spermine,185.189198,opt,Q,Chemical derivative
-ptm=[862]Label:13C(1)2H(3),4.022185,opt,M,Isotopic label
-ptm=[864]Label:13C(6)15N(2)+GG,122.057126,opt,K,Isotopic label
-ptm=[866]ICPL:13C(6)2H(4),115.0667,opt,N-term,Isotopic label
-ptm=[866]ICPL:13C(6)2H(4),115.0667,opt,K,Isotopic label
-ptm=[890]DyLight-maleimide,940.1999,opt,C,Chemical derivative
-ptm=[889]mTRAQ:13C(3)15N(1),144.102063,opt,S,Isotopic label
-ptm=[889]mTRAQ:13C(3)15N(1),144.102063,opt,T,Isotopic label
-ptm=[889]mTRAQ:13C(3)15N(1),144.102063,opt,H,Isotopic label
-ptm=[889]mTRAQ:13C(3)15N(1),144.102063,opt,Y,Isotopic label
-ptm=[889]mTRAQ:13C(3)15N(1),144.102063,opt,N-term,Isotopic label
-ptm=[889]mTRAQ:13C(3)15N(1),144.102063,opt,K,Isotopic label
-ptm=[891]Methyl-PEO12-Maleimide,710.383719,opt,C,Chemical derivative
-ptm=[887]MDCC,383.148121,opt,C,Chemical derivative
-ptm=[884]Biotin:Thermo-21325,695.310118,opt,K,Chemical derivative
-ptm=[885]Label:13C(1)2H(3)+Oxidation,20.0171,opt,M,Multiple
-ptm=[878]Bodipy,414.167478,opt,C,Chemical derivative
-ptm=[895]Biotin-PEG-PRA,578.317646,opt,M,Chemical derivative
-ptm=[897]Label:15N(4),3.98814,opt,R,Isotopic label
-ptm=[898]pyrophospho,159.932662,opt,T,Post-translational
-ptm=[898]pyrophospho,159.932662,opt,S,Post-translational
-ptm=[901]4AcAllylGal,372.142033,opt,C,Chemical derivative
-ptm=[902]DimethylArsino,103.960719,opt,C,Post-translational
-ptm=[903]Lys->CamCys,31.935685,opt,K,Pre-translational
-ptm=[904]Phe->CamCys,12.962234,opt,F,Pre-translational
-ptm=[905]Leu->MetOx,33.951335,opt,L,Pre-translational
-ptm=[906]Lys->MetOx,18.940436,opt,K,Pre-translational
-ptm=[908]Xlink:SMCC[321],321.205242,opt,C,Chemical derivative
-ptm=[911]MTSL,184.07961,opt,C,Chemical derivative
-ptm=[912]HNE-BAHAH,511.319226,opt,H,Chemical derivative
-ptm=[912]HNE-BAHAH,511.319226,opt,C,Chemical derivative
-ptm=[912]HNE-BAHAH,511.319226,opt,K,Chemical derivative
-ptm=[915]Ethoxyformyl,72.021129,opt,H,Chemical derivative
-ptm=[914]Methylmalonylation,100.016044,opt,S,Chemical derivative
-ptm=[938]AROD,820.336015,opt,C,Chemical derivative
-ptm=[939]Cys->methylaminoAla,-2.945522,opt,C,Chemical derivative
-ptm=[940]Cys->ethylaminoAla,11.070128,opt,C,Chemical derivative
-ptm=[923]Label:13C(4)15N(2)+GG,120.050417,opt,K,Isotopic label
-ptm=[926]ethylamino,27.047285,opt,S,Chemical derivative
-ptm=[926]ethylamino,27.047285,opt,T,Chemical derivative
-ptm=[928]MercaptoEthanol,60.003371,opt,S,Chemical derivative
-ptm=[928]MercaptoEthanol,60.003371,opt,T,Chemical derivative
-ptm=[935]Atto495Maleimide,474.250515,opt,C,Chemical derivative
-ptm=[934]AMTzHexNAc2,502.202341,opt,T,Chemical derivative
-ptm=[934]AMTzHexNAc2,502.202341,opt,S,Chemical derivative
-ptm=[934]AMTzHexNAc2,502.202341,opt,N,Chemical derivative
-ptm=[931]Ethyl+Deamidated,29.015316,opt,Q,Chemical derivative
-ptm=[931]Ethyl+Deamidated,29.015316,opt,N,Chemical derivative
-ptm=[936]Chlorination,33.961028,opt,W,Artefact
-ptm=[936]Chlorination,33.961028,opt,Y,Artefact
-ptm=[937]dichlorination,67.922055,opt,C,Chemical derivative
-ptm=[937]dichlorination,67.922055,opt,Y,Artefact
-ptm=[941]DNPS,198.981352,opt,C,Chemical derivative
-ptm=[941]DNPS,198.981352,opt,W,Chemical derivative
-ptm=[943]DimethylamineGMBS,267.158292,opt,C,Chemical derivative
-ptm=[944]Label:15N(2)2H(9),11.050561,opt,K,Isotopic label
-ptm=[946]LG-anhydrolactam,314.188195,opt,N-term,Post-translational
-ptm=[946]LG-anhydrolactam,314.188195,opt,K,Post-translational
-ptm=[947]LG-pyrrole,316.203845,opt,N-term,Post-translational
-ptm=[947]LG-pyrrole,316.203845,opt,K,Post-translational
-ptm=[948]LG-anhyropyrrole,298.19328,opt,N-term,Post-translational
-ptm=[948]LG-anhyropyrrole,298.19328,opt,K,Post-translational
-ptm=[949]3-deoxyglucosone,144.042259,opt,R,Multiple
-ptm=[950]Cation:Li,6.008178,opt,D,Artefact
-ptm=[950]Cation:Li,6.008178,opt,E,Artefact
-ptm=[950]Cation:Li,6.008178,opt,C-term,Artefact
-ptm=[951]Cation:Ca[II],37.946941,opt,C-term,Artefact
-ptm=[951]Cation:Ca[II],37.946941,opt,E,Artefact
-ptm=[951]Cation:Ca[II],37.946941,opt,D,Artefact
-ptm=[952]Cation:Fe[II],53.919289,opt,D,Artefact
-ptm=[952]Cation:Fe[II],53.919289,opt,E,Artefact
-ptm=[952]Cation:Fe[II],53.919289,opt,C-term,Artefact
-ptm=[953]Cation:Ni[II],55.919696,opt,D,Artefact
-ptm=[953]Cation:Ni[II],55.919696,opt,E,Artefact
-ptm=[953]Cation:Ni[II],55.919696,opt,C-term,Artefact
-ptm=[954]Cation:Zn[II],61.913495,opt,C-term,Artefact
-ptm=[954]Cation:Zn[II],61.913495,opt,E,Artefact
-ptm=[954]Cation:Zn[II],61.913495,opt,D,Artefact
-ptm=[954]Cation:Zn[II],61.913495,opt,H,Artefact
-ptm=[955]Cation:Ag,105.897267,opt,D,Artefact
-ptm=[955]Cation:Ag,105.897267,opt,E,Artefact
-ptm=[955]Cation:Ag,105.897267,opt,C-term,Artefact
-ptm=[956]Cation:Mg[II],21.969392,opt,D,Artefact
-ptm=[956]Cation:Mg[II],21.969392,opt,E,Artefact
-ptm=[956]Cation:Mg[II],21.969392,opt,C-term,Artefact
-ptm=[957]2-succinyl,116.010959,opt,C,Chemical derivative
-ptm=[958]Propargylamine,37.031634,opt,D,Chemical derivative
-ptm=[958]Propargylamine,37.031634,opt,C-term,Chemical derivative
-ptm=[958]Propargylamine,37.031634,opt,E,Chemical derivative
-ptm=[959]Phosphopropargyl,116.997965,opt,T,Multiple
-ptm=[959]Phosphopropargyl,116.997965,opt,Y,Multiple
-ptm=[959]Phosphopropargyl,116.997965,opt,S,Multiple
-ptm=[1992]serotonylation,159.068414,opt,Q,Post-translational
-ptm=[978]BITC,149.02992,opt,N-term,Chemical derivative
-ptm=[978]BITC,149.02992,opt,K,Chemical derivative
-ptm=[978]BITC,149.02992,opt,C,Chemical derivative
-ptm=[977]Carbofuran,58.029289,opt,S,Chemical derivative
-ptm=[979]PEITC,163.04557,opt,N-term,Chemical derivative
-ptm=[979]PEITC,163.04557,opt,K,Chemical derivative
-ptm=[979]PEITC,163.04557,opt,C,Chemical derivative
-ptm=[967]thioacylPA,159.035399,opt,K,Chemical derivative
-ptm=[971]maleimide3,969.366232,opt,K,Post-translational
-ptm=[971]maleimide3,969.366232,opt,C,Post-translational
-ptm=[972]maleimide5,1293.471879,opt,K,Post-translational
-ptm=[972]maleimide5,1293.471879,opt,C,Post-translational
-ptm=[973]Puromycin,453.212452,opt,C-term,Co-translational
-ptm=[986]Label:13C(6)+Dimethyl,34.051429,opt,K,Isotopic label
-ptm=[984]cysTMT,299.166748,opt,C,Chemical derivative
-ptm=[985]cysTMT6plex,304.177202,opt,C,Isotopic label
-ptm=[991]ISD_z+2_ion,-15.010899,opt,N-term,Artefact
-ptm=[989]Ammonium,17.026549,opt,E,Artefact
-ptm=[989]Ammonium,17.026549,opt,D,Artefact
-ptm=[989]Ammonium,17.026549,opt,C-term,Artefact
-ptm=[993]Biotin:Sigma-B1267,449.17329,opt,C,Chemical derivative
-ptm=[994]Label:15N(1),0.997035,opt,M,Isotopic label
-ptm=[994]Label:15N(1),0.997035,opt,E,Isotopic label
-ptm=[994]Label:15N(1),0.997035,opt,D,Isotopic label
-ptm=[994]Label:15N(1),0.997035,opt,L,Isotopic label
-ptm=[994]Label:15N(1),0.997035,opt,I,Isotopic label
-ptm=[994]Label:15N(1),0.997035,opt,C,Isotopic label
-ptm=[994]Label:15N(1),0.997035,opt,T,Isotopic label
-ptm=[994]Label:15N(1),0.997035,opt,V,Isotopic label
-ptm=[994]Label:15N(1),0.997035,opt,P,Isotopic label
-ptm=[994]Label:15N(1),0.997035,opt,S,Isotopic label
-ptm=[994]Label:15N(1),0.997035,opt,A,Isotopic label
-ptm=[994]Label:15N(1),0.997035,opt,G,Isotopic label
-ptm=[994]Label:15N(1),0.997035,opt,Y,Isotopic label
-ptm=[994]Label:15N(1),0.997035,opt,F,Isotopic label
-ptm=[995]Label:15N(2),1.99407,opt,W,Isotopic label
-ptm=[995]Label:15N(2),1.99407,opt,K,Isotopic label
-ptm=[995]Label:15N(2),1.99407,opt,Q,Isotopic label
-ptm=[995]Label:15N(2),1.99407,opt,N,Isotopic label
-ptm=[996]Label:15N(3),2.991105,opt,H,Isotopic label
-ptm=[997]sulfo+amino,94.967714,opt,Y,Chemical derivative
-ptm=[1000]AHA-Alkyne,107.077339,opt,M,Chemical derivative
-ptm=[1001]AHA-Alkyne-KDDDD,695.280074,opt,M,Chemical derivative
-ptm=[1002]EGCG1,456.069261,opt,C,Post-translational
-ptm=[1003]EGCG2,287.055563,opt,C,Post-translational
-ptm=[1004]Label:13C(6)15N(4)+Methyl,24.023919,opt,R,Isotopic label
-ptm=[1005]Label:13C(6)15N(4)+Dimethyl,38.039569,opt,R,Isotopic label
-ptm=[1006]Label:13C(6)15N(4)+Methyl:2H(3)13C(1),28.046104,opt,R,Isotopic label
-ptm=[1007]Label:13C(6)15N(4)+Dimethyl:2H(6)13C(2),46.083939,opt,R,Isotopic label
-ptm=[1009]Thiazolidine,12.0,opt,W,Chemical derivative
-ptm=[1009]Thiazolidine,12.0,opt,Y,Chemical derivative
-ptm=[1009]Thiazolidine,12.0,opt,H,Chemical derivative
-ptm=[1009]Thiazolidine,12.0,opt,R,Chemical derivative
-ptm=[1009]Thiazolidine,12.0,opt,K,Chemical derivative
-ptm=[1009]Thiazolidine,12.0,opt,N-term,Chemical derivative
-ptm=[1009]Thiazolidine,12.0,opt,C,Chemical derivative
-ptm=[1009]Thiazolidine,12.0,opt,F,Chemical derivative
-ptm=[1010]DEDGFLYMVYASQETFG,1970.824411,opt,K,Post-translational
-ptm=[1012]Biotin:Invitrogen-M1602,523.210069,opt,C,Chemical derivative
-ptm=[1020]Xlink:DSS[156],156.078644,opt,K,Chemical derivative
-ptm=[1020]Xlink:DSS[156],156.078644,opt,N-term,Chemical derivative
-ptm=[1017]DMPO,111.068414,opt,H,Post-translational
-ptm=[1017]DMPO,111.068414,opt,Y,Post-translational
-ptm=[1017]DMPO,111.068414,opt,C,Post-translational
-ptm=[1014]glycidamide,87.032028,opt,K,Chemical derivative
-ptm=[1014]glycidamide,87.032028,opt,N-term,Chemical derivative
-ptm=[1018]ICDID,138.06808,opt,C,Isotopic label
-ptm=[1019]ICDID:2H(6),144.10574,opt,C,Isotopic label
-ptm=[1021]Xlink:EGS[244],244.058303,opt,N-term,Chemical derivative
-ptm=[1021]Xlink:EGS[244],244.058303,opt,K,Chemical derivative
-ptm=[1022]Xlink:DST[132],132.005873,opt,N-term,Chemical derivative
-ptm=[1022]Xlink:DST[132],132.005873,opt,K,Chemical derivative
-ptm=[1023]Xlink:DTSSP[192],191.991486,opt,N-term,Chemical derivative
-ptm=[1023]Xlink:DTSSP[192],191.991486,opt,K,Chemical derivative
-ptm=[1024]Xlink:SMCC[237],237.100108,opt,C,Chemical derivative
-ptm=[1024]Xlink:SMCC[237],237.100108,opt,K,Chemical derivative
-ptm=[1024]Xlink:SMCC[237],237.100108,opt,N-term,Chemical derivative
-ptm=[1032]2-nitrobenzyl,135.032028,opt,Y,Chemical derivative
-ptm=[1027]Xlink:DMP[140],140.094963,opt,N-term,Chemical derivative
-ptm=[1027]Xlink:DMP[140],140.094963,opt,K,Chemical derivative
-ptm=[1028]Xlink:EGS[115],115.026943,opt,N-term,Chemical derivative
-ptm=[1028]Xlink:EGS[115],115.026943,opt,K,Chemical derivative
-ptm=[1034]Cys->SecNEM:2H(5),178.023511,opt,C,Chemical derivative
-ptm=[1035]Thiadiazole,174.025169,opt,C,Chemical derivative
-ptm=[1031]Biotin:Thermo-88310,196.121178,opt,K,Chemical derivative
-ptm=[1038]TAMRA-FP,659.312423,opt,Y,Chemical derivative
-ptm=[1038]TAMRA-FP,659.312423,opt,S,Chemical derivative
-ptm=[1039]Biotin:Thermo-21901+H2O,543.236284,opt,C,Chemical derivative
-ptm=[1041]Deoxyhypusine,71.073499,opt,Q,Chemical derivative
-ptm=[1041]Deoxyhypusine,71.073499,opt,K,Post-translational
-ptm=[1042]Acetyldeoxyhypusine,97.089149,opt,K,Post-translational
-ptm=[1043]Acetylhypusine,113.084064,opt,K,Post-translational
-ptm=[1249]AHA-SS,195.075625,opt,M,Multiple
-ptm=[1250]AHA-SS_CAM,252.097088,opt,M,Multiple
-ptm=[1251]Biotin:Thermo-33033,548.223945,opt,N-term,Chemical derivative
-ptm=[1252]Biotin:Thermo-33033-H,546.208295,opt,N-term,Chemical derivative
-ptm=[1253]2-monomethylsuccinyl,130.026609,opt,C,Chemical derivative
-ptm=[1254]Saligenin,106.041865,opt,H,Chemical derivative
-ptm=[1254]Saligenin,106.041865,opt,K,Chemical derivative
-ptm=[1255]Cresylphosphate,170.013281,opt,R,Chemical derivative
-ptm=[1255]Cresylphosphate,170.013281,opt,S,Chemical derivative
-ptm=[1255]Cresylphosphate,170.013281,opt,T,Chemical derivative
-ptm=[1255]Cresylphosphate,170.013281,opt,Y,Chemical derivative
-ptm=[1255]Cresylphosphate,170.013281,opt,K,Chemical derivative
-ptm=[1255]Cresylphosphate,170.013281,opt,H,Chemical derivative
-ptm=[1256]CresylSaligeninPhosphate,276.055146,opt,R,Chemical derivative
-ptm=[1256]CresylSaligeninPhosphate,276.055146,opt,S,Chemical derivative
-ptm=[1256]CresylSaligeninPhosphate,276.055146,opt,T,Chemical derivative
-ptm=[1256]CresylSaligeninPhosphate,276.055146,opt,Y,Chemical derivative
-ptm=[1256]CresylSaligeninPhosphate,276.055146,opt,K,Chemical derivative
-ptm=[1256]CresylSaligeninPhosphate,276.055146,opt,H,Chemical derivative
-ptm=[1257]Ub-Br2,100.063663,opt,C,Chemical derivative
-ptm=[1258]Ub-VME,173.092617,opt,C,Chemical derivative
-ptm=[1260]Ub-amide,196.108602,opt,C,Chemical derivative
-ptm=[1261]Ub-fluorescein,597.209772,opt,C,Chemical derivative
-ptm=[1262]2-dimethylsuccinyl,144.042259,opt,C,Chemical derivative
-ptm=[1264]pupylation,243.085521,opt,K,Post-translational
-ptm=[1266]Label:13C(4),4.013419,opt,M,Isotopic label
-ptm=[1271]HCysteinyl,133.019749,opt,C,Post-translational
-ptm=[1267]Label:13C(4)+Oxidation,20.008334,opt,M,Isotopic label
-ptm=[1276]UgiJoullie,1106.48935,opt,E,Chemical derivative
-ptm=[1276]UgiJoullie,1106.48935,opt,D,Chemical derivative
-ptm=[1270]HCysThiolactone,117.024835,opt,K,Post-translational
-ptm=[1282]UgiJoullieProGly,154.074228,opt,D,Chemical derivative
-ptm=[1282]UgiJoullieProGly,154.074228,opt,E,Chemical derivative
-ptm=[1277]Dipyridyl,225.090212,opt,C,Chemical derivative
-ptm=[1278]Furan,66.010565,opt,Y,Chemical derivative
-ptm=[1279]Difuran,132.021129,opt,Y,Chemical derivative
-ptm=[1281]BMP-piperidinol,263.131014,opt,C,Chemical derivative
-ptm=[1281]BMP-piperidinol,263.131014,opt,M,Chemical derivative
-ptm=[1283]UgiJoullieProGlyProGly,308.148455,opt,D,Chemical derivative
-ptm=[1283]UgiJoullieProGlyProGly,308.148455,opt,E,Chemical derivative
-ptm=[1289]Butyryl,70.041865,opt,K,Post-translational
-ptm=[1290]Dicarbamidomethyl,114.042927,opt,K,Artefact
-ptm=[1290]Dicarbamidomethyl,114.042927,opt,H,Artefact
-ptm=[1290]Dicarbamidomethyl,114.042927,opt,C,Artefact
-ptm=[1290]Dicarbamidomethyl,114.042927,opt,R,Artefact
-ptm=[1290]Dicarbamidomethyl,114.042927,opt,N-term,Artefact
-ptm=[1291]Dimethyl:2H(6),34.068961,opt,K,Isotopic label
-ptm=[1291]Dimethyl:2H(6),34.068961,opt,N-term,Isotopic label
-ptm=[1291]Dimethyl:2H(6),34.068961,opt,R,Isotopic label
-ptm=[1297]Label:13C(3)15N(1),4.007099,opt,A,Isotopic label
-ptm=[1296]Label:13C(3),3.010064,opt,A,Isotopic label
-ptm=[1298]Label:13C(4)15N(1),5.010454,opt,D,Isotopic label
-ptm=[1299]Label:2H(10),10.062767,opt,L,Isotopic label
-ptm=[1300]Label:2H(4)13C(1),5.028462,opt,R,Isotopic label
-ptm=[1302]mTRAQ:13C(6)15N(2),148.109162,opt,K,Isotopic label
-ptm=[1302]mTRAQ:13C(6)15N(2),148.109162,opt,N-term,Isotopic label
-ptm=[1302]mTRAQ:13C(6)15N(2),148.109162,opt,Y,Isotopic label
-ptm=[1302]mTRAQ:13C(6)15N(2),148.109162,opt,H,Isotopic label
-ptm=[1302]mTRAQ:13C(6)15N(2),148.109162,opt,S,Isotopic label
-ptm=[1302]mTRAQ:13C(6)15N(2),148.109162,opt,T,Isotopic label
-ptm=[1305]Propyl,42.04695,opt,D,Chemical derivative
-ptm=[1305]Propyl,42.04695,opt,K,Isotopic label
-ptm=[1305]Propyl,42.04695,opt,N-term,Isotopic label
-ptm=[1305]Propyl,42.04695,opt,E,Chemical derivative
-ptm=[1305]Propyl,42.04695,opt,C-term,Chemical derivative
-ptm=[1306]Propyl:2H(6),48.084611,opt,N-term,Isotopic label
-ptm=[1306]Propyl:2H(6),48.084611,opt,K,Isotopic label
-ptm=[1310]Propiophenone,132.057515,opt,C,Chemical derivative
-ptm=[1310]Propiophenone,132.057515,opt,W,Chemical derivative
-ptm=[1310]Propiophenone,132.057515,opt,T,Chemical derivative
-ptm=[1310]Propiophenone,132.057515,opt,S,Chemical derivative
-ptm=[1310]Propiophenone,132.057515,opt,R,Chemical derivative
-ptm=[1310]Propiophenone,132.057515,opt,K,Chemical derivative
-ptm=[1310]Propiophenone,132.057515,opt,H,Chemical derivative
-ptm=[1345]PS_Hapten,120.021129,opt,H,Chemical derivative
-ptm=[1345]PS_Hapten,120.021129,opt,C,Chemical derivative
-ptm=[1345]PS_Hapten,120.021129,opt,K,Chemical derivative
-ptm=[1348]Cy3-maleimide,753.262796,opt,C,Chemical derivative
-ptm=[1312]Delta:H(6)C(3)O(1),58.041865,opt,N-term,Chemical derivative
-ptm=[1312]Delta:H(6)C(3)O(1),58.041865,opt,K,Chemical derivative
-ptm=[1312]Delta:H(6)C(3)O(1),58.041865,opt,H,Chemical derivative
-ptm=[1312]Delta:H(6)C(3)O(1),58.041865,opt,C,Chemical derivative
-ptm=[1313]Delta:H(8)C(6)O(1),96.057515,opt,N-term,Chemical derivative
-ptm=[1313]Delta:H(8)C(6)O(1),96.057515,opt,K,Chemical derivative
-ptm=[1314]biotinAcrolein298,298.146347,opt,H,Chemical derivative
-ptm=[1314]biotinAcrolein298,298.146347,opt,K,Chemical derivative
-ptm=[1314]biotinAcrolein298,298.146347,opt,N-term,Chemical derivative
-ptm=[1314]biotinAcrolein298,298.146347,opt,C,Chemical derivative
-ptm=[1315]MM-diphenylpentanone,265.146664,opt,C,Chemical derivative
-ptm=[1317]EHD-diphenylpentanone,266.13068,opt,M,Chemical derivative
-ptm=[1317]EHD-diphenylpentanone,266.13068,opt,C,Chemical derivative
-ptm=[1349]benzylguanidine,132.068748,opt,K,Chemical derivative
-ptm=[1350]CarboxymethylDMAP,162.079313,opt,N-term,Chemical derivative
-ptm=[1320]Biotin:Thermo-21901+2H2O,561.246849,opt,C,Chemical derivative
-ptm=[1321]DiLeu4plex115,145.12,opt,K,Isotopic label
-ptm=[1321]DiLeu4plex115,145.12,opt,N-term,Isotopic label
-ptm=[1321]DiLeu4plex115,145.12,opt,Y,Isotopic label
-ptm=[1322]DiLeu4plex,145.132163,opt,N-term,Isotopic label
-ptm=[1322]DiLeu4plex,145.132163,opt,K,Isotopic label
-ptm=[1322]DiLeu4plex,145.132163,opt,Y,Isotopic label
-ptm=[1323]DiLeu4plex117,145.128307,opt,K,Isotopic label
-ptm=[1323]DiLeu4plex117,145.128307,opt,N-term,Isotopic label
-ptm=[1323]DiLeu4plex117,145.128307,opt,Y,Isotopic label
-ptm=[1324]DiLeu4plex118,145.140471,opt,K,Isotopic label
-ptm=[1324]DiLeu4plex118,145.140471,opt,N-term,Isotopic label
-ptm=[1324]DiLeu4plex118,145.140471,opt,Y,Isotopic label
-ptm=[1887]Xlink:BuUrBu[213],213.111341,opt,K,Chemical derivative
-ptm=[1887]Xlink:BuUrBu[213],213.111341,opt,N-term,Chemical derivative
-ptm=[1330]bisANS-sulfonates,437.201774,opt,S,Chemical derivative
-ptm=[1330]bisANS-sulfonates,437.201774,opt,T,Chemical derivative
-ptm=[1330]bisANS-sulfonates,437.201774,opt,K,Chemical derivative
-ptm=[1331]DNCB_hapten,166.001457,opt,Y,Chemical derivative
-ptm=[1331]DNCB_hapten,166.001457,opt,H,Chemical derivative
-ptm=[1331]DNCB_hapten,166.001457,opt,K,Chemical derivative
-ptm=[1331]DNCB_hapten,166.001457,opt,C,Chemical derivative
-ptm=[1326]NEMsulfur,157.019749,opt,C,Chemical derivative
-ptm=[1327]SulfurDioxide,63.9619,opt,C,Post-translational
-ptm=[1328]NEMsulfurWater,175.030314,opt,C,Chemical derivative
-ptm=[1389]HN3_mustard,131.094629,opt,C,Post-translational
-ptm=[1389]HN3_mustard,131.094629,opt,H,Post-translational
-ptm=[1389]HN3_mustard,131.094629,opt,K,Post-translational
-ptm=[1387]3-phosphoglyceryl,167.982375,opt,K,Post-translational
-ptm=[1388]HN2_mustard,101.084064,opt,H,Post-translational
-ptm=[1388]HN2_mustard,101.084064,opt,K,Post-translational
-ptm=[1388]HN2_mustard,101.084064,opt,C,Post-translational
-ptm=[1358]NEM:2H(5)+H2O,148.089627,opt,C,Chemical derivative
-ptm=[1363]Crotonyl,68.026215,opt,K,Post-translational
-ptm=[1364]O-Et-N-diMePhospho,135.044916,opt,S,Chemical derivative
-ptm=[1365]N-dimethylphosphate,107.013615,opt,S,Chemical derivative
-ptm=[1356]phosphoRibosyl,212.00859,opt,E,Post-translational
-ptm=[1356]phosphoRibosyl,212.00859,opt,R,Post-translational
-ptm=[1356]phosphoRibosyl,212.00859,opt,D,Post-translational
-ptm=[1355]azole,-20.026215,opt,C,Post-translational
-ptm=[1355]azole,-20.026215,opt,S,Post-translational
-ptm=[1340]Biotin:Thermo-21911,921.461652,opt,C,Chemical derivative
-ptm=[1341]iodoTMT,324.216141,opt,K,Chemical derivative
-ptm=[1341]iodoTMT,324.216141,opt,H,Chemical derivative
-ptm=[1341]iodoTMT,324.216141,opt,E,Chemical derivative
-ptm=[1341]iodoTMT,324.216141,opt,D,Chemical derivative
-ptm=[1341]iodoTMT,324.216141,opt,C,Chemical derivative
-ptm=[1342]iodoTMT6plex,329.226595,opt,K,Chemical derivative
-ptm=[1342]iodoTMT6plex,329.226595,opt,H,Chemical derivative
-ptm=[1342]iodoTMT6plex,329.226595,opt,E,Chemical derivative
-ptm=[1342]iodoTMT6plex,329.226595,opt,D,Chemical derivative
-ptm=[1342]iodoTMT6plex,329.226595,opt,C,Chemical derivative
-ptm=[1787]Label:13C(2)15N(2),4.00078,opt,K,Isotopic label
-ptm=[1344]Phosphogluconoylation,258.014069,opt,N-term,Post-translational
-ptm=[1344]Phosphogluconoylation,258.014069,opt,K,Post-translational
-ptm=[1368]Methyl:2H(3)+Acetyl:2H(3),62.063875,opt,K,Isotopic label
-ptm=[1380]methylsulfonylethyl,106.00885,opt,K,Chemical derivative
-ptm=[1380]methylsulfonylethyl,106.00885,opt,H,Chemical derivative
-ptm=[1380]methylsulfonylethyl,106.00885,opt,C,Chemical derivative
-ptm=[1370]Label:2H(3)+Oxidation,19.013745,opt,M,Isotopic label
-ptm=[1371]Trimethyl:2H(9),51.103441,opt,R,Isotopic label
-ptm=[1371]Trimethyl:2H(9),51.103441,opt,K,Isotopic label
-ptm=[1372]Acetyl:13C(2),44.017274,opt,K,Isotopic label
-ptm=[1372]Acetyl:13C(2),44.017274,opt,N-term,Isotopic label
-ptm=[1381]ethylsulfonylethyl,120.0245,opt,H,Chemical derivative
-ptm=[1381]ethylsulfonylethyl,120.0245,opt,C,Chemical derivative
-ptm=[1381]ethylsulfonylethyl,120.0245,opt,K,Chemical derivative
-ptm=[1382]phenylsulfonylethyl,168.0245,opt,C,Chemical derivative
-ptm=[1383]PyridoxalPhosphateH2,231.02966,opt,K,Chemical derivative
-ptm=[1384]Homocysteic_acid,33.969094,opt,M,Artefact
-ptm=[1385]Hydroxamic_acid,15.010899,opt,E,Artefact
-ptm=[1385]Hydroxamic_acid,15.010899,opt,D,Artefact
-ptm=[1390]Oxidation+NEM,141.042593,opt,C,Chemical derivative
-ptm=[1391]NHS-fluorescein,471.131802,opt,K,Chemical derivative
-ptm=[1392]DiART6plex,217.162932,opt,Y,Isotopic label
-ptm=[1392]DiART6plex,217.162932,opt,N-term,Isotopic label
-ptm=[1392]DiART6plex,217.162932,opt,K,Isotopic label
-ptm=[1393]DiART6plex115,217.156612,opt,K,Isotopic label
-ptm=[1393]DiART6plex115,217.156612,opt,N-term,Isotopic label
-ptm=[1393]DiART6plex115,217.156612,opt,Y,Isotopic label
-ptm=[1394]DiART6plex116/119,217.168776,opt,Y,Isotopic label
-ptm=[1394]DiART6plex116/119,217.168776,opt,N-term,Isotopic label
-ptm=[1394]DiART6plex116/119,217.168776,opt,K,Isotopic label
-ptm=[1395]DiART6plex117,217.162456,opt,K,Isotopic label
-ptm=[1395]DiART6plex117,217.162456,opt,N-term,Isotopic label
-ptm=[1395]DiART6plex117,217.162456,opt,Y,Isotopic label
-ptm=[1396]DiART6plex118,217.175096,opt,K,Isotopic label
-ptm=[1396]DiART6plex118,217.175096,opt,N-term,Isotopic label
-ptm=[1396]DiART6plex118,217.175096,opt,Y,Isotopic label
-ptm=[1397]Iodoacetanilide,133.052764,opt,K,Artefact
-ptm=[1397]Iodoacetanilide,133.052764,opt,C,Chemical derivative
-ptm=[1397]Iodoacetanilide,133.052764,opt,N-term,Artefact
-ptm=[1398]Iodoacetanilide:13C(6),139.072893,opt,K,Artefact
-ptm=[1398]Iodoacetanilide:13C(6),139.072893,opt,C,Chemical derivative
-ptm=[1398]Iodoacetanilide:13C(6),139.072893,opt,N-term,Artefact
-ptm=[1399]Dap-DSP,364.076278,opt,K,Chemical derivative
-ptm=[1405]EEEDVIEVYQEQTGG,1705.73189,opt,K,Chemical derivative
-ptm=[1402]Label:2H(7)15N(4),11.032077,opt,R,Isotopic label
-ptm=[1403]Label:2H(6)15N(1),7.034695,opt,P,Isotopic label
-ptm=[1406]EDEDTIDVFQQQTGG,1662.700924,opt,K,Chemical derivative
-ptm=[1414]Trimethyl:13C(3)2H(9),54.113505,opt,K,Isotopic label
-ptm=[1414]Trimethyl:13C(3)2H(9),54.113505,opt,R,Isotopic label
-ptm=[1419]15N-oxobutanoic,-18.023584,opt,S,Post-translational
-ptm=[1419]15N-oxobutanoic,-18.023584,opt,C,Artefact
-ptm=[1419]15N-oxobutanoic,-18.023584,opt,T,Post-translational
-ptm=[1421]spermidine,128.131349,opt,Q,Chemical derivative
-ptm=[1423]Biotin:Thermo-21330,473.219571,opt,N-term,Chemical derivative
-ptm=[1423]Biotin:Thermo-21330,473.219571,opt,K,Chemical derivative
-ptm=[1789]Xlink:DSS[155],155.094629,opt,N-term,Chemical derivative
-ptm=[1789]Xlink:DSS[155],155.094629,opt,K,Chemical derivative
-ptm=[1799]NQIGG,469.228496,opt,K,Post-translational
-ptm=[1801]Fluorescein-tyramine,493.116152,opt,Y,Chemical derivative
-ptm=[1839]betaFNA,454.210387,opt,C,Chemical derivative
-ptm=[1839]betaFNA,454.210387,opt,K,Chemical derivative
-ptm=[1831]Tris,104.071154,opt,N,Artefact
-ptm=[1830]Biotin-tyramide,361.146012,opt,Y,Chemical derivative
-ptm=[1829]LRGG+dimethyl,411.259403,opt,K,Post-translational
-ptm=[1824]GEE,86.036779,opt,Q,Chemical derivative
-ptm=[1826]Glu->pyro-Glu+Methyl,-3.994915,opt,E,Artefact
-ptm=[1827]Glu->pyro-Glu+Methyl:2H(2)13C(1),-0.979006,opt,E,Artefact
-ptm=[1828]LRGG+methyl,397.243753,opt,K,Post-translational
-ptm=[1832]IASD,452.034807,opt,C,Chemical derivative
-ptm=[1841]Biotin:Thermo-21328,389.090154,opt,K,Chemical derivative
-ptm=[1841]Biotin:Thermo-21328,389.090154,opt,N-term,Chemical derivative
-ptm=[1843]PhosphoCytidine,305.041287,opt,Y,Post-translational
-ptm=[1843]PhosphoCytidine,305.041287,opt,T,Post-translational
-ptm=[1843]PhosphoCytidine,305.041287,opt,S,Post-translational
-ptm=[1845]AzidoF,41.001397,opt,F,Chemical derivative
-ptm=[1846]Dimethylaminoethyl,71.073499,opt,C,Chemical derivative
-ptm=[1848]Gluratylation,114.031694,opt,K,Post-translational
-ptm=[1849]2-hydroxyisobutyrylation,86.036779,opt,K,Post-translational
-ptm=[1868]MeMePhosphorothioate,107.979873,opt,S,Chemical derivative
-ptm=[1870]Cation:Fe[III],52.911464,opt,D,Artefact
-ptm=[1870]Cation:Fe[III],52.911464,opt,E,Artefact
-ptm=[1870]Cation:Fe[III],52.911464,opt,C-term,Artefact
-ptm=[1871]DTT,151.996571,opt,C,Artefact
-ptm=[1878]Xlink:DSSO[176],176.01433,opt,K,Chemical derivative
-ptm=[1878]Xlink:DSSO[176],176.01433,opt,N-term,Chemical derivative
-ptm=[1873]MesitylOxide,98.073165,opt,K,Chemical derivative
-ptm=[1873]MesitylOxide,98.073165,opt,H,Chemical derivative
-ptm=[1873]MesitylOxide,98.073165,opt,N-term,Chemical derivative
-ptm=[1877]Xlink:DSS[259],259.141973,opt,K,Chemical derivative
-ptm=[1877]Xlink:DSS[259],259.141973,opt,N-term,Chemical derivative
-ptm=[1875]methylol,30.010565,opt,Y,Chemical derivative
-ptm=[1875]methylol,30.010565,opt,W,Chemical derivative
-ptm=[1875]methylol,30.010565,opt,K,Chemical derivative
-ptm=[1879]Xlink:DSSO[175],175.030314,opt,K,Chemical derivative
-ptm=[1879]Xlink:DSSO[175],175.030314,opt,N-term,Chemical derivative
-ptm=[1880]Xlink:DSSO[279],279.077658,opt,K,Chemical derivative
-ptm=[1880]Xlink:DSSO[279],279.077658,opt,N-term,Chemical derivative
-ptm=[1881]Xlink:DSSO[54],54.010565,opt,N-term,Chemical derivative
-ptm=[1881]Xlink:DSSO[54],54.010565,opt,K,Chemical derivative
-ptm=[1882]Xlink:DSSO[86],85.982635,opt,K,Chemical derivative
-ptm=[1882]Xlink:DSSO[86],85.982635,opt,N-term,Chemical derivative
-ptm=[1883]Xlink:DSSO[104],103.9932,opt,K,Chemical derivative
-ptm=[1883]Xlink:DSSO[104],103.9932,opt,N-term,Chemical derivative
-ptm=[1885]Xlink:BuUrBu[111],111.032028,opt,K,Chemical derivative
-ptm=[1885]Xlink:BuUrBu[111],111.032028,opt,N-term,Chemical derivative
-ptm=[1886]Xlink:BuUrBu[85],85.052764,opt,K,Chemical derivative
-ptm=[1886]Xlink:BuUrBu[85],85.052764,opt,N-term,Chemical derivative
-ptm=[1888]Xlink:BuUrBu[214],214.095357,opt,N-term,Chemical derivative
-ptm=[1888]Xlink:BuUrBu[214],214.095357,opt,K,Chemical derivative
-ptm=[1889]Xlink:BuUrBu[317],317.158686,opt,N-term,Chemical derivative
-ptm=[1889]Xlink:BuUrBu[317],317.158686,opt,K,Chemical derivative
-ptm=[1896]Xlink:DSSO[158],158.003765,opt,K,Chemical derivative
-ptm=[1896]Xlink:DSSO[158],158.003765,opt,N-term,Chemical derivative
-ptm=[1898]Xlink:DSS[138],138.06808,opt,K,Chemical derivative
-ptm=[1898]Xlink:DSS[138],138.06808,opt,N-term,Chemical derivative
-ptm=[1899]Xlink:BuUrBu[196],196.084792,opt,N-term,Chemical derivative
-ptm=[1899]Xlink:BuUrBu[196],196.084792,opt,K,Chemical derivative
-ptm=[1900]Xlink:DTBP[172],172.01289,opt,K,Chemical derivative
-ptm=[1900]Xlink:DTBP[172],172.01289,opt,N-term,Chemical derivative
-ptm=[1901]Xlink:DST[114],113.995309,opt,K,Chemical derivative
-ptm=[1901]Xlink:DST[114],113.995309,opt,N-term,Chemical derivative
-ptm=[1902]Xlink:DTSSP[174],173.980921,opt,K,Chemical derivative
-ptm=[1902]Xlink:DTSSP[174],173.980921,opt,N-term,Chemical derivative
-ptm=[1903]Xlink:SMCC[219],219.089543,opt,C,Chemical derivative
-ptm=[1903]Xlink:SMCC[219],219.089543,opt,K,Chemical derivative
-ptm=[1903]Xlink:SMCC[219],219.089543,opt,N-term,Chemical derivative
-ptm=[1910]Cation:Al[III],23.958063,opt,D,Artefact
-ptm=[1910]Cation:Al[III],23.958063,opt,E,Artefact
-ptm=[1910]Cation:Al[III],23.958063,opt,C-term,Artefact
-ptm=[1906]Xlink:BS2G[113],113.047679,opt,N-term,Chemical derivative
-ptm=[1906]Xlink:BS2G[113],113.047679,opt,K,Chemical derivative
-ptm=[1907]Xlink:BS2G[114],114.031694,opt,N-term,Chemical derivative
-ptm=[1907]Xlink:BS2G[114],114.031694,opt,K,Chemical derivative
-ptm=[1908]Xlink:BS2G[217],217.095023,opt,N-term,Chemical derivative
-ptm=[1908]Xlink:BS2G[217],217.095023,opt,K,Chemical derivative
-ptm=[1911]Xlink:DMP[139],139.110947,opt,K,Chemical derivative
-ptm=[1911]Xlink:DMP[139],139.110947,opt,N-term,Chemical derivative
-ptm=[1912]Xlink:DMP[122],122.084398,opt,K,Chemical derivative
-ptm=[1912]Xlink:DMP[122],122.084398,opt,N-term,Chemical derivative
-ptm=[1913]glyoxalAGE,21.98435,opt,R,Post-translational
-ptm=[1914]Met->AspSA,-32.008456,opt,M,Chemical derivative
-ptm=[1915]Decarboxylation,-30.010565,opt,D,Chemical derivative
-ptm=[1915]Decarboxylation,-30.010565,opt,E,Chemical derivative
-ptm=[1916]Aspartylurea,-10.031969,opt,H,Chemical derivative
-ptm=[1917]Formylasparagine,4.97893,opt,H,Chemical derivative
-ptm=[1918]Carbonyl,13.979265,opt,S,Chemical derivative
-ptm=[1918]Carbonyl,13.979265,opt,R,Chemical derivative
-ptm=[1918]Carbonyl,13.979265,opt,Q,Chemical derivative
-ptm=[1918]Carbonyl,13.979265,opt,L,Chemical derivative
-ptm=[1918]Carbonyl,13.979265,opt,I,Chemical derivative
-ptm=[1918]Carbonyl,13.979265,opt,E,Chemical derivative
-ptm=[1918]Carbonyl,13.979265,opt,A,Chemical derivative
-ptm=[1918]Carbonyl,13.979265,opt,V,Chemical derivative
-ptm=[1922]Pro->HAVA,18.010565,opt,P,Chemical derivative
-ptm=[1920]AFB1_Dialdehyde,310.047738,opt,K,Post-translational
-ptm=[1923]Delta:H(-4)O(2),27.958529,opt,W,Chemical derivative
-ptm=[1924]Delta:H(-4)O(3),43.953444,opt,W,Chemical derivative
-ptm=[1925]Delta:O(4),63.979659,opt,W,Artefact
-ptm=[1926]Delta:H(3)C(3)O(2),71.013304,opt,K,Artefact
-ptm=[1927]Delta:H(4)C(6)O(1),92.026215,opt,R,Chemical derivative
-ptm=[1928]Delta:H(10)C(8)O(1),122.073165,opt,K,Artefact
-ptm=[1929]Delta:H(6)C(7)O(4),154.026609,opt,R,Chemical derivative
-ptm=[1986]Diethylphosphothione,152.006087,opt,Y,Chemical derivative
-ptm=[1986]Diethylphosphothione,152.006087,opt,T,Chemical derivative
-ptm=[1986]Diethylphosphothione,152.006087,opt,S,Chemical derivative
-ptm=[1986]Diethylphosphothione,152.006087,opt,K,Chemical derivative
-ptm=[1986]Diethylphosphothione,152.006087,opt,H,Chemical derivative
-ptm=[1986]Diethylphosphothione,152.006087,opt,C,Chemical derivative
-ptm=[1990]CIGG,330.136176,opt,K,Post-translational
-ptm=[1991]GNLLFLACYCIGG,1324.6308,opt,K,Post-translational
-ptm=[1987]Dimethylphosphothione,123.974787,opt,S,Chemical derivative
-ptm=[1987]Dimethylphosphothione,123.974787,opt,K,Chemical derivative
-ptm=[1987]Dimethylphosphothione,123.974787,opt,H,Chemical derivative
-ptm=[1987]Dimethylphosphothione,123.974787,opt,C,Chemical derivative
-ptm=[1987]Dimethylphosphothione,123.974787,opt,Y,Chemical derivative
-ptm=[1987]Dimethylphosphothione,123.974787,opt,T,Chemical derivative
-ptm=[1989]monomethylphosphothione,109.959137,opt,S,Chemical derivative
-ptm=[1989]monomethylphosphothione,109.959137,opt,K,Chemical derivative
-ptm=[1989]monomethylphosphothione,109.959137,opt,H,Chemical derivative
-ptm=[1989]monomethylphosphothione,109.959137,opt,C,Chemical derivative
-ptm=[1989]monomethylphosphothione,109.959137,opt,T,Chemical derivative
-ptm=[1989]monomethylphosphothione,109.959137,opt,Y,Chemical derivative
-ptm=[1773]Hex-4-HexNAc-3-NeuAc-1-,1548.544828,opt,X,glycosilation
-ptm=[1752]Hex-2-HexNAc-3-NeuGc-3-,1854.614759,opt,X,glycosilation
-ptm=[1697]dHex-4-Hex-1-HexNAc-2-Kdn-1-,1402.512072,opt,X,glycosilation
-ptm=[137]Hex-5-HexNAc-2-,1216.422863,opt,X,glycosilation
-ptm=[1497]dHex-3-Hex-3-HexNAc-3-Pent-1-,1665.612574,opt,X,glycosilation
-ptm=[143]HexNAc-2-,406.158745,opt,X,glycosilation
-ptm=[1461]Hex-4-HexNAc-2-NeuAc-1-,1345.465456,opt,X,glycosilation
-ptm=[1687]Hex-1-HexNAc-3-NeuAc-2-,1353.481775,opt,X,glycosilation
-ptm=[1477]dHex-1-Hex-6-HexNAc-2-,1524.533595,opt,X,glycosilation
-ptm=[1621]dHex-2-Hex-2-HexA-1-HexNAc-1-,995.332925,opt,X,glycosilation
-ptm=[146]Hex-1-HexNAc-1-dHex-1-,511.190105,opt,X,glycosilation
-ptm=[1504]Hex-8-HexNAc-2-,1702.581333,opt,X,glycosilation
-ptm=[1438]Hex-2-HexNAc-1-,527.18502,opt,X,glycosilation
-ptm=[1485]dHex-1-Hex-3-HexNAc-4-Pent-1-,1576.576129,opt,X,glycosilation
-ptm=[1680]dHex-2-HexNAc-5-,1307.512681,opt,X,glycosilation
-ptm=[1579]dHex-1-Hex-2-HexNAc-1-Sulf-1-,753.199743,opt,X,glycosilation
-ptm=[490]Hep,192.063388,opt,X,glycosilation
-ptm=[1452]Hex-4-HexNAc-2-,1054.370039,opt,X,glycosilation
-ptm=[1602]Hex-1-HexNAc-2-NeuGc-1-,875.3019,opt,X,glycosilation
-ptm=[1457]Hex-3-HexNAc-3-Pent-1-,1227.438847,opt,X,glycosilation
-ptm=[1771]dHex-2-Hex-3-HexNAc-3-,1387.512406,opt,X,glycosilation
-ptm=[1500]dHex-1-Hex-7-HexNAc-2-,1686.586419,opt,X,glycosilation
-ptm=[1938]Hex-4-HexA-1-,824.243382,opt,X,glycosilation
-ptm=[1667]dHex-3-Hex-1-HexNAc-2-Kdn-1-,1256.454163,opt,X,glycosilation
-ptm=[1525]dHex-1-Hex-3-HexNAc-4-Pent-3-,1840.660646,opt,X,glycosilation
-ptm=[1694]Hex-2-HexNAc-2-NeuAc-2-Sulf-1-,1392.41204,opt,X,glycosilation
-ptm=[1748]dHex-2-Hex-4-HexA-1-HexNAc-3-Sulf-1-,1805.554132,opt,X,glycosilation
-ptm=[1930]Pent-2-,264.084518,opt,X,glycosilation
-ptm=[1473]Hex-8-HexNAc-1-,1499.501961,opt,X,glycosilation
-ptm=[1672]Hex-1-HexNAc-1-NeuGc-3-,1286.40319,opt,X,glycosilation
-ptm=[1736]dHex-2-Hex-3-HexA-1-HexNAc-3-Sulf-1-,1643.501309,opt,X,glycosilation
-ptm=[1738]Hex-3-HexNAc-3-NeuAc-2-,1677.587422,opt,X,glycosilation
-ptm=[1936]Hex-2-Pent-2-,588.190165,opt,X,glycosilation
-ptm=[1523]dHex-2-Hex-4-HexNAc-4-Sulf-1-,1832.601417,opt,X,glycosilation
-ptm=[1784]dHex-1-Hex-3-HexNAc-5-NeuAc-1-,1938.708659,opt,X,glycosilation
-ptm=[1486]Hex-3-HexNAc-5-Sulf-1-,1581.512148,opt,X,glycosilation
-ptm=[1623]Hex-2-HexA-1-NeuAc-1-Pent-1-Sulf-1-,1003.232225,opt,X,glycosilation
-ptm=[154]Hex-3-HexNAc-1-Pent-1-,821.280102,opt,X,glycosilation
-ptm=[1564]dHex-1-Hex-2-HexNAc-1-,673.242928,opt,X,glycosilation
-ptm=[1609]dHex-1-Hex-2-HexA-1-HexNAc-1-Sulf-1-,929.231831,opt,X,glycosilation
-ptm=[1563]Hex-1-HexNAc-1-NeuGc-1-,672.222527,opt,X,glycosilation
-ptm=[1463]dHex-1-Hex-3-HexNAc-3-Pent-1-,1373.496756,opt,X,glycosilation
-ptm=[1701]dHex-2-Hex-2-HexNAc-4-,1428.538955,opt,X,glycosilation
-ptm=[1587]dHex-1-Hex-1-HexNAc-2-Sulf-1-,794.226292,opt,X,glycosilation
-ptm=[1760]dHex-2-Hex-2-HexNAc-2-,1022.38021,opt,X,glycosilation
-ptm=[1646]Hex-2-HexNAc-4-,1136.423137,opt,X,glycosilation
-ptm=[1427]Hex-1-HexA-1-,338.084912,opt,X,glycosilation
-ptm=[1591]HexNAc-4-,812.31749,opt,X,glycosilation
-ptm=[1444]Hex-2-NeuAc-1-,615.201064,opt,X,glycosilation
-ptm=[1946]dHex-3-Hex-3-HexNAc-1-,1127.41157,opt,X,glycosilation
-ptm=[1586]dHex-2-Hex-2-HexA-1-,792.253553,opt,X,glycosilation
-ptm=[1737]dHex-1-Hex-4-HexA-1-HexNAc-3-Sulf-1-,1659.496223,opt,X,glycosilation
-ptm=[1786]Hex-1-HexNAc-1-NeuAc-1-Ac-1-,698.238177,opt,X,glycosilation
-ptm=[1377]dHex-1-Hex-4-,794.269203,opt,X,glycosilation
-ptm=[1650]dHex-2-Hex-1-HexNAc-2-NeuAc-1-,1151.422803,opt,X,glycosilation
-ptm=[142]HexNAc-1-dHex-1-,349.137281,opt,X,glycosilation
-ptm=[1541]dHex-2-Hex-5-HexNAc-4-,1914.697426,opt,X,glycosilation
-ptm=[1375]dHex-1-Hex-2-,470.163556,opt,X,glycosilation
-ptm=[1545]Hex-5-HexNAc-4-NeuGc-1-,1929.671939,opt,X,glycosilation
-ptm=[1958]dHex-3-Hex-2-HexNAc-4-Sulf-2-,1734.510493,opt,X,glycosilation
-ptm=[1600]Hex-1-HexNAc-2-NeuAc-1-,859.306985,opt,X,glycosilation
-ptm=[1948]Hex-1-HexNAc-4-dHex-1-Sulf-1-,1200.385037,opt,X,glycosilation
-ptm=[1678]Hex-2-HexNAc-3-NeuAc-1-Sulf-1-,1304.395996,opt,X,glycosilation
-ptm=[1537]dHex-1-Hex-7-HexNAc-3-,1889.665791,opt,X,glycosilation
-ptm=[1503]Hex-5-HexNAc-4-Sulf-1-,1702.538423,opt,X,glycosilation
-ptm=[1726]dHex-4-Hex-2-HexNAc-2-Kdn-1-,1564.564895,opt,X,glycosilation
-ptm=[1670]dHex-2-Hex-2-HexA-1-HexNAc-2-Sulf-1-,1278.369113,opt,X,glycosilation
-ptm=[1666]dHex-1-Hex-2-HexNAc-2-NeuAc-1-Sulf-1-,1247.374532,opt,X,glycosilation
-ptm=[1552]Hex-6-HexNAc-4-Me-3-Pent-1-,1958.72364,opt,X,glycosilation
-ptm=[1963]dHex-1-Hex-8-HexNAc-2-,1848.639242,opt,X,glycosilation
-ptm=[1594]dHex-2-Hex-2-HexNAc-1-,819.300837,opt,X,glycosilation
-ptm=[1750]dHex-1-Hex-3-HexNAc-3-NeuAc-2-,1823.64533,opt,X,glycosilation
-ptm=[1433]HexNAc-3-,609.238118,opt,X,glycosilation
-ptm=[1590]Hex-5-,810.264117,opt,X,glycosilation
-ptm=[1942]HexA-2-HexNAc-3-,961.302294,opt,X,glycosilation
-ptm=[1772]Hex-3-HexNAc-5-,1501.555334,opt,X,glycosilation
-ptm=[1700]Hex-2-HexNAc-4-NeuAc-1-,1427.518554,opt,X,glycosilation
-ptm=[1543]dHex-1-Hex-5-HexNAc-4-Sulf-2-,1928.553146,opt,X,glycosilation
-ptm=[1511]dHex-3-Hex-3-HexNAc-4-,1736.649688,opt,X,glycosilation
-ptm=[1596]dHex-1-Hex-3-HexNAc-1-,835.295752,opt,X,glycosilation
-ptm=[310]Hex-4-HexNAc-4-,1460.528784,opt,X,glycosilation
-ptm=[1633]Hex-6-Phos-1-,1052.283272,opt,X,glycosilation
-ptm=[1547]dHex-1-Hex-6-HexNAc-4-,1930.69234,opt,X,glycosilation
-ptm=[1577]Hex-1-HexNAc-1-NeuAc-1-Sulf-1-,736.184427,opt,X,glycosilation
-ptm=[1430]Hex-1-HexNAc-1-Sulf-1-,445.089011,opt,X,glycosilation
-ptm=[1539]dHex-1-Hex-5-HexA-1-HexNAc-3-Sulf-2-,1901.505861,opt,X,glycosilation
-ptm=[1456]Hex-4-HexNAc-2-Pent-1-,1186.412298,opt,X,glycosilation
-ptm=[160]Hex-1-HexNAc-1-NeuAc-2-,947.323029,opt,X,glycosilation
-ptm=[1703]dHex-4-HexNAc-3-Kdn-1-,1443.538621,opt,X,glycosilation
-ptm=[295]dHex,146.057909,opt,X,glycosilation
-ptm=[1691]dHex-1-Hex-4-HexA-1-HexNAc-2-,1376.460036,opt,X,glycosilation
-ptm=[1610]Hex-2-HexNAc-3-,933.343765,opt,X,glycosilation
-ptm=[1606]dHex-2-Hex-1-HexNAc-1-Kdn-1-,907.316881,opt,X,glycosilation
-ptm=[1570]HexNAc-1-Kdn-2-,703.217108,opt,X,glycosilation
-ptm=[1649]Hex-1-HexNAc-2-NeuAc-2-,1150.402402,opt,X,glycosilation
-ptm=[1376]dHex-1-Hex-3-,632.216379,opt,X,glycosilation
-ptm=[1585]Hex-2-HexA-1-HexNAc-1-Sulf-1-,783.173922,opt,X,glycosilation
-ptm=[1480]Hex-7-HexNAc-2-,1540.52851,opt,X,glycosilation
-ptm=[1658]dHex-1-Hex-1-HexNAc-3-NeuAc-1-,1208.444267,opt,X,glycosilation
-ptm=[1939]Hex-2-HexNAc-1-Pent-1-HexA-1-,835.259366,opt,X,glycosilation
-ptm=[1624]dHex-1-Hex-1-HexNAc-2-NeuAc-1-,1005.364894,opt,X,glycosilation
-ptm=[1663]dHex-2-Hex-3-HexA-1-HexNAc-1-Sulf-1-,1237.342563,opt,X,glycosilation
-ptm=[1441]Hex-1-Pent-3-,558.1796,opt,X,glycosilation
-ptm=[1513]Hex-4-HexNAc-5-Sulf-1-,1743.564972,opt,X,glycosilation
-ptm=[1560]Hex-5-HexNAc-4-NeuAc-1-Sulf-1-,1993.633839,opt,X,glycosilation
-ptm=[1718]dHex-2-Hex-2-HexNAc-3-NeuAc-1-,1516.554999,opt,X,glycosilation
-ptm=[1679]dHex-2-Hex-2-HexNAc-3-Sulf-1-,1305.416397,opt,X,glycosilation
-ptm=[1562]dHex-2-Hex-3-HexNAc-6-,1996.750524,opt,X,glycosilation
-ptm=[1604]Hex-5-Phos-1-,890.230448,opt,X,glycosilation
-ptm=[1956]dHex-2-Hex-2-HexNAc-4-Sulf-2-,1588.452584,opt,X,glycosilation
-ptm=[1756]Hex-4-HexNAc-4-NeuAc-1-Sulf-2-,1911.53783,opt,X,glycosilation
-ptm=[151]Hex-1-HexNAc-2-Pent-1-,700.253828,opt,X,glycosilation
-ptm=[1639]dHex-1-Hex-1-HexNAc-2-NeuAc-1-Sulf-1-,1085.321709,opt,X,glycosilation
-ptm=[1303]NeuAc,291.095417,opt,X,glycosilation
-ptm=[1466]Hex-4-HexNAc-3-Pent-1-,1389.491671,opt,X,glycosilation
-ptm=[1749]Hex-2-HexNAc-3-NeuAc-3-,1806.630015,opt,X,glycosilation
-ptm=[1411]dHex-1-Hex-5-HexNAc-4-NeuAc-2-,2350.83035,opt,X,glycosilation
-ptm=[1785]dHex-2-Hex-4-HexNAc-5-,1955.723975,opt,X,glycosilation
-ptm=[1781]dHex-1-Hex-3-HexNAc-6-,1850.692615,opt,X,glycosilation
-ptm=[1595]Hex-2-HexNAc-1-NeuGc-1-,834.275351,opt,X,glycosilation
-ptm=[1443]Hex-2-HexNAc-1-Sulf-1-,607.141834,opt,X,glycosilation
-ptm=[1542]dHex-2-Hex-4-HexNAc-3-NeuAc-1-Sulf-1-,1920.617461,opt,X,glycosilation
-ptm=[1487]Hex-6-HexNAc-3-,1581.555059,opt,X,glycosilation
-ptm=[1711]Hex-3-HexNAc-3-NeuAc-1-Sulf-1-,1466.44882,opt,X,glycosilation
-ptm=[1713]Hex-3-HexNAc-3-NeuGc-1-Sulf-1-,1482.443734,opt,X,glycosilation
-ptm=[1653]dHex-2-Hex-1-HexNAc-2-NeuGc-1-,1167.417718,opt,X,glycosilation
-ptm=[1578]Hex-1-HexA-1-HexNAc-2-,744.243657,opt,X,glycosilation
-ptm=[1657]dHex-1-Hex-2-HexNAc-2-NeuGc-1-,1183.412632,opt,X,glycosilation
-ptm=[148]Hex-1-HexNAc-2-,568.211569,opt,X,glycosilation
-ptm=[1765]dHex-2-Hex-3-HexNAc-2-,1184.433033,opt,X,glycosilation
-ptm=[1524]Hex-4-HexNAc-3-NeuAc-2-,1839.640245,opt,X,glycosilation
-ptm=[1566]Hex-3-HexNAc-1-,689.237843,opt,X,glycosilation
-ptm=[1484]dHex-1-Hex-5-HexNAc-3-,1565.560144,opt,X,glycosilation
-ptm=[1725]Hex-2-HexNAc-3-NeuGc-2-,1547.524427,opt,X,glycosilation
-ptm=[1520]dHex-1-Hex-5-HexA-1-HexNAc-3-Sulf-1-,1821.549047,opt,X,glycosilation
-ptm=[1716]dHex-1-Hex-3-HexA-1-HexNAc-3-Sulf-1-,1497.4434,opt,X,glycosilation
-ptm=[1628]HexNAc-5-,1015.396863,opt,X,glycosilation
-ptm=[1508]dHex-1-Hex-3-HexNAc-5-Sulf-1-,1727.570057,opt,X,glycosilation
-ptm=[1717]Hex-2-HexNAc-3-NeuAc-2-,1515.534598,opt,X,glycosilation
-ptm=[1724]dHex-1-Hex-2-HexNAc-2-NeuAc-2-Sulf-1-,1538.469949,opt,X,glycosilation
-ptm=[1637]dHex-2-Hex-1-HexNAc-3-,1063.406759,opt,X,glycosilation
-ptm=[1934]HexNAc-2-Sulf-1-,486.11556,opt,X,glycosilation
-ptm=[1515]dHex-1-Hex-4-HexNAc-3-NeuAc-1-Sulf-1-,1774.559552,opt,X,glycosilation
-ptm=[1740]dHex-4-Hex-3-HexNAc-3-,1679.628224,opt,X,glycosilation
-ptm=[1465]Hex-6-HexNAc-2-,1378.475686,opt,X,glycosilation
-ptm=[155]Hex-1-HexNAc-2-dHex-1-Pent-1-,846.311736,opt,X,glycosilation
-ptm=[1673]dHex-1-Hex-1-HexNAc-3-NeuAc-1-Sulf-1-,1288.401081,opt,X,glycosilation
-ptm=[1757]Hex-4-HexNAc-4-NeuGc-1-Sulf-2-,1927.532745,opt,X,glycosilation
-ptm=[159]Hex-3-HexNAc-2-,892.317216,opt,X,glycosilation
-ptm=[1722]dHex-3-Hex-3-HexNAc-3-,1533.570315,opt,X,glycosilation
-ptm=[305]dHex-1-Hex-3-HexNAc-4-,1444.53387,opt,X,glycosilation
-ptm=[1968]Hex-3-HexNAc-3-NeuAc-3-,1968.682838,opt,X,glycosilation
-ptm=[1742]Hex-9-Phos-3-,1698.374404,opt,X,glycosilation
-ptm=[429]PhosphoHex,242.019154,opt,X,glycosilation
-ptm=[1733]dHex-3-Hex-2-HexNAc-3-Kdn-1-,1621.586359,opt,X,glycosilation
-ptm=[1947]Hex-6-HexNAc-1-,1175.396314,opt,X,glycosilation
-ptm=[1378]dHex-1-Hex-5-,956.322026,opt,X,glycosilation
-ptm=[1768]dHex-1-Hex-3-HexNAc-3-,1241.454497,opt,X,glycosilation
-ptm=[1732]Hex-4-HexNAc-4-Sulf-2-,1620.442414,opt,X,glycosilation
-ptm=[1519]dHex-1-Hex-4-HexNAc-5-,1809.666066,opt,X,glycosilation
-ptm=[1728]dHex-3-Hex-2-HexNAc-4-,1574.596864,opt,X,glycosilation
-ptm=[512]Hex-2-,324.105647,opt,X,glycosilation
-ptm=[1625]dHex-1-Hex-3-HexA-1-HexNAc-1-,1011.32784,opt,X,glycosilation
-ptm=[1634]dHex-1-Hex-2-HexA-1-HexNAc-2-,1052.354389,opt,X,glycosilation
-ptm=[1631]Hex-2-HexNAc-2-NeuGc-1-,1037.354723,opt,X,glycosilation
-ptm=[307]dHex-1-Hex-4-HexNAc-4-,1606.586693,opt,X,glycosilation
-ptm=[1429]Hex-1-HexNAc-1-Phos-1-,445.098527,opt,X,glycosilation
-ptm=[1774]dHex-2-Hex-3-HexNAc-4-,1590.591779,opt,X,glycosilation
-ptm=[1640]dHex-1-Hex-3-HexA-1-HexNAc-1-Sulf-1-,1091.284655,opt,X,glycosilation
-ptm=[1527]dHex-1-Hex-5-HexNAc-4-Sulf-1-,1848.596331,opt,X,glycosilation
-ptm=[793]Hex-1-HexNAc-1-,365.132196,opt,X,glycosilation
-ptm=[1449]dHex-1-Hex-2-HexNAc-2-Pent-1-,1008.36456,opt,X,glycosilation
-ptm=[1689]dHex-3-Hex-2-HexNAc-3-,1371.517491,opt,X,glycosilation
-ptm=[1933]Hex-1-Pent-2-Me-1-,440.152991,opt,X,glycosilation
-ptm=[144]Hex-3-,486.158471,opt,X,glycosilation
-ptm=[1782]dHex-1-Hex-4-HexNAc-4-NeuAc-1-,1897.68211,opt,X,glycosilation
-ptm=[1556]dHex-1-Hex-4-HexNAc-4-NeuAc-1-Sulf-1-,1977.638925,opt,X,glycosilation
-ptm=[1622]dHex-1-Hex-1-HexNAc-3-Sulf-1-,997.305665,opt,X,glycosilation
-ptm=[1952]dHex-1-Hex-2-HexNAc-4-Sulf-2-,1442.394675,opt,X,glycosilation
-ptm=[1698]dHex-3-Hex-2-HexNAc-2-Kdn-1-,1418.506986,opt,X,glycosilation
-ptm=[1448]Hex-4-,648.211294,opt,X,glycosilation
-ptm=[1582]Hex-1-HexNAc-3-,771.290941,opt,X,glycosilation
-ptm=[1439]Hex-1-HexA-1-HexNAc-1-,541.164284,opt,X,glycosilation
-ptm=[1693]dHex-1-Hex-3-HexA-2-HexNAc-2-,1390.439301,opt,X,glycosilation
-ptm=[1671]dHex-1-Hex-2-HexNAc-4-,1282.481046,opt,X,glycosilation
-ptm=[1949]dHex-1-Hex-2-HexNAc-1-NeuAc-2-,1255.433762,opt,X,glycosilation
-ptm=[1546]dHex-1-Hex-3-HexNAc-6-Sulf-1-,1930.64943,opt,X,glycosilation
-ptm=[1458]Hex-5-HexNAc-2-Phos-1-,1296.389194,opt,X,glycosilation
-ptm=[1453]dHex-1-Hex-4-HexNAc-1-Pent-1-,1129.390834,opt,X,glycosilation
-ptm=[1491]Hex-4-HexNAc-4-Me-2-Pent-1-,1620.602343,opt,X,glycosilation
-ptm=[1471]dHex-1-Hex-4-HexNAc-3-Sulf-1-,1483.464135,opt,X,glycosilation
-ptm=[1962]Hex-10-HexNAc-1-,1823.607608,opt,X,glycosilation
-ptm=[1950]dHex-3-Hex-3-HexNAc-2-,1330.490942,opt,X,glycosilation
-ptm=[1931]Pent-1-HexNAc-1-,335.121631,opt,X,glycosilation
-ptm=[213]ADP-Ribosyl,541.06111,opt,X,glycosilation
-ptm=[309]Hex-3-HexNAc-4-,1298.475961,opt,X,glycosilation
-ptm=[1743]dHex-2-HexNAc-7-,1713.671426,opt,X,glycosilation
-ptm=[1534]dHex-1-Hex-5-HexNAc-3-NeuGc-1-,1872.650475,opt,X,glycosilation
-ptm=[1783]dHex-3-Hex-4-HexNAc-4-,1898.702511,opt,X,glycosilation
-ptm=[1304]NeuGc,307.090331,opt,X,glycosilation
-ptm=[1635]dHex-2-Hex-3-HexNAc-1-Sulf-1-,1061.310475,opt,X,glycosilation
-ptm=[1535]dHex-2-Hex-4-HexNAc-4-Pent-1-,1884.686861,opt,X,glycosilation
-ptm=[1758]dHex-2-Hex-3-HexNAc-3-NeuAc-2-,1969.703239,opt,X,glycosilation
-ptm=[1660]dHex-1-Hex-3-HexA-1-HexNAc-2-,1214.407213,opt,X,glycosilation
-ptm=[1510]dHex-1-Hex-3-HexNAc-4-NeuAc-1-,1735.629286,opt,X,glycosilation
-ptm=[1460]Hex-7-HexNAc-1-,1337.449137,opt,X,glycosilation
-ptm=[1532]Hex-4-HexNAc-6-,1866.68753,opt,X,glycosilation
-ptm=[1467]dHex-1-Hex-4-HexNAc-3-,1403.507321,opt,X,glycosilation
-ptm=[1507]dHex-2-Hex-3-HexNAc-4-Pent-1-,1722.634037,opt,X,glycosilation
-ptm=[1533]dHex-3-Hex-3-HexNAc-4-Pent-1-,1868.691946,opt,X,glycosilation
-ptm=[1568]HexNAc-2-NeuAc-1-,697.254162,opt,X,glycosilation
-ptm=[1567]Hex-1-HexNAc-1-Kdn-1-Sulf-1-,695.157878,opt,X,glycosilation
-ptm=[1937]Hex-2-Pent-2-Me-1-,602.205815,opt,X,glycosilation
-ptm=[1528]dHex-2-Hex-3-HexNAc-4-Pent-2-,1854.676296,opt,X,glycosilation
-ptm=[1780]Hex-5-HexNAc-5-,1825.660981,opt,X,glycosilation
-ptm=[1969]Hex-5-HexNAc-4-NeuAc-1-Ac-2-,1997.698154,opt,X,glycosilation
-ptm=[1661]dHex-1-Hex-1-HexNAc-3-NeuGc-1-,1224.439181,opt,X,glycosilation
-ptm=[1645]dHex-1-Hex-1-HexNAc-4-,1120.428223,opt,X,glycosilation
-ptm=[1599]Hex-4-HexNAc-1-,851.290667,opt,X,glycosilation
-ptm=[1957]Hex-9-HexNAc-1-,1661.554784,opt,X,glycosilation
-ptm=[41]Hex,162.052824,opt,X,glycosilation
-ptm=[1442]Hex-1-NeuAc-1-Pent-1-,585.190499,opt,X,glycosilation
-ptm=[1575]Hex-4-Phos-1-,728.177625,opt,X,glycosilation
-ptm=[1955]dHex-2-Hex-5-HexNAc-2-Me-1-,1522.554331,opt,X,glycosilation
-ptm=[1675]dHex-1-Hex-1-HexNAc-2-NeuAc-2-,1296.460311,opt,X,glycosilation
-ptm=[1960]dHex-4-Hex-3-HexNAc-2-NeuAc-1-,1767.644268,opt,X,glycosilation
-ptm=[152]Hex-1-HexNAc-2-dHex-1-,714.269478,opt,X,glycosilation
-ptm=[145]HexNAc-1-dHex-2-,495.19519,opt,X,glycosilation
-ptm=[1959]Hex-4-HexNAc-4-NeuGc-1-,1767.619116,opt,X,glycosilation
-ptm=[1746]dHex-2-Hex-3-HexNAc-5-,1793.671151,opt,X,glycosilation
-ptm=[1464]Hex-3-HexNAc-4-Sulf-1-,1378.432776,opt,X,glycosilation
-ptm=[161]Hex-3-HexNAc-2-Phos-1-,972.283547,opt,X,glycosilation
-ptm=[1619]Hex-1-HexNAc-1-NeuGc-2-,979.312859,opt,X,glycosilation
-ptm=[1451]Hex-3-HexNAc-2-Pent-1-,1024.359475,opt,X,glycosilation
-ptm=[1766]dHex-1-Hex-4-HexNAc-2-,1200.427948,opt,X,glycosilation
-ptm=[1767]dHex-2-Hex-2-HexNAc-3-,1225.459583,opt,X,glycosilation
-ptm=[1745]Hex-3-HexNAc-3-NeuAc-2-Sulf-1-,1757.544236,opt,X,glycosilation
-ptm=[1459]dHex-1-Hex-4-HexNAc-2-Pent-1-,1332.470207,opt,X,glycosilation
-ptm=[1965]dHex-2-Hex-3-HexNAc-4-NeuAc-1-,1881.687195,opt,X,glycosilation
-ptm=[1495]Hex-6-HexNAc-3-Phos-1-,1661.52139,opt,X,glycosilation
-ptm=[1620]Hex-1-HexNAc-1-NeuAc-2-Ac-1-,989.333594,opt,X,glycosilation
-ptm=[1526]dHex-2-Hex-5-HexNAc-3-Pent-1-,1843.660312,opt,X,glycosilation
-ptm=[1447]Hex-1-HexNAc-2-Sulf-1-,648.168383,opt,X,glycosilation
-ptm=[1644]dHex-2-Hex-1-HexNAc-2-Kdn-1-,1110.396254,opt,X,glycosilation
-ptm=[1769]Hex-4-HexNAc-3-,1257.449412,opt,X,glycosilation
-ptm=[1512]dHex-1-Hex-4-HexNAc-4-Pent-1-,1738.628952,opt,X,glycosilation
-ptm=[1715]dHex-2-Hex-3-HexNAc-2-NeuGc-1-,1491.523365,opt,X,glycosilation
-ptm=[1559]Hex-6-HexNAc-5-,1987.713804,opt,X,glycosilation
-ptm=[1676]dHex-3-HexNAc-3-Kdn-1-,1297.480712,opt,X,glycosilation
-ptm=[1581]dHex-1-Hex-1-HexNAc-1-Kdn-1-,761.258973,opt,X,glycosilation
-ptm=[1450]Hex-2-HexNAc-2-NeuAc-1-,1021.359809,opt,X,glycosilation
-ptm=[1611]Hex-1-HexNAc-2-NeuAc-1-Sulf-1-,939.2638,opt,X,glycosilation
-ptm=[1583]HexNAc-2-NeuAc-1-Sulf-1-,777.210976,opt,X,glycosilation
-ptm=[1428]Hex-1-Pent-2-,426.137341,opt,X,glycosilation
-ptm=[1612]dHex-2-Hex-4-,940.327112,opt,X,glycosilation
-ptm=[1763]Hex-3-HexNAc-3-,1095.396588,opt,X,glycosilation
-ptm=[1494]dHex-2-Hex-3-HexNAc-3-Pent-2-,1651.596924,opt,X,glycosilation
-ptm=[1648]dHex-2-Hex-4-HexNAc-1-,1143.406484,opt,X,glycosilation
-ptm=[1496]Hex-4-HexNAc-5-,1663.608157,opt,X,glycosilation
-ptm=[1699]dHex-3-Hex-2-HexA-1-HexNAc-2-Sulf-1-,1424.427021,opt,X,glycosilation
-ptm=[1712]Hex-3-HexNAc-2-NeuAc-2-,1474.508049,opt,X,glycosilation
-ptm=[1669]dHex-2-Hex-2-HexNAc-2-Kdn-1-,1272.449077,opt,X,glycosilation
-ptm=[1412]s-GlcNAc,283.036187,opt,X,glycosilation
-ptm=[1426]Hex-1-Pent-1-,294.095082,opt,X,glycosilation
-ptm=[1641]dHex-1-Hex-1-HexA-1-HexNAc-3-,1093.380938,opt,X,glycosilation
-ptm=[1696]Hex-3-HexNAc-3-NeuGc-1-,1402.48692,opt,X,glycosilation
-ptm=[1665]Hex-2-HexNAc-3-NeuGc-1-,1240.434096,opt,X,glycosilation
-ptm=[1413]PhosphoHex-2-,404.071978,opt,X,glycosilation
-ptm=[1468]Hex-5-HexNAc-3-,1419.502235,opt,X,glycosilation
-ptm=[1431]Hex-1-NeuAc-1-,453.14824,opt,X,glycosilation
-ptm=[1544]dHex-1-Hex-5-HexNAc-4-Me-2-Pent-1-,1928.713076,opt,X,glycosilation
-ptm=[1695]dHex-2-Hex-2-HexNAc-2-NeuAc-1-Sulf-1-,1393.432441,opt,X,glycosilation
-ptm=[1408]Hex-5-HexNAc-4-NeuAc-2-,2204.772441,opt,X,glycosilation
-ptm=[1499]dHex-1-Hex-4-HexNAc-4-Sulf-1-,1686.543508,opt,X,glycosilation
-ptm=[1498]dHex-2-Hex-4-HexNAc-3-Pent-1-,1681.607488,opt,X,glycosilation
-ptm=[1554]dHex-1-Hex-7-HexNAc-3-Phos-1-,1969.632122,opt,X,glycosilation
-ptm=[1729]Hex-1-HexNAc-1-NeuGc-4-,1593.493521,opt,X,glycosilation
-ptm=[1751]dHex-3-Hex-3-HexNAc-3-NeuAc-1-,1824.665732,opt,X,glycosilation
-ptm=[1967]Hex-5-HexNAc-4-NeuAc-1-Ac-1-,1955.687589,opt,X,glycosilation
-ptm=[1553]dHex-1-Hex-7-HexNAc-3-Sulf-1-,1969.622606,opt,X,glycosilation
-ptm=[1565]HexNAc-3-Sulf-1-,689.194932,opt,X,glycosilation
-ptm=[1478]dHex-1-Hex-4-HexNAc-3-Pent-1-,1535.549579,opt,X,glycosilation
-ptm=[1434]HexNAc-1-NeuAc-1-,494.174789,opt,X,glycosilation
-ptm=[1616]dHex-1-HexNAc-4-,958.375399,opt,X,glycosilation
-ptm=[1754]dHex-1-Hex-2-HexNAc-4-NeuAc-2-,1864.67188,opt,X,glycosilation
-ptm=[1705]Hex-2-HexNAc-1-NeuGc-3-,1448.456013,opt,X,glycosilation
-ptm=[1686]dHex-1-Hex-3-HexNAc-2-NeuGc-1-,1345.465456,opt,X,glycosilation
-ptm=[1714]dHex-1-Hex-2-HexNAc-2-NeuGc-2-,1490.502964,opt,X,glycosilation
-ptm=[1584]dHex-2-Hex-3-,778.274288,opt,X,glycosilation
-ptm=[54]Glucuronyl,176.032088,opt,X,glycosilation
-ptm=[1690]Hex-7-Phos-3-,1374.268757,opt,X,glycosilation
-ptm=[1608]dHex-1-Hex-1-HexNAc-3-,917.34885,opt,X,glycosilation
-ptm=[1932]Hex-2-Sulf-1-,404.062462,opt,X,glycosilation
-ptm=[1723]Hex-8-Phos-3-,1536.321581,opt,X,glycosilation
-ptm=[1598]Hex-1-HexNAc-3-Sulf-1-,851.247756,opt,X,glycosilation
-ptm=[1626]Hex-2-HexNAc-3-Sulf-1-,1013.300579,opt,X,glycosilation
-ptm=[1557]dHex-3-Hex-4-HexNAc-4-Sulf-1-,1978.659326,opt,X,glycosilation
-ptm=[1454]dHex-1-Hex-3-HexNAc-2-Pent-1-,1170.417383,opt,X,glycosilation
-ptm=[1571]Hex-3-HexNAc-1-Me-1-,703.253493,opt,X,glycosilation
-ptm=[1555]dHex-1-Hex-5-HexNAc-5-,1971.718889,opt,X,glycosilation
-ptm=[149]Hex-1-HexNAc-1-NeuAc-1-,656.227613,opt,X,glycosilation
-ptm=[1778]dHex-2-Hex-4-HexNAc-4-,1752.644602,opt,X,glycosilation
-ptm=[1529]dHex-1-Hex-5-HexNAc-3-NeuAc-1-,1856.655561,opt,X,glycosilation
-ptm=[1643]dHex-2-Hex-2-HexNAc-2-Sulf-1-,1102.337025,opt,X,glycosilation
-ptm=[1550]dHex-1-Hex-5-HexNAc-3-NeuGc-1-Sulf-1-,1952.60729,opt,X,glycosilation
-ptm=[308]dHex-1-Hex-5-HexNAc-4-,1768.639517,opt,X,glycosilation
-ptm=[1538]dHex-1-Hex-5-HexNAc-4-Pent-1-,1900.681776,opt,X,glycosilation
-ptm=[1664]Hex-1-HexNAc-1-NeuAc-3-,1238.418446,opt,X,glycosilation
-ptm=[1707]dHex-1-Hex-2-HexNAc-3-NeuAc-1-Sulf-1-,1450.453905,opt,X,glycosilation
-ptm=[1561]Hex-3-HexNAc-6-NeuAc-1-,1995.730123,opt,X,glycosilation
-ptm=[1615]dHex-1-Hex-2-HexNAc-2-Sulf-1-,956.279116,opt,X,glycosilation
-ptm=[1681]Hex-2-HexNAc-2-NeuAc-2-,1312.455225,opt,X,glycosilation
-ptm=[1489]Hex-4-HexNAc-4-Pent-1-,1592.571043,opt,X,glycosilation
-ptm=[1558]Hex-3-HexNAc-7-Sulf-1-,1987.670893,opt,X,glycosilation
-ptm=[1684]dHex-2-Hex-2-HexNAc-2-NeuGc-1-,1329.470541,opt,X,glycosilation
-ptm=[1502]Hex-7-HexNAc-2-Phos-2-,1700.461172,opt,X,glycosilation
-ptm=[1481]dHex-2-Hex-4-HexNAc-3-,1549.56523,opt,X,glycosilation
-ptm=[1651]dHex-1-Hex-2-HexNAc-3-Sulf-1-,1159.358488,opt,X,glycosilation
-ptm=[1506]dHex-1-Hex-4-HexNAc-3-NeuGc-1-,1710.597652,opt,X,glycosilation
-ptm=[1840]dHex-1-Hex-7-HexNAc-4-,2092.745164,opt,X,glycosilation
-ptm=[1739]dHex-2-Hex-3-HexNAc-3-NeuAc-1-,1678.607823,opt,X,glycosilation
-ptm=[1685]Hex-2-HexNAc-5-,1339.50251,opt,X,glycosilation
-ptm=[1516]Hex-5-HexNAc-4-Me-2-Pent-1-,1782.655167,opt,X,glycosilation
-ptm=[1940]Hex-3-HexNAc-1-HexA-1-,865.269931,opt,X,glycosilation
-ptm=[1744]Hex-2-HexNAc-1-NeuGc-4-,1755.546345,opt,X,glycosilation
-ptm=[1445]dHex-2-Hex-2-,616.221465,opt,X,glycosilation
-ptm=[1668]dHex-2-Hex-3-HexNAc-2-Sulf-1-,1264.389848,opt,X,glycosilation
-ptm=[1379]dHex-1-Hex-6-,1118.37485,opt,X,glycosilation
-ptm=[1638]Hex-1-HexNAc-3-NeuGc-1-,1078.381273,opt,X,glycosilation
-ptm=[1455]Hex-3-HexNAc-2-NeuAc-1-,1183.412632,opt,X,glycosilation
-ptm=[1656]dHex-2-Hex-2-HexNAc-2-Sulf-2-,1182.293839,opt,X,glycosilation
-ptm=[910]Bacillosamine,228.111007,opt,X,glycosilation
-ptm=[1627]Hex-5-HexNAc-1-,1013.34349,opt,X,glycosilation
-ptm=[147]HexNAc-2-dHex-1-,552.216654,opt,X,glycosilation
-ptm=[1961]Hex-3-HexNAc-5-NeuAc-1-,1792.65075,opt,X,glycosilation
-ptm=[1551]Hex-4-HexNAc-5-NeuAc-1-,1954.703574,opt,X,glycosilation
-ptm=[1530]Hex-3-HexNAc-6-Sulf-2-,1864.548335,opt,X,glycosilation
-ptm=[1617]Hex-1-HexNAc-1-NeuAc-1-NeuGc-1-,963.317944,opt,X,glycosilation
-ptm=[1692]Hex-3-HexNAc-3-NeuAc-1-,1386.492005,opt,X,glycosilation
-ptm=[1779]Hex-6-HexNAc-4-,1784.634431,opt,X,glycosilation
-ptm=[1654]dHex-3-Hex-2-HexNAc-2-,1168.438119,opt,X,glycosilation
-ptm=[1470]Hex-6-HexNAc-2-Phos-1-,1458.442017,opt,X,glycosilation
-ptm=[1446]dHex-1-Hex-2-HexA-1-,646.195644,opt,X,glycosilation
-ptm=[1618]dHex-1-Hex-1-HexNAc-2-Kdn-1-,964.338345,opt,X,glycosilation
-ptm=[1662]Hex-1-HexNAc-2-NeuAc-2-Sulf-1-,1230.359217,opt,X,glycosilation
-ptm=[1753]Hex-10-Phos-3-,1860.427228,opt,X,glycosilation
-ptm=[1953]Hex-9-,1458.475412,opt,X,glycosilation
-ptm=[1719]dHex-4-Hex-2-HexNAc-3-,1517.5754,opt,X,glycosilation
-ptm=[1761]dHex-1-Hex-3-HexNAc-2-,1038.375125,opt,X,glycosilation
-ptm=[428]PhosphoHexNAc,283.045704,opt,X,glycosilation
-ptm=[1944]Hex-5-HexA-1-,986.296206,opt,X,glycosilation
-ptm=[1683]dHex-1-Hex-3-HexNAc-3-Sulf-1-,1321.411312,opt,X,glycosilation
-ptm=[1759]Hex-4-HexNAc-4-NeuAc-1-Sulf-3-,1991.494645,opt,X,glycosilation
-ptm=[1490]Hex-7-HexNAc-2-Phos-1-,1620.494841,opt,X,glycosilation
-ptm=[1437]Hex-1-HexNAc-1-dHex-1-Me-2-,539.221405,opt,X,glycosilation
-ptm=[1777]Hex-4-HexNAc-4-NeuAc-1-,1751.624201,opt,X,glycosilation
-ptm=[1652]dHex-1-HexNAc-5-,1161.454772,opt,X,glycosilation
-ptm=[1409]Hex-5-HexNAc-4-NeuAc-1-,1913.677025,opt,X,glycosilation
-ptm=[1709]dHex-3-Hex-1-HexNAc-3-Kdn-1-,1459.533535,opt,X,glycosilation
-ptm=[1501]dHex-1-Hex-4-HexNAc-3-NeuAc-1-,1694.602737,opt,X,glycosilation
-ptm=[1632]Hex-5-Phos-3-,1050.16311,opt,X,glycosilation
-ptm=[1775]dHex-1-Hex-3-HexNAc-5-,1647.613242,opt,X,glycosilation
-ptm=[1770]dHex-2-Hex-4-HexNAc-2-,1346.485857,opt,X,glycosilation
-ptm=[1682]dHex-2-Hex-2-HexNAc-2-NeuAc-1-,1313.475627,opt,X,glycosilation
-ptm=[1479]Hex-4-HexNAc-4-Sulf-1-,1540.485599,opt,X,glycosilation
-ptm=[454]HexN,161.068808,opt,X,glycosilation
-ptm=[1964]Hex-3-HexNAc-4-NeuAc-2-,1880.666794,opt,X,glycosilation
-ptm=[1425]Pentose,132.042259,opt,X,glycosilation
-ptm=[1435]HexNAc-1-NeuGc-1-,510.169704,opt,X,glycosilation
-ptm=[311]Hex-5-HexNAc-4-,1622.581608,opt,X,glycosilation
-ptm=[1630]Hex-1-HexNAc-1-NeuAc-2-Ac-2-,1031.344159,opt,X,glycosilation
-ptm=[1721]dHex-2-Hex-2-HexNAc-3-NeuGc-1-,1532.549914,opt,X,glycosilation
-ptm=[1954]dHex-2-Hex-3-HexNAc-3-Sulf-1-,1467.469221,opt,X,glycosilation
-ptm=[1518]dHex-1-Hex-6-HexNAc-3-Sulf-1-,1807.569782,opt,X,glycosilation
-ptm=[1522]Hex-6-HexNAc-4-Me-3-,1826.681382,opt,X,glycosilation
-ptm=[1764]dHex-1-Hex-3-HexNAc-2-Sulf-1-,1118.331939,opt,X,glycosilation
-ptm=[1688]dHex-1-Hex-2-HexNAc-3-NeuAc-1-,1370.49709,opt,X,glycosilation
-ptm=[1735]dHex-2-Hex-2-HexNAc-5-,1631.618328,opt,X,glycosilation
-ptm=[1493]dHex-1-Hex-5-HexNAc-3-Sulf-1-,1645.516959,opt,X,glycosilation
-ptm=[1943]dHex-1-Hex-4-HexA-1-,970.301291,opt,X,glycosilation
-ptm=[1589]Hex-2-HexNAc-2-Sulf-1-,810.221207,opt,X,glycosilation
-ptm=[1488]Hex-3-HexNAc-4-NeuAc-1-,1589.571378,opt,X,glycosilation
-ptm=[150]HexNAc-2-dHex-2-,698.274563,opt,X,glycosilation
-ptm=[1367]dHex-1-Hex-1-,308.110732,opt,X,glycosilation
-ptm=[1469]Hex-3-HexNAc-4-Pent-1-,1430.51822,opt,X,glycosilation
-ptm=[1521]Hex-7-HexNAc-3-Phos-1-,1823.574213,opt,X,glycosilation
-ptm=[1492]dHex-1-Hex-3-HexNAc-3-Pent-3-,1637.581274,opt,X,glycosilation
-ptm=[1483]Hex-4-HexNAc-3-NeuGc-1-,1564.539743,opt,X,glycosilation
-ptm=[1935]Hex-1-Pent-3-Me-1-,572.19525,opt,X,glycosilation
-ptm=[1720]Hex-2-HexNAc-3-NeuAc-1-NeuGc-1-,1531.529513,opt,X,glycosilation
-ptm=[156]Hex-1-HexNAc-2-dHex-2-,860.327386,opt,X,glycosilation
-ptm=[1706]dHex-4-Hex-1-HexNAc-1-Kdn-2-,1449.501567,opt,X,glycosilation
-ptm=[1462]dHex-1-Hex-5-HexNAc-2-,1362.480772,opt,X,glycosilation
-ptm=[1475]dHex-2-Hex-3-HexNAc-3-Pent-1-,1519.554665,opt,X,glycosilation
-ptm=[1540]Hex-3-HexNAc-7-,1907.714079,opt,X,glycosilation
-ptm=[1762]dHex-1-Hex-2-HexNAc-3-,1079.401674,opt,X,glycosilation
-ptm=[1472]dHex-1-Hex-5-HexNAc-2-Pent-1-,1494.52303,opt,X,glycosilation
-ptm=[1474]dHex-1-Hex-3-HexNAc-3-Pent-2-,1505.539015,opt,X,glycosilation
-ptm=[1588]dHex-1-Hex-1-HexNAc-1-NeuAc-1-,802.285522,opt,X,glycosilation
-ptm=[1674]dHex-1-Hex-3-HexA-1-HexNAc-2-Sulf-1-,1294.364027,opt,X,glycosilation
-ptm=[1597]dHex-1-Hex-2-HexA-1-HexNAc-1-,849.275017,opt,X,glycosilation
-ptm=[1514]Hex-7-HexNAc-3-,1743.607882,opt,X,glycosilation
-ptm=[1966]dHex-2-Hex-2-HexNAc-6-Sulf-1-,1914.654515,opt,X,glycosilation
-ptm=[1941]Hex-1-HexNAc-2-dHex-2-Sulf-1-,940.284201,opt,X,glycosilation
-ptm=[153]Hex-2-HexNAc-2-,730.264392,opt,X,glycosilation
-ptm=[1642]Hex-2-HexNAc-2-NeuAc-1-Sulf-1-,1101.316623,opt,X,glycosilation
-ptm=[1659]Hex-6-Phos-3-,1212.215934,opt,X,glycosilation
-ptm=[1776]Hex-3-HexNAc-6-,1704.634706,opt,X,glycosilation
-ptm=[1536]dHex-1-Hex-4-HexNAc-5-Sulf-1-,1889.62288,opt,X,glycosilation
-ptm=[1476]dHex-1-Hex-3-HexNAc-4-Sulf-1-,1524.490684,opt,X,glycosilation
-ptm=[1531]Hex-9-HexNAc-2-,1864.634157,opt,X,glycosilation
-ptm=[1592]HexNAc-1-NeuGc-2-,817.260035,opt,X,glycosilation
-ptm=[1580]dHex-1-HexNAc-3-,755.296027,opt,X,glycosilation
-ptm=[1549]Hex-7-HexNAc-4-,1946.687255,opt,X,glycosilation
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diff --git a/docs/source/LICENSE.txt b/docs/source/LICENSE.txt
new file mode 100644
index 00000000..4f1f9557
--- /dev/null
+++ b/docs/source/LICENSE.txt
@@ -0,0 +1,435 @@
+Attribution-NonCommercial-ShareAlike 4.0 International
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\ No newline at end of file
diff --git a/docs/source/_static/css/custom.css b/docs/source/_static/css/custom.css
new file mode 100644
index 00000000..fe30d213
--- /dev/null
+++ b/docs/source/_static/css/custom.css
@@ -0,0 +1,14 @@
+/* replace the copyright to eliminate the copyright symbol enforced by
+ the ReadTheDocs theme but eschewed by our legal team */
+ div[role=contentinfo] {
+ visibility: hidden;
+ position: relative;
+}
+
+div[role=contentinfo]:after {
+ visibility: visible;
+ position: absolute;
+ top: 0;
+ left: 0;
+ content: "Creative Commons CC-BY-SA 4.0";
+}
\ No newline at end of file
diff --git a/docs/source/api/ms2pip.constants.rst b/docs/source/api/ms2pip.constants.rst
new file mode 100644
index 00000000..f7aeb2e8
--- /dev/null
+++ b/docs/source/api/ms2pip.constants.rst
@@ -0,0 +1,8 @@
+****************
+ms2pip.constants
+****************
+
+.. py:data:: ms2pip.constants.MODELS
+ :type: dict
+
+ Built-in prediction models
diff --git a/docs/source/api/ms2pip.correlation.rst b/docs/source/api/ms2pip.correlation.rst
new file mode 100644
index 00000000..fcb7a5fc
--- /dev/null
+++ b/docs/source/api/ms2pip.correlation.rst
@@ -0,0 +1,6 @@
+******************
+ms2pip.correlation
+******************
+
+.. automodule:: ms2pip.correlation
+ :members:
diff --git a/docs/source/api/ms2pip.exceptions.rst b/docs/source/api/ms2pip.exceptions.rst
new file mode 100644
index 00000000..328b9d73
--- /dev/null
+++ b/docs/source/api/ms2pip.exceptions.rst
@@ -0,0 +1,6 @@
+*****************
+ms2pip.exceptions
+*****************
+
+.. automodule:: ms2pip.exceptions
+ :members:
diff --git a/docs/source/api/ms2pip.result.rst b/docs/source/api/ms2pip.result.rst
new file mode 100644
index 00000000..aee97a08
--- /dev/null
+++ b/docs/source/api/ms2pip.result.rst
@@ -0,0 +1,5 @@
+*************
+ms2pip.result
+*************
+
+.. automodule:: ms2pip.result
diff --git a/docs/source/api/ms2pip.rst b/docs/source/api/ms2pip.rst
new file mode 100644
index 00000000..9e25652e
--- /dev/null
+++ b/docs/source/api/ms2pip.rst
@@ -0,0 +1,9 @@
+******
+ms2pip
+******
+
+.. automodule:: ms2pip
+ :members:
+ :undoc-members:
+ :imported-members:
+ :exclude-members: filterwarnings
diff --git a/docs/source/api/ms2pip.search-space.rst b/docs/source/api/ms2pip.search-space.rst
new file mode 100644
index 00000000..456f2bef
--- /dev/null
+++ b/docs/source/api/ms2pip.search-space.rst
@@ -0,0 +1,6 @@
+*******************
+ms2pip.search_space
+*******************
+
+.. automodule:: ms2pip.search_space
+ :members:
diff --git a/docs/source/api/ms2pip.spectrum-input.rst b/docs/source/api/ms2pip.spectrum-input.rst
new file mode 100644
index 00000000..e924bbaa
--- /dev/null
+++ b/docs/source/api/ms2pip.spectrum-input.rst
@@ -0,0 +1,6 @@
+*********************
+ms2pip.spectrum_input
+*********************
+
+.. automodule:: ms2pip.spectrum_input
+ :members:
diff --git a/docs/source/api/ms2pip.spectrum-output.rst b/docs/source/api/ms2pip.spectrum-output.rst
new file mode 100644
index 00000000..6cb1b366
--- /dev/null
+++ b/docs/source/api/ms2pip.spectrum-output.rst
@@ -0,0 +1,11 @@
+**********************
+ms2pip.spectrum_output
+**********************
+
+.. automodule:: ms2pip.spectrum_output
+ :members:
+
+.. py:data:: ms2pip.spectrum_output.SUPPORTED_FORMATS
+ :type: dict
+
+ Supported file formats and respective :py:class:`_Writer` class for spectrum output.
diff --git a/docs/source/api/ms2pip.spectrum.rst b/docs/source/api/ms2pip.spectrum.rst
new file mode 100644
index 00000000..0bcea63b
--- /dev/null
+++ b/docs/source/api/ms2pip.spectrum.rst
@@ -0,0 +1,5 @@
+***************
+ms2pip.spectrum
+***************
+
+.. automodule:: ms2pip.spectrum
diff --git a/docs/source/cli/cli.rst b/docs/source/cli/cli.rst
new file mode 100644
index 00000000..a0288d49
--- /dev/null
+++ b/docs/source/cli/cli.rst
@@ -0,0 +1,7 @@
+**********************
+Command line interface
+**********************
+
+.. click:: ms2pip.__main__:cli
+ :prog: ms2pip
+ :nested: full
diff --git a/docs/source/conf.py b/docs/source/conf.py
new file mode 100644
index 00000000..08896c69
--- /dev/null
+++ b/docs/source/conf.py
@@ -0,0 +1,67 @@
+"""Configuration file for the Sphinx documentation builder."""
+
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath("../../"))
+
+from ms2pip import __version__
+
+# Project information
+project = "ms2pip"
+author = "CompOmics"
+github_project_url = "https://github.com/compomics/ms2pip/"
+github_doc_root = "https://github.com/compomics/ms2pip/tree/releases/docs/"
+
+# Version
+release = __version__
+
+# General configuration
+extensions = [
+ "sphinx.ext.autodoc",
+ "sphinx.ext.autosectionlabel",
+ "sphinx.ext.autosummary",
+ "sphinx.ext.napoleon",
+ "sphinx.ext.intersphinx",
+ "sphinx.ext.viewcode",
+ "sphinx_click.ext",
+ "sphinx_rtd_theme",
+]
+source_suffix = [".rst", ".md"]
+master_doc = "index"
+exclude_patterns = ["_build"]
+
+# Options for HTML output
+html_theme = "sphinx_rtd_theme"
+html_static_path = ["_static"]
+html_css_files = ["css/custom.css"]
+
+# Autodoc options
+autodoc_default_options = {"members": True, "show-inheritance": True}
+autodoc_member_order = "bysource"
+autodoc_typehints = "description"
+autoclass_content = "init"
+# autodoc_type_aliases = {
+# "Path": "pathlib.Path",
+# "DataFrame": "pandas.DataFrame",
+# "Series": "pandas.Series",
+# "PSMList": "psm_utils.psm_list.PSMList",
+# }
+
+# Intersphinx options
+intersphinx_mapping = {
+ "python": ("https://docs.python.org/3", None),
+ "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
+ "numpy": ("https://numpy.org/doc/stable/", None),
+ "psm_utils": ("https://psm-utils.readthedocs.io/en/stable/", None),
+}
+
+# Napoleon options
+# numpydoc_xref_aliases = autodoc_type_aliases
+
+
+def setup(app):
+ config = {
+ # "auto_toc_tree_section": "Contents",
+ "enable_eval_rst": True,
+ }
diff --git a/docs/source/index.rst b/docs/source/index.rst
new file mode 100644
index 00000000..39e1974e
--- /dev/null
+++ b/docs/source/index.rst
@@ -0,0 +1,33 @@
+.. include:: ../../README.rst
+
+
+.. toctree::
+ :caption: About
+ :hidden:
+ :includehidden:
+ :maxdepth: 2
+
+ Home
+ installation
+ usage
+ prediction-models
+
+
+.. toctree::
+ :caption: Command line interface
+ :hidden:
+ :includehidden:
+ :glob:
+ :maxdepth: 2
+
+ cli/*
+
+
+.. toctree::
+ :caption: Python API reference
+ :hidden:
+ :includehidden:
+ :glob:
+ :maxdepth: 2
+
+ api/*
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
new file mode 100644
index 00000000..e530b455
--- /dev/null
+++ b/docs/source/installation.rst
@@ -0,0 +1,63 @@
+Installation
+============
+
+Pip package
+-----------
+.. image:: https://flat.badgen.net/badge/install%20with/pip/green
+ :target: https://pypi.org/project/ms2pip/
+
+With Python 3.8 or higher, run:
+
+.. code-block:: bash
+
+ pip install ms2pip
+
+Compiled wheels are available for various Python versions on 64bit Linux,
+Windows, and macOS. This should install MS²PIP in a few seconds. For other
+platforms, MS²PIP can be built from source, although it can take a while
+to compile the large prediction models.
+
+We recommend using a `venv `__ or
+`conda `__ virtual environment.
+
+Conda package
+-------------
+.. image:: https://flat.badgen.net/badge/install%20with/bioconda/green
+ :target: https://bioconda.github.io/recipes/ms2pip/README.html
+
+Install with activated bioconda and conda-forge channels:
+
+.. code-block:: bash
+
+ conda install -c defaults -c bioconda -c conda-forge ms2pip
+
+Bioconda packages are only available for Linux and macOS.
+
+Docker container
+----------------
+.. image:: https://flat.badgen.net/badge/pull/biocontainer/blue?icon=docker
+ :target: https://quay.io/repository/biocontainers/ms2pip
+
+First check the latest version tag on `biocontainers/ms2pip/tags `__. Then pull and run the container with:
+
+.. code-block:: bash
+
+ docker container run -v :/data -w /data quay.io/biocontainers/ms2pip: ms2pip
+
+where `` is the absolute path to the directory with your MS²PIP input files, `` is the container version tag, and `` are the ms2pip command line options (see :ref:`Command line interface`).
+
+For development
+---------------
+Clone this repository and use pip to install an editable version:
+
+.. code-block:: bash
+
+ pip install --editable .
+
+
+Optionally, add the ``[dev,docs]`` extras to install the development and
+documentation dependencies:
+
+.. code-block:: bash
+
+ pip install --editable .[dev,docs]
diff --git a/docs/source/prediction-models.rst b/docs/source/prediction-models.rst
new file mode 100644
index 00000000..f1a71a0d
--- /dev/null
+++ b/docs/source/prediction-models.rst
@@ -0,0 +1,402 @@
+Prediction models
+=================
+
+Pre-trained MS²PIP models
+-------------------------
+
+MS²PIP includes multiple specialized prediction models, fit for peptide spectra
+with different properties. These properties include fragmentation method,
+instrument, labeling techniques and modifications. As all of these properties
+can influence fragmentation patterns, it is important to match the MS²PIP model
+to the properties of your experimental dataset.
+
+All models are downloaded automatically upon first use. Model files can also be downloaded manually
+from `genesis.ugent.be/uvpublicdata/ms2pip `_.
+
+MS2 acquisition information and peptide properties of the models' training datasets:
+
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+| Model | Fragmentation method | MS2 mass analyzer | Peptide properties |
++==============+======================+========================================+====================================================+
+| HCD2019 | HCD | Orbitrap | Tryptic digest |
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+| HCD2021 | HCD | Orbitrap | Tryptic / Chymotrypsin digest |
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+| CID | CID | Linear ion trap | Tryptic digest |
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+| iTRAQ | HCD | Orbitrap | Tryptic digest, iTRAQ-labeled |
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+| iTRAQphospho | HCD | Orbitrap | Tryptic digest, iTRAQ-labeled, enriched for |
+| | | | phosphorylation |
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+| TMT | HCD | Orbitrap | Tryptic digest, TMT-labeled |
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+| TTOF5600 | CID | Quadrupole time-of-flight | Tryptic digest |
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+| HCDch2 | HCD | Orbitrap | Tryptic digest |
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+| CIDch2 | CID | Linear ion trap | Tryptic digest |
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+| Immuno-HCD | HCD | Orbitrap | Immunopeptides |
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+| CID-TMT | CID | Linear ion trap | Tryptic digest, TMT-labeled |
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+| timsTOF2023 | CID | Ion mobility quadrupole time-of-flight | Tryptic-, elastase digest, immuno class 1 |
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+| timsTOF2024 | CID | Ion mobility quadrupole time-of-flight | Tryptic-, elastase digest, immuno class 1 & class 2|
++--------------+----------------------+----------------------------------------+----------------------------------------------------+
+
+Models, version numbers, and the train and test datasets used to create each model:
+
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+| Model | Current version | Train-test dataset (unique peptides) | Evaluation dataset (unique peptides) | Median Pearson correlation on evaluation|
+| | | | | dataset |
++===============+===================+=================================================+===================================================+=========================================+
+| HCD2019 | v20190107 | `MassIVE-KB`_ (1 623 712) | `PXD008034`_ (35 269) | 0.903786 |
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+| CID | v20190107 | `NIST CID Human`_ (340 356) | `NIST CID Yeast`_ (92 609) | 0.904947 |
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+| iTRAQ | v20190107 | `NIST iTRAQ`_ (704 041) | `PXD001189`_ (41 502) | 0.905870 |
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+| iTRAQphospho | v20190107 | `NIST iTRAQ phospho`_ (183 383) | `PXD001189`_ (9 088) | 0.843898 |
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+| TMT | v20190107 | `Peng Lab TMT Spectral Library`_ (1 185 547) | `PXD009495`_ (36 137) | 0.950460 |
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+| TTOF5600 | v20190107 | `PXD000954`_ (215 713) | `PXD001587`_ (15 111) | 0.746823 |
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+| HCDch2 | v20190107 | `MassIVE-KB`_ (1 623 712) | `PXD008034`_ (35 269) | 0.903786 (+) |
+| | | | | 0.644162 (++) |
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+| CIDch2 | v20190107 | `NIST CID Human`_ (340 356) | `NIST CID Yeast`_ (92 609) | 0.904947 (+) |
+| | | | | 0.813342 (++) |
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+| HCD2021 | v20210416 | Combined dataset (520 579) | `PXD008034`_ (35 269) | 0.932361 |
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+| Immuno-HCD | v20210316 | Combined dataset (460 191) | `PXD005231 (HLA-I)`_ (46 753) | 0.963736 |
+| | | | `PXD020011 (HLA-II)`_ (23 941) | |
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+| CID-TMT | v20220104 | `PXD041002`_ (72 138) | `PXD005890`_ (69 768) | 0.851085 |
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+| timsTOF2023 | v20230912 | Combined dataset (234 973) | `PXD043026` `PXD046535` `PXD046543` | 0.892540 (tryptic) |
+| | | | | 0.871258 (elastase) |
+| | | | | 0.899834 (class I) |
+| | | | | 0.635548 (class II) |
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+| timsTOF2024 | v20240105 | Combined dataset (480 024) | `PXD043026` `PXD046535` `PXD046543` `PXD038782` | 0.883270 (tryptic) |
+| | | | | 0.814374 (elastase) |
+| | | | | 0.887192 (class I) |
+| | | | | 0.847951 (class II) |
++---------------+-------------------+-------------------------------------------------+---------------------------------------------------+-----------------------------------------+
+
+Training new MS²PIP models
+--------------------------
+
+[todo]
+
+
+Prediction features
+-------------------
+
+The table below lists and describes all features generated and used by MS²PIP. These are mostly
+based on four amino acid properties (basicity, hydrophobicity, helicity and isoelectric point)
+for the full precursor and for the N- and C-terminal ions.
+
++-----------------+----------------------------------------------------------------------+
+| Feature | Description |
++=================+======================================================================+
+| ``p_length`` | Precursor length |
++-----------------+----------------------------------------------------------------------+
+| ``p_charge`` | Precursor charge |
++-----------------+----------------------------------------------------------------------+
+| ``p_charge1`` | Precursor charge is 1 (one-hot encoding) |
++-----------------+----------------------------------------------------------------------+
+| ``p_charge2`` | Precursor charge is 2 (one-hot encoding) |
++-----------------+----------------------------------------------------------------------+
+| ``p_charge3`` | Precursor charge is 3 (one-hot encoding) |
++-----------------+----------------------------------------------------------------------+
+| ``p_charge4`` | Precursor charge is 4 (one-hot encoding) |
++-----------------+----------------------------------------------------------------------+
+| ``p_charge5`` | Precursor charge is 5 (one-hot encoding) |
++-----------------+----------------------------------------------------------------------+
+| ``p_basi_min`` | Minimum basicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_basi_q1`` | First quartile of basicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_basi_q2`` | Second quartile of basicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_basi_q3`` | Third quartile of basicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_basi_max`` | Maximum basicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_heli_min`` | Minimum helicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_heli_q1`` | First quartile of helicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_heli_q2`` | Second quartile of helicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_heli_q3`` | Third quartile of helicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_heli_max`` | Maximum helicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_hydro_min`` | Minimum hydrophobicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_hydro_q1`` | First quartile of hydrophobicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_hydro_q2`` | Second quartile of hydrophobicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_hydro_q3`` | Third quartile of hydrophobicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_hydro_max`` | Maximum hydrophobicity of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_iso_min`` | Minimum isoelectric point of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_iso_q1`` | First quartile of isoelectric point of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_iso_q2`` | Second quartile of isoelectric point of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_iso_q3`` | Third quartile of isoelectric point of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``p_iso_max`` | Maximum isoelectric point of the precursor |
++-----------------+----------------------------------------------------------------------+
+| ``n_length`` | Length of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_length`` | Length of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_A`` | Count of amino acid 'A' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_A`` | Count of amino acid 'A' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_C`` | Count of amino acid 'C' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_C`` | Count of amino acid 'C' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_D`` | Count of amino acid 'D' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_D`` | Count of amino acid 'D' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_E`` | Count of amino acid 'E' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_E`` | Count of amino acid 'E' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_F`` | Count of amino acid 'F' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_F`` | Count of amino acid 'F' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_G`` | Count of amino acid 'G' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_G`` | Count of amino acid 'G' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_H`` | Count of amino acid 'H' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_H`` | Count of amino acid 'H' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_I`` | Count of amino acid 'I' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_I`` | Count of amino acid 'I' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_K`` | Count of amino acid 'K' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_K`` | Count of amino acid 'K' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_M`` | Count of amino acid 'M' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_M`` | Count of amino acid 'M' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_N`` | Count of amino acid 'N' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_N`` | Count of amino acid 'N' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_P`` | Count of amino acid 'P' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_P`` | Count of amino acid 'P' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_Q`` | Count of amino acid 'Q' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_Q`` | Count of amino acid 'Q' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_R`` | Count of amino acid 'R' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_R`` | Count of amino acid 'R' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_S`` | Count of amino acid 'S' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_S`` | Count of amino acid 'S' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_T`` | Count of amino acid 'T' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_T`` | Count of amino acid 'T' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_V`` | Count of amino acid 'V' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_V`` | Count of amino acid 'V' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_W`` | Count of amino acid 'W' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_W`` | Count of amino acid 'W' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_count_Y`` | Count of amino acid 'Y' in the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_count_Y`` | Count of amino acid 'Y' in the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``p0_basi`` | basicity of the first amino acid of the peptide |
++-----------------+----------------------------------------------------------------------+
+| ``p-1_basi`` | basicity of the last amino acid of the peptide |
++-----------------+----------------------------------------------------------------------+
+| ``pi-1_basi`` | basicity of the amino acid before the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``pi_basi`` | basicity of the amino acid at the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``pi+1_basi`` | basicity of the 1st amino acid after the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``pi+2_basi`` | basicity of the 2nd amino acid after the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``n_basi_sum`` | Sum of basicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_basi_min`` | Minimum basicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_basi_q1`` | First quartile of basicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_basi_q2`` | Second quartile of basicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_basi_q3`` | Third quartile of basicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_basi_max`` | Maximum basicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_basi_sum`` | Sum of basicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_basi_min`` | Minimum basicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_basi_q1`` | First quartile of basicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_basi_q2`` | Second quartile of basicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_basi_q3`` | Third quartile of basicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_basi_max`` | Maximum basicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``p0_heli`` | Helicity of the first amino acid of the peptide |
++-----------------+----------------------------------------------------------------------+
+| ``p-1_heli`` | Helicity of the last amino acid of the peptide |
++-----------------+----------------------------------------------------------------------+
+| ``pi-1_heli`` | Helicity of the amino acid before the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``pi_heli`` | Helicity of the amino acid at the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``pi+1_heli`` | Helicity of the 1st amino acid after the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``pi+2_heli`` | Helicity of the 2nd amino acid after the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``n_heli_sum`` | Sum of helicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_heli_min`` | Minimum helicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_heli_q1`` | First quartile of helicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_heli_q2`` | Second quartile of helicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_heli_q3`` | Third quartile of helicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_heli_max`` | Maximum helicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_heli_sum`` | Sum of helicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_heli_min`` | Minimum helicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_heli_q1`` | First quartile of helicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_heli_q2`` | Second quartile of helicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_heli_q3`` | Third quartile of helicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_heli_max`` | Maximum helicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``p0_hydro`` | Hydrophobicity of the first amino acid of the peptide |
++-----------------+----------------------------------------------------------------------+
+| ``p-1_hydro`` | Hydrophobicity of the last amino acid of the peptide |
++-----------------+----------------------------------------------------------------------+
+| ``pi-1_hydro`` | Hydrophobicity of the amino acid before the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``pi_hydro`` | Hydrophobicity of the amino acid at the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``pi+1_hydro`` | Hydrophobicity of the 1st amino acid after the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``pi+2_hydro`` | Hydrophobicity of the 2nd amino acid after the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``n_hydro_sum`` | Sum of hydrophobicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_hydro_min`` | Minimum hydrophobicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_hydro_q1`` | First quartile of hydrophobicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_hydro_q2`` | Second quartile of hydrophobicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_hydro_q3`` | Third quartile of hydrophobicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_hydro_max`` | Maximum hydrophobicity of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_hydro_sum`` | Sum of hydrophobicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_hydro_min`` | Minimum hydrophobicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_hydro_q1`` | First quartile of hydrophobicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_hydro_q2`` | Second quartile of hydrophobicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_hydro_q3`` | Third quartile of hydrophobicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_hydro_max`` | Maximum hydrophobicity of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``p0_iso`` | Isoelectric point of the first amino acid of the peptide |
++-----------------+----------------------------------------------------------------------+
+| ``p-1_iso`` | Isoelectric point of the last amino acid of the peptide |
++-----------------+----------------------------------------------------------------------+
+| ``pi-1_iso`` | Isoelectric point of the amino acid before the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``pi_iso`` | Isoelectric point of the amino acid at the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``pi+1_iso`` | Isoelectric point of the 1st amino acid after the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``pi+2_iso`` | Isoelectric point of the 2nd amino acid after the fragmentation site |
++-----------------+----------------------------------------------------------------------+
+| ``n_iso_sum`` | Sum of isoelectric points of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_iso_min`` | Minimum isoelectric point of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_iso_q1`` | First quartile of isoelectric points of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_iso_q2`` | Second quartile of isoelectric points of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_iso_q3`` | Third quartile of isoelectric points of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``n_iso_max`` | Maximum isoelectric point of the N-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_iso_sum`` | Sum of isoelectric points of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_iso_min`` | Minimum isoelectric point of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_iso_q1`` | First quartile of isoelectric points of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_iso_q2`` | Second quartile of isoelectric points of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_iso_q3`` | Third quartile of isoelectric points of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+| ``c_iso_max`` | Maximum isoelectric point of the C-terminal ion |
++-----------------+----------------------------------------------------------------------+
+
+
+.. _MassIVE-KB: https://doi.org/10.1016/j.cels.2018.08.004
+.. _PXD008034: https://doi.org/10.1016/j.jprot.2017.12.006
+.. _NIST CID Human: https://chemdata.nist.gov/
+.. _NIST CID Yeast: https://chemdata.nist.gov/
+.. _NIST iTRAQ: https://chemdata.nist.gov/
+.. _PXD001189: https://doi.org/10.1182/blood-2016-05-714048
+.. _NIST iTRAQ phospho: https://chemdata.nist.gov/
+.. _PXD009495: https://doi.org/10.15252/msb.20188242
+.. _Peng Lab TMT Spectral Library: https://doi.org/10.1021/acs.jproteome.8b00594
+.. _PXD000954: https://doi.org/10.1038/sdata.2014.31
+.. _PXD001587: https://doi.org/10.1038/nmeth.3255
+.. _PXD005231 (HLA-I): https://doi.org/10.1101/098780
+.. _PXD020011 (HLA-II): https://doi.org/10.3389/fimmu.2020.01981
+.. _PXD041002: https://doi.org/10.1093/nar/gkad335
+.. _PXD005890: https://doi.org/10.1021/acs.jproteome.7b00091
+.. _Training new MS²PIP models: http://compomics.github.io/projects/ms2pip_c/wiki/Training-new-MS2PIP-models.html
diff --git a/docs/source/usage.rst b/docs/source/usage.rst
new file mode 100644
index 00000000..aca0d152
--- /dev/null
+++ b/docs/source/usage.rst
@@ -0,0 +1,173 @@
+#####
+Usage
+#####
+
+
+Usage modes
+===========
+
+MS²PIP has various usage modes that each can be accessed through the command-line interface, or
+through the Python API.
+
+``predict-single``
+------------------
+
+In this mode, a single peptide spectrum is predicted with MS²PIP and optionally plotted with
+`spectrum_utils `_. For instance,
+
+.. code-block:: sh
+
+ ms2pip predict-single "PGAQANPYSR/3" --model TMT --plot
+
+results in:
+
+.. image:: ../../img/PGAQANPYSR-3-TMT.png
+ :alt: Predicted spectrum
+
+
+``predict-batch``
+-----------------
+
+Provide a list of peptidoforms (see :ref:`Peptides / PSMs`) to predict multiple spectra at once.
+For instance,
+
+.. code-block:: sh
+
+ ms2pip predict-batch peptides.tsv --model TMT
+
+results in a file ``peptides_predictions.csv`` with the predicted spectra.
+
+
+``predict-library``
+-------------------
+
+Predict spectra for a full peptide search space generated from a protein FASTA file. Various
+peptide search space parameters can be configured to control the peptidoforms that are generated.
+See :py:mod:`ms2pip.search_space` for more information.
+
+Minimal example:
+
+.. code-block:: sh
+
+ ms2pip predict-library proteins.fasta
+
+This mode was first developed in collaboration with the ProGenTomics group for the
+`MS²PIP for DIA `_ project.
+
+``correlate``
+-------------
+
+Predict spectrum intensities for a list of peptides and correlate them with observed intensities
+from a spectrum file. This mode is useful for evaluating MS²PIP models or for (re)scoring
+peptide-spectrum matches.
+
+For instance:
+
+.. code-block:: sh
+
+ ms2pip correlate results.sage.tsv --spectrum-file spectra.mgf
+
+
+``get-training-data``
+---------------------
+
+Given a list of peptides and corresponding spectra, generate training data for MS²PIP. This
+includes observed intensities for the supported ion types and the feature vectors for each ion.
+For more info, see :ref:`Training new MS²PIP models`.
+
+
+``annotate-spectra``
+---------------------
+
+Given a list of peptides annotate the peaks in the corresponding spectra.
+
+
+Input
+=====
+
+Peptides / PSMs
+---------------
+
+PSM file types
+~~~~~~~~~~~~~~
+
+For peptide information input, MS²PIP accepts any file format that is supported by
+:py:mod:`psm_utils`. See
+`Supported file formats `_ for
+the full list. The simplest format is a tab-separated file with at least the columns
+``peptidoform`` and ``spectrum_id`` present.
+
+- ``peptidoform`` is the full
+ `ProForma 2.0 notation `_ including amino acid
+ modifications and precursor charge state.
+- ``spectrum_id`` should match the ``TITLE`` or ``nativeID`` field of the related spectrum in the
+ optional MGF or mzML file, if provided. Otherwise, any value is accepted.
+
+For example:
+
+.. code-block::
+
+ peptidoform spectrum_id
+ RNVIM[Oxidation]DKVAK/2 1
+ KHLEQHPK/2 2
+ ...
+
+See :py:mod:`psm_utils.io.tsv` for the full specification.
+
+Peptide sequence properties
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Peptides must be strictly longer than 2 and shorter than 100 amino acids and
+cannot contain the following amino acid one-letter codes: B, J, O, U, X or Z.
+Peptides not fulfilling these requirements will be filtered out and will not be
+reported in the output.
+
+Amino acid modifications
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+Amino acid modification labels must be resolvable to a known mass shift. This means that
+accepted labels are:
+
+- A name or accession from an controlled vocabulary, such as Unimod or PSI-MOD. (e.g.,
+ ``Oxidation``, ``U:Oxidation``, ``U:35``, ``MOD:00046``...)
+- An elemental formula (e.g, ``Formula:C12H20O2``)
+- A mass shift in Da (e.g., ``+15.9949``)
+
+Any unresolvable modification will result in an error. If needed, PSM files can be converted with
+:py:mod:`psm_utils.io` and modifications can be renamed with the
+:py:meth:`~psm_utils.psm_list.PSMList.rename_modifications()` method.
+
+Spectrum file
+-------------
+
+In the :ref:`correlate` and :ref:`get-training-data` usage modes, an MGF or mzML file with observed
+spectra must be provided to MS²PIP.
+
+Make sure that the PSM file ``spectrum_id`` matches the MGF ``TITLE`` field or mzML ``nativeID``
+fields. If the values of these fields are different, but the PSM file ``spectrum_id`` is embedded
+in them, the ``spectrum_id_pattern`` argument can be used to extract the ``spectrum_id`` from
+the ``TITLE`` or ``nativeID`` fields with a regular expression pattern. For example, if an MGF
+entry has ``TITLE=scan=1``, but the PSM file has ``spectrum_id=1``, ``spectrum_id_pattern`` can be
+set to ``scan=(\d+)``. Note that the pattern must contain a single matching group that captures the
+``spectrum_id``.
+
+.. note::
+ Find out more about regular expression patterns and try them on
+ `regex101.com `_. You can try out the above examples at
+ https://regex101.com/r/TynuIe/1.
+
+Spectra present in the spectrum file, but missing in the PSM file (and vice versa) will be skipped.
+
+
+Output
+======
+
+MS²PIP supports various spectral library output formats, including TSV, MGF, MSP, Spectronaut CSV,
+BiblioSpec/Skyline SSL and MS2, and Encycopedia DLIB.
+
+Note that the normalization of intensities depends on the output file format. In the TSV file
+output, intensities are log2-transformed. To "unlog" the intensities, use the following formula:
+
+.. code-block::
+
+ intensity = (2 ** log2_intensity) - 0.001
diff --git a/fasta2speclib/fasta2speclib.py b/fasta2speclib/fasta2speclib.py
deleted file mode 100644
index a0dcba32..00000000
--- a/fasta2speclib/fasta2speclib.py
+++ /dev/null
@@ -1,702 +0,0 @@
-"""
-Create a spectral library starting from a proteome in fasta format.
-
-The script runs through the following steps:
-- In silico cleavage of proteins from the fasta file
-- Remove peptide redundancy
-- Add all variations of variable modifications (max 7 PTMs/peptide)
-- Add variations on charge state
-- Predict spectra with MS2PIP
-- Write to various output file formats
-
-
-Unspecific cleavage (e.g. for immunopeptidomics) is supported by setting
-``cleavage_rule`` to ``unspecific``.
-
-
-Decoys added by reversing sequences, keeping the N-terminal residue inplace.
-
-
-Modifications:
-- Peptides can carry only one modification per site (side chain or terminus).
-- Protein terminal modifications take precedence over peptide terminal modifications.
-- Terminal modifications can have site specificity (e.g. N-term K or N-term P).
-
-"""
-from __future__ import annotations
-
-__author__ = "Ralf Gabriels"
-__copyright__ = "CompOmics"
-__credits__ = ["Ralf Gabriels", "Sven Degroeve", "Lennart Martens"]
-__license__ = "Apache License, Version 2.0"
-__email__ = "Ralf.Gabriels@ugent.be"
-
-import argparse
-import json
-import logging
-import multiprocessing
-import multiprocessing.dummy
-from collections import defaultdict
-from functools import cmp_to_key, partial
-from itertools import chain, product
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Union
-
-import pandas as pd
-from ms2pip.ms2pip_tools import spectrum_output
-from ms2pip.ms2pipC import MODELS, MS2PIP
-from ms2pip.peptides import Modifications as MS2PIPModifications
-from ms2pip.retention_time import RetentionTime
-from pydantic import BaseModel, validator
-from pyteomics.fasta import FASTA, Protein, decoy_db
-from pyteomics.parser import icleave
-from rich.logging import RichHandler
-from rich.progress import track
-
-logger = logging.getLogger(__name__)
-
-
-class Peptide(BaseModel):
- """Peptide representation within the fasta2speclib search space."""
- sequence: str
- proteins: List[str]
- is_n_term: Optional[bool] = None
- is_c_term: Optional[bool] = None
- modification_options: List[str] = None
- charge_options: List[int] = None
-
-
-class ModificationConfig(BaseModel):
- """Configuration for a single modification in the search space."""
- name: str
- mass_shift: float
- unimod_accession: Optional[int] = None
- amino_acid: Optional[str] = None
- peptide_n_term: Optional[bool] = False
- protein_n_term: Optional[bool] = False
- peptide_c_term: Optional[bool] = False
- protein_c_term: Optional[bool] = False
- fixed: Optional[bool] = False
-
- @validator("protein_c_term", always=True) # Validate on last target in model
- def modification_must_have_target(cls, v, values):
- target_fields = [
- "amino_acid",
- "peptide_n_term",
- "protein_n_term",
- "peptide_c_term",
- "protein_c_term",
- ]
- if not any(t in values and values[t] for t in target_fields):
- raise ValueError(
- "Modifications must have at least one target (amino acid or N/C-term)."
- )
- return v
-
-
-DEFAULT_MODIFICATIONS = [
- ModificationConfig(
- name="Oxidation",
- unimod_accession=35,
- mass_shift=15.994915,
- amino_acid="M",
- ),
- ModificationConfig(
- name="Carbamidomethyl",
- mass_shift=57.021464,
- unimod_accession=4,
- amino_acid="C",
- fixed=True,
- ),
-]
-
-
-class Configuration(BaseModel):
- """Configuration for fasta2speclib."""
-
- fasta_filename: Union[str, Path]
- output_filename: Optional[str] = None
- output_filetype: Optional[List[str]] = None
- charges: List[int] = [2, 3]
- min_length: int = 8
- max_length: int = 30
- cleavage_rule: str = "trypsin"
- missed_cleavages: int = 2
- semi_specific: bool = False
- modifications: List[ModificationConfig] = DEFAULT_MODIFICATIONS
- max_variable_modifications: int = 3
- min_precursor_mz: Optional[float] = None
- max_precursor_mz: Optional[float] = None
- ms2pip_model: str = "HCD"
- add_decoys: float = False
- add_retention_time: float = True
- deeplc: dict = dict()
- batch_size: int = 10000
- num_cpu: Optional[int] = None
-
- @validator("output_filetype")
- def _validate_output_filetypes(cls, v):
- allowed_types = ["msp", "mgf", "bibliospec", "spectronaut", "dlib", "hdf"]
- v = [filetype.lower() for filetype in v]
- for filetype in v:
- if filetype not in allowed_types:
- raise ValueError(
- f"File type `{filetype}` not recognized. Should be one of "
- f"`{allowed_types}`."
- )
- return v
-
- @validator("modifications")
- def _validate_modifications(cls, v):
- if all(isinstance(m, ModificationConfig) for m in v):
- return v
- elif all(isinstance(m, dict) for m in v):
- return [ModificationConfig(**modification) for modification in v]
- else:
- raise ValueError(
- "Modifications should be a list of dicts or ModificationConfig objects."
- )
-
- @validator("ms2pip_model")
- def _validate_ms2pip_model(cls, v):
- if v not in MODELS.keys():
- raise ValueError(
- f"MS²PIP model `{v}` not recognized. Should be one of " f"`{MODELS.keys()}`."
- )
- return v
-
- @validator("num_cpu")
- def _validate_num_cpu(cls, v):
- available_cpus = multiprocessing.cpu_count()
- if not v or not 0 < v < available_cpus:
- return available_cpus
- else:
- return v
-
- def get_output_filename(self):
- if self.output_filename:
- return self.output_filename
- else:
- return str(Path(self.fasta_filename).with_suffix(""))
-
-
-class Fasta2SpecLib:
- """Generate an MS²PIP- and DeepLC-predicted spectral library from a FASTA file."""
-
- def __init__(
- self,
- fasta_filename: Union[str, Path],
- output_filename: Optional[Union[str, Path]] = None,
- config: Optional[Union[Configuration, dict]] = None,
- ):
- """
- Generate an MS²PIP- and DeepLC-predicted spectral library.
-
- fasta_filename: str, Path
- Path to input FASTA file.
- output_filename: str, Path
- Stem for output filenames. For instance, ``./output`` would result in
- ``./output.msp``. If ``None``, the output filename will be based on the
- input FASTA filename.
- config: Configuration, dict, optional
- Configuration of fasta2speclib. See documentation for more info
-
- """
- # Parse configuration
- if config:
- if isinstance(config, dict):
- config["fasta_filename"] = fasta_filename
- config["output_filename"] = output_filename
- config = Configuration.parse_obj(config)
- elif isinstance(config, Configuration):
- config.fasta_filename = fasta_filename
- config.output_filename = output_filename
- else:
- raise TypeError(f"Invalid type for configuration: `{type(config)}`.")
- else:
- config = Configuration(fasta_filename=fasta_filename, output_filename=output_filename)
-
- # `unspecific` is not an option in pyteomics.parser.icleave, so we configure
- # the settings for unspecific cleavage here.
- if config.cleavage_rule == "unspecific":
- config.missed_cleavages = config.max_length
- config.cleavage_rule = r"(?<=[A-Z])"
-
- # Setup multiprocessing, using a dummy pool if num_cpu is 1
- if config.num_cpu != 1:
- self.Pool = multiprocessing.Pool
- else:
- self.Pool = multiprocessing.dummy.Pool
-
- self.config = config
- self.rt_predictor = self._get_rt_predictor(config)
- self.ms2pip_params = self._prepare_ms2pip_params(config)
-
- def run(self):
- """Run the library generation pipeline."""
- peptides = self.prepare_search_space()
- batches = self.peptides_to_batches(peptides, self.config.batch_size)
-
- # Run in batches to avoid memory issues
- for batch_id, batch_peptides in enumerate(batches):
- logger.info(f"Processing batch {batch_id + 1}/{len(batches)}...")
- self.process_batch(batch_id, batch_peptides)
-
- def prepare_search_space(self) -> List[Peptide]:
- """Prepare peptide search space from FASTA file."""
- logger.info("Preparing search space...")
-
- # Setup database, with decoy configuration if required
- n_proteins = count_fasta_entries(self.config.fasta_filename)
- if self.config.add_decoys:
- fasta_db = decoy_db(
- self.config.fasta_filename,
- mode="reverse",
- decoy_only=False,
- keep_nterm=True,
- )
- else:
- fasta_db = FASTA(self.config.fasta_filename)
- n_proteins *= 2
-
- # Read proteins and digest to peptides
- with self.Pool(self.config.num_cpu) as pool:
- partial_digest_protein = partial(
- self._digest_protein,
- min_length=self.config.min_length,
- max_length=self.config.max_length,
- cleavage_rule=self.config.cleavage_rule,
- missed_cleavages=self.config.missed_cleavages,
- semi_specific=self.config.semi_specific,
- )
- results = track(
- pool.imap(partial_digest_protein, fasta_db),
- total=n_proteins,
- description="Digesting proteins...",
- transient=True,
- )
- peptides = list(chain.from_iterable(results))
-
- # Remove redundancy in peptides and combine protein lists
- peptide_dict = dict()
- for peptide in track(
- peptides,
- description="Removing peptide redundancy...",
- transient=True,
- ):
- if peptide.sequence in peptide_dict:
- peptide_dict[peptide.sequence].proteins.extend(peptide.proteins)
- else:
- peptide_dict[peptide.sequence] = peptide
- peptides = list(peptide_dict.values())
-
- # Add modification and charge permutations
- modifications_by_target = self._get_modifications_by_target(self.config.modifications)
- modification_options = []
- with self.Pool(self.config.num_cpu) as pool:
- partial_get_modification_versions = partial(
- self._get_modification_versions,
- modifications=self.config.modifications,
- modifications_by_target=modifications_by_target,
- max_variable_modifications=self.config.max_variable_modifications,
- )
- modification_options = pool.imap(partial_get_modification_versions, peptides)
- for pep, mod_opt in track(
- zip(peptides, modification_options),
- description="Adding modifications...",
- total=len(peptides),
- transient=True,
- ):
- pep.modification_options = mod_opt
- pep.charge_options = self.config.charges
-
- logger.info(f"Search space contains {len(peptides)} peptides.")
- return peptides
-
- @staticmethod
- def peptides_to_batches(peptides: List[Peptide], batch_size: int) -> List[List[Peptide]]:
- """Divide peptides into batches for batch-based processing."""
- return [peptides[i : i + batch_size] for i in range(0, len(peptides), batch_size)]
-
- def process_batch(self, batch_id, batch_peptides):
- """Predict and write library for a batch of peptides."""
- # Generate MS²PIP input
- logger.info("Generating MS²PIP input...")
- peprec = self._peptides_to_peprec(batch_peptides)
- logger.info(f"Chunk contains {len(peprec)} peptidoforms.")
-
- # Filter on precursor m/z
- if self.config.min_precursor_mz and self.config.max_precursor_mz:
- mods = MS2PIPModifications()
- mods.add_from_ms2pip_modstrings(self.ms2pip_params["ms2pip"]["ptm"])
- precursor_mz = peprec.apply(
- lambda x: mods.calc_precursor_mz(x["peptide"], x["modifications"], x["charge"])[1],
- axis=1,
- )
- before = len(peprec)
- peprec = (
- peprec[
- (self.config.min_precursor_mz <= precursor_mz)
- & (precursor_mz <= self.config.max_precursor_mz)
- ]
- .reset_index(drop=True)
- .copy()
- )
- after = len(peprec)
- logger.info(f"Filtered batch on precursor m/z: {before} -> {after}")
-
- # Predict retention time
- if self.config.add_retention_time:
- logger.info("Predicting retention times with DeepLC...")
- self.rt_predictor.add_rt_predictions(peprec)
-
- # Predict spectra
- logger.info("Predicting spectra with MS²PIP...")
- ms2pip = MS2PIP(
- peprec,
- num_cpu=self.config.num_cpu,
- params=self.ms2pip_params,
- return_results=True,
- add_retention_time=False,
- )
- predictions = ms2pip.run()
-
- # Write output
- logger.info("Writing output...")
- self._write_predictions(
- predictions,
- peprec,
- self.config.output_filetype,
- self.config.get_output_filename(),
- self.ms2pip_params,
- append=batch_id != 0,
- )
-
- @staticmethod
- def _get_rt_predictor(config: Configuration) -> RetentionTime:
- """Initialize and return MS²PIP wrapper for DeepLC predictor."""
- if config.add_retention_time:
- logger.debug("Initializing DeepLC predictor")
- if not config.deeplc:
- config.deeplc = {"calibration_file": None}
- if not "n_jobs" in config.deeplc:
- config.deeplc["n_jobs"] = config.num_cpu
- rt_predictor = RetentionTime(config=config.dict())
- else:
- rt_predictor = None
- return rt_predictor
-
- @staticmethod
- def _prepare_ms2pip_params(config: Configuration) -> dict:
- """Prepare MS²PIP parameters from fasta2speclib configuration."""
- ms2pip_params = {
- "ms2pip": {
- "model": config.ms2pip_model,
- "frag_error": 0.02,
- "ptm": [
- "{},{},opt,N-term".format(mod.name, mod.mass_shift)
- if mod.peptide_n_term or mod.protein_n_term
- else "{},{},opt,C-term".format(mod.name, mod.mass_shift)
- if mod.peptide_c_term or mod.protein_c_term
- else "{},{},opt,{}".format(mod.name, mod.mass_shift, mod.amino_acid)
- for mod in config.modifications
- ],
- "sptm": [],
- "gptm": [],
- }
- }
- return ms2pip_params
-
- @staticmethod
- def _digest_protein(
- protein: Protein,
- min_length: int = 8,
- max_length: int = 30,
- cleavage_rule: str = "trypsin",
- missed_cleavages: int = 2,
- semi_specific: bool = False,
- ) -> List[Peptide]:
- """Digest protein sequence and return a list of validated peptides."""
-
- def valid_residues(sequence: str) -> bool:
- return not any(aa in sequence for aa in ["B", "J", "O", "U", "X", "Z"])
-
- def parse_peptide(
- start_position: int,
- sequence: str,
- protein: Protein,
- ) -> Peptide:
- """Parse result from parser.icleave into Peptide."""
- return Peptide(
- sequence=sequence,
- # Assumes protein ID is description until first space
- proteins=[protein.description.split(" ")[0]],
- is_n_term=start_position == 0,
- is_c_term=start_position + len(sequence) == len(protein.sequence),
- )
-
- peptides = [
- parse_peptide(start, seq, protein)
- for start, seq in icleave(
- protein.sequence,
- cleavage_rule,
- missed_cleavages=missed_cleavages,
- min_length=min_length,
- max_length=max_length,
- semi=semi_specific,
- )
- if valid_residues(seq)
- ]
-
- return peptides
-
- @staticmethod
- def _get_modifications_by_target(
- modifications,
- ) -> Dict[str, Dict[str, List[ModificationConfig]]]:
- """Restructure variable modifications to options per side chain or terminus."""
- modifications_by_target = {
- "sidechain": defaultdict(lambda: [None]),
- "peptide_n_term": defaultdict(lambda: [None]),
- "peptide_c_term": defaultdict(lambda: [None]),
- "protein_n_term": defaultdict(lambda: [None]),
- "protein_c_term": defaultdict(lambda: [None]),
- }
-
- def add_mod(mod, target, amino_acid):
- if amino_acid:
- modifications_by_target[target][amino_acid].append(mod)
- else:
- modifications_by_target[target]["any"].append(mod)
-
- for mod in modifications:
- if mod.fixed:
- continue
- if mod.peptide_n_term:
- add_mod(mod, "peptide_n_term", mod.amino_acid)
- elif mod.peptide_c_term:
- add_mod(mod, "peptide_c_term", mod.amino_acid)
- elif mod.protein_n_term:
- add_mod(mod, "protein_n_term", mod.amino_acid)
- elif mod.protein_c_term:
- add_mod(mod, "protein_c_term", mod.amino_acid)
- else:
- add_mod(mod, "sidechain", mod.amino_acid)
-
- return {k: dict(v) for k, v in modifications_by_target.items()}
-
- # TODO: Make adding modifications more efficient
- @staticmethod
- def _get_modification_versions(
- peptide: Peptide,
- modifications: List[ModificationConfig],
- modifications_by_target: Dict[str, Dict[str, List[ModificationConfig]]],
- max_variable_modifications: int = 3,
- ) -> List[str]:
- """Get MS²PIP modification strings for all potential versions."""
- possibilities_by_site = defaultdict(list)
-
- # Generate dictionary of positions per amino acid
- pos_dict = defaultdict(list)
- for pos, aa in enumerate(peptide.sequence):
- pos_dict[aa].append(pos + 1)
- # Map modifications to positions
- for aa in set(pos_dict).intersection(set(modifications_by_target["sidechain"])):
- possibilities_by_site.update(
- {pos: modifications_by_target["sidechain"][aa] for pos in pos_dict[aa]}
- )
-
- # Assign possible modifications per terminus
- for terminus, position, specificity in [
- ("peptide_n_term", 0, None),
- ("peptide_c_term", -1, None),
- ("protein_n_term", 0, "is_n_term"),
- ("protein_c_term", -1, "is_c_term"),
- ]:
- if specificity is None or getattr(peptide, specificity):
- for site, mods in modifications_by_target[terminus].items():
- if site == "any" or peptide.sequence[position] == site:
- possibilities_by_site[position].extend(mods)
-
- # Override with fixed modifications
- for mod in modifications:
- aa = mod.amino_acid
- # Skip variable modifications
- if not mod.fixed:
- continue
- # Assign if specific aa matches or if no aa is specified for each terminus
- for terminus, position, specificity in [
- ("peptide_n_term", 0, None),
- ("peptide_c_term", -1, None),
- ("protein_n_term", 0, "is_n_term"),
- ("protein_c_term", -1, "is_c_term"),
- ]:
- if getattr(mod, terminus): # Mod has this terminus
- if specificity is None or getattr(peptide, specificity): # Specificity matches
- if not aa or (aa and peptide.sequence[position] == aa): # Aa matches
- possibilities_by_site[position] = [mod] # Override with fixed mod
- break # Allow `else: if amino_acid` if no terminus matches
- # Assign if fixed modification is not terminal and specific aa matches
- else:
- if aa:
- for pos in pos_dict[aa]:
- possibilities_by_site[pos] = [mod]
-
- # Get all possible combinations of modifications for all sites
- mod_permutations = product(*possibilities_by_site.values())
- mod_positions = possibilities_by_site.keys()
-
- # Filter by max modified sites (avoiding combinatorial explosion)
- mod_permutations = filter(
- lambda mods: sum([1 for m in mods if m is not None and not m.fixed])
- <= max_variable_modifications,
- mod_permutations,
- )
-
- def _compare_minus_one_larger(a, b):
- """Custom comparison function where `-1` is always larger."""
- if a[0] == -1:
- return 1
- elif b[0] == -1:
- return -1
- else:
- return a[0] - b[0]
-
- # Get MS²PIP modifications strings for each combination
- mod_strings = []
- for p in mod_permutations:
- if p == [""]:
- mod_strings.append("-")
- else:
- mods = sorted(zip(mod_positions, p), key=cmp_to_key(_compare_minus_one_larger))
- mod_strings.append("|".join(f"{p}|{m.name}" for p, m in mods if m))
-
- return mod_strings
-
- @staticmethod
- def _peptides_to_peprec(peptides: List[Peptide]) -> pd.DataFrame:
- """Convert a list of peptides to a PeptideRecord DataFrame."""
- peprec = pd.DataFrame(
- [
- {
- "peptide": peptide.sequence,
- "modifications": modifications,
- "charge": charge,
- "protein_list": peptide.proteins
- }
- for peptide in peptides
- for charge in peptide.charge_options
- for modifications in peptide.modification_options
- ],
- columns=["spec_id", "peptide", "modifications", "charge", "protein_list"],
- )
- peprec["spec_id"] = peprec.index
- return peprec
-
- @staticmethod
- def _write_predictions(
- predictions: pd.DataFrame,
- peprec: pd.DataFrame,
- filetypes: List[str],
- filename: str,
- ms2pip_params: Dict,
- append: bool = False,
- ):
- """Write predictions (for batch) to requested output file formats."""
- write_mode = "a" if append else "w"
- if "hdf" in filetypes:
- logger.info(f"Writing results to {filename}_predictions.hdf")
- predictions.astype(str).to_hdf(
- f"{filename}_predictions.hdf",
- key="table",
- format="table",
- complevel=3,
- complib="zlib",
- mode=write_mode,
- append=append,
- min_itemsize=50,
- )
- spec_out = spectrum_output.SpectrumOutput(
- predictions,
- peprec,
- ms2pip_params["ms2pip"],
- output_filename=filename,
- write_mode=write_mode,
- )
- if "msp" in filetypes:
- spec_out.write_msp()
- if "mgf" in filetypes:
- spec_out.write_mgf()
- if "bibliospec" in filetypes:
- spec_out.write_bibliospec()
- if "spectronaut" in filetypes:
- spec_out.write_spectronaut()
- if "dlib" in filetypes:
- spec_out.write_dlib()
-
-
-def count_fasta_entries(filename: Union[str, Path]) -> int:
- """Count the number of entries in a FASTA file."""
- with open(filename, "rt") as f:
- count = 0
- for line in f:
- if line[0] == ">":
- count += 1
- return count
-
-
-def _argument_parser():
- parser = argparse.ArgumentParser(
- description=(
- "Create an MS2PIP- and DeepLC-predicted spectral library, starting from a "
- "FASTA file."
- )
- )
- parser.add_argument(
- "fasta_filename",
- action="store",
- help="Path to the FASTA file containing protein sequences",
- )
- parser.add_argument(
- "-o",
- dest="output_filename",
- action="store",
- help="Name for output file(s) (if not given, derived from FASTA file)",
- )
- parser.add_argument(
- "-c",
- dest="config_filename",
- action="store",
- help="Name of configuration json file (default: fasta2speclib_config.json)",
- )
-
- args = parser.parse_args()
- return args
-
-
-def main():
- """Command line entrypoint for fasta2speclib."""
- # Configure logging
- logging.basicConfig(
- format="%(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- level=logging.INFO,
- handlers=[RichHandler(rich_tracebacks=True, show_level=True, show_path=False)],
- )
- logging.getLogger("ms2pip").setLevel(logging.WARNING)
- logging.getLogger("deeplc").setLevel(logging.WARNING)
-
- # Get configuration from CLI and config file
- args = _argument_parser()
- with open(args.config_filename, "rt") as config_file:
- config_dict = json.load(config_file)
-
- # Run fasta2speclib
- logger.info("Starting library generation pipeline...")
- f2sl = Fasta2SpecLib(args.fasta_filename, args.output_filename, config_dict)
- f2sl.run()
- logger.info("Done!")
-
-
-if __name__ == "__main__":
- main()
diff --git a/fasta2speclib/fasta2speclib_config.json b/fasta2speclib/fasta2speclib_config.json
deleted file mode 100644
index 2accdaab..00000000
--- a/fasta2speclib/fasta2speclib_config.json
+++ /dev/null
@@ -1,20 +0,0 @@
-{
- "output_filetype":["msp", "mgf", "bibliospec", "dlib"],
- "charges":[2, 3],
- "min_peplen":8,
- "max_peplen":30,
- "cleavage_rule":"trypsin",
- "missed_cleavages":2,
- "semi_specific":false,
- "modifications":[
- {"name":"Acetyl", "unimod_accession":1, "mass_shift":42.010565, "amino_acid":null, "protein_n_term":true},
- {"name":"Oxidation", "unimod_accession":35, "mass_shift":15.994915, "amino_acid":"M"},
- {"name":"Carbamidomethyl", "unimod_accession":4, "mass_shift":57.021464, "amino_acid":"C", "fixed":true}
- ],
- "ms2pip_model":"HCD",
- "decoy":false,
- "add_retention_time":true,
- "deeplc": {},
- "batch_size":10000,
- "num_cpu":null
-}
diff --git a/fasta2speclib/fasta2speclib_config.md b/fasta2speclib/fasta2speclib_config.md
deleted file mode 100644
index fba38a11..00000000
--- a/fasta2speclib/fasta2speclib_config.md
+++ /dev/null
@@ -1,2 +0,0 @@
-# fasta2speclib configuration info
-Moved to [compomics.github.io](http://compomics.github.io/projects/ms2pip_c/wiki/fasta2speclib.html)
diff --git a/img/mirror-DVAQIFNNILR-2.png b/img/mirror-DVAQIFNNILR-2.png
new file mode 100644
index 00000000..3cf7bc67
Binary files /dev/null and b/img/mirror-DVAQIFNNILR-2.png differ
diff --git a/img/mirror-DVAQIFNNILR-2.svg b/img/mirror-DVAQIFNNILR-2.svg
new file mode 100644
index 00000000..ff4e5b3f
--- /dev/null
+++ b/img/mirror-DVAQIFNNILR-2.svg
@@ -0,0 +1,1928 @@
+
+
+
diff --git a/ms2pip/__init__.py b/ms2pip/__init__.py
index 7cf45cf5..849851cd 100644
--- a/ms2pip/__init__.py
+++ b/ms2pip/__init__.py
@@ -1 +1,21 @@
-from ms2pip.ms2pipC import MS2PIP
+# isort: skip_file
+"""MS2PIP: Accurate and versatile peptide fragmentation spectrum prediction."""
+
+__version__ = "4.0.0"
+
+from warnings import filterwarnings
+
+filterwarnings(
+ "ignore", message="hdf5plugin is missing", category=UserWarning, module="psims.mzmlb"
+)
+
+
+from ms2pip.core import ( # noqa: F401 E402
+ predict_single,
+ predict_batch,
+ predict_library,
+ correlate,
+ get_training_data,
+ annotate_spectra,
+ download_models,
+)
diff --git a/ms2pip/__main__.py b/ms2pip/__main__.py
index 40899508..9500509a 100644
--- a/ms2pip/__main__.py
+++ b/ms2pip/__main__.py
@@ -1,188 +1,236 @@
-import argparse
import logging
-import multiprocessing
import sys
+from pathlib import Path
+from typing import Optional
+import click
+from psm_utils.io import READERS
from rich.console import Console
from rich.logging import RichHandler
-
-from ms2pip.config_parser import ConfigParser
-from ms2pip.exceptions import (EmptySpectrumError,
- FragmentationModelRequiredError,
- InvalidModificationFormattingError,
- InvalidPEPRECError, InvalidXGBoostModelError,
- NoValidPeptideSequencesError,
- UnknownFragmentationMethodError,
- UnknownModificationError,
- UnknownOutputFormatError)
-from ms2pip.ms2pipC import MODELS, MS2PIP, SUPPORTED_OUT_FORMATS
-
-
-def print_logo():
- logo = r"""
- __ __ ___ __ ___ ___ ___
-| \/ / __||_ ) _ \_ _| _ \
-| |\/| \__ \/__| _/| || _/
-|_| |_|___/ |_| |___|_|
-
-by CompOmics
-sven.degroeve@ugent.be
-ralf.gabriels@ugent.be
-
-http://compomics.github.io/projects/ms2pip_c.html
- """
- print(logo)
-
-
-def argument_parser():
- parser = argparse.ArgumentParser()
- parser.add_argument("pep_file", metavar="", help="list of peptides")
- parser.add_argument(
- "-c",
- "--config-file",
- metavar="CONFIG_FILE",
- action="store",
- required=True,
- dest="config_file",
- help="Configuration file: text-based (extensions `.txt`, `.config`, or `.ms2pip`) or TOML (extension `.toml`).",
- )
- parser.add_argument(
- "-s",
- "--spectrum-file",
- metavar="SPECTRUM_FILE",
- action="store",
- dest="spec_file",
- help="MGF or mzML spectrum file (optional)",
- )
- parser.add_argument(
- "-w",
- "--vector-file",
- metavar="FEATURE_VECTOR_OUTPUT",
- action="store",
- dest="vector_file",
- help="write feature vectors to FILE.{pkl,h5} (optional)",
- )
- parser.add_argument(
- "-r",
- "--retention-time",
- action="store_true",
- default=False,
- dest="add_retention_time",
- help="add retention time predictions (requires DeepLC python package)",
- )
- parser.add_argument(
- "-x",
- "--correlations",
- action="store_true",
- default=False,
- dest="correlations",
- help="calculate correlations (if spectrum file is given)",
- )
- parser.add_argument(
- "-m",
- "--match-spectra",
- action="store_true",
- default=False,
- dest="match_spectra",
- help="match peptides to spectra based on predicted spectra (if spectrum file is given)",
- )
- parser.add_argument(
- "-n",
- "--num-cpu",
- metavar="NUM_CPU",
- action="store",
- dest="num_cpu",
- type=int,
- help="number of CPUs to use (default: all available)",
- )
- parser.add_argument(
- "--sqldb-uri",
- action="store",
- dest="sqldb_uri",
- help="use sql database of observed spectra instead of spectrum files",
- )
- parser.add_argument(
- "--model-dir",
- action="store",
- dest="model_dir",
- help="Custom directory for downloaded XGBoost model files, default: `~/.ms2pip`",
- )
- args = parser.parse_args()
-
- if not args.num_cpu:
- args.num_cpu = multiprocessing.cpu_count()
-
- return args
-
-
-def main():
+from werkzeug.utils import secure_filename
+
+import ms2pip.core
+from ms2pip import __version__, exceptions
+from ms2pip._utils.cli import build_credits, build_prediction_table
+from ms2pip.constants import MODELS
+from ms2pip.plot import spectrum_to_png
+from ms2pip.result import write_correlations
+from ms2pip.spectrum_output import SUPPORTED_FORMATS, write_spectra
+
+console = Console()
+logger = logging.getLogger(__name__)
+
+LOGGING_LEVELS = {
+ "DEBUG": logging.DEBUG,
+ "INFO": logging.INFO,
+ "WARNING": logging.WARNING,
+ "ERROR": logging.ERROR,
+ "CRITICAL": logging.CRITICAL,
+}
+
+PSM_FILETYPES = list(READERS.keys())
+
+
+def _infer_output_name(
+ input_filename: str,
+ output_name: Optional[str] = None,
+) -> Path:
+ """Infer output filename from input filename if output_filename was not defined."""
+ if output_name:
+ return Path(output_name)
+ else:
+ input__filename = Path(input_filename)
+ return input__filename.with_name(input__filename.stem + "_predictions").with_suffix("")
+
+
+@click.group()
+@click.option("--logging-level", "-l", type=click.Choice(LOGGING_LEVELS.keys()), default="INFO")
+@click.version_option(version=__version__)
+def cli(*args, **kwargs):
logging.basicConfig(
format="%(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
- level=logging.DEBUG,
- handlers=[RichHandler(
- rich_tracebacks=True, console=Console(), show_level=True, show_path=False
- )],
+ level=LOGGING_LEVELS[kwargs["logging_level"]],
+ handlers=[
+ RichHandler(rich_tracebacks=True, console=console, show_level=True, show_path=False)
+ ],
+ )
+ console.print(build_credits())
+
+
+@cli.command(help=ms2pip.core.predict_single.__doc__)
+@click.argument("peptidoform", required=True)
+@click.option("--output-name", "-o", type=str)
+@click.option("--output-format", "-f", type=click.Choice(SUPPORTED_FORMATS), default="tsv")
+@click.option("--model", type=click.Choice(MODELS), default="HCD")
+@click.option("--model-dir")
+@click.option("--plot", "-p", is_flag=True)
+def predict_single(*args, **kwargs):
+ # Parse arguments
+ output_name = kwargs.pop("output_name")
+ output_format = kwargs.pop("output_format")
+ plot = kwargs.pop("plot")
+ if not output_name:
+ output_name = "ms2pip_prediction_" + secure_filename(kwargs["peptidoform"])
+
+ # Predict spectrum
+ result = ms2pip.core.predict_single(*args, **kwargs)
+ predicted_spectrum, _ = result.as_spectra()
+
+ # Write output
+ console.print(build_prediction_table(predicted_spectrum))
+ write_spectra(output_name, [result], output_format)
+ if plot:
+ spectrum_to_png(predicted_spectrum, output_name)
+
+
+@cli.command(help=ms2pip.core.predict_batch.__doc__)
+@click.argument("psms", required=True)
+@click.option("--psm-filetype", "-t", type=click.Choice(PSM_FILETYPES), default=None)
+@click.option("--output-name", "-o", type=str)
+@click.option("--output-format", "-f", type=click.Choice(SUPPORTED_FORMATS), default="tsv")
+@click.option("--add-retention-time", "-r", is_flag=True)
+@click.option("--add-ion-mobility", "-i", is_flag=True)
+@click.option("--model", type=click.Choice(MODELS), default="HCD")
+@click.option("--model-dir")
+@click.option("--processes", "-n", type=int)
+def predict_batch(*args, **kwargs):
+ # Parse arguments
+ output_format = kwargs.pop("output_format")
+ output_name = _infer_output_name(kwargs["psms"], kwargs.pop("output_name"))
+
+ # Run
+ predictions = ms2pip.core.predict_batch(*args, **kwargs)
+
+ # Write output
+ write_spectra(output_name, predictions, output_format)
+
+
+@cli.command(help=ms2pip.core.predict_library.__doc__)
+@click.argument("fasta-file", required=False, type=click.Path(exists=True, dir_okay=False))
+@click.option("--config", "-c", type=click.Path(exists=True, dir_okay=False))
+@click.option("--output-name", "-o", type=str)
+@click.option("--output-format", "-f", type=click.Choice(SUPPORTED_FORMATS), default="msp")
+@click.option("--add-retention-time", "-r", is_flag=True)
+@click.option("--add-ion-mobility", "-i", is_flag=True)
+@click.option("--model", type=click.Choice(MODELS), default="HCD")
+@click.option("--model-dir")
+@click.option("--batch-size", type=int, default=100000)
+@click.option("--processes", "-n", type=int)
+def predict_library(*args, **kwargs):
+ # Parse arguments
+ if not kwargs["fasta_file"] and not kwargs["config"]:
+ raise click.UsageError("Either `fasta_file` or `config` must be provided.")
+ output_format = kwargs.pop("output_format")
+ output_name = _infer_output_name(
+ kwargs["fasta_file"] or kwargs["config"], kwargs.pop("output_name")
)
- logger = logging.getLogger(__name__)
- print_logo()
+ # Run and write output for each batch
+ for i, result_batch in enumerate(ms2pip.core.predict_library(*args, **kwargs)):
+ write_spectra(output_name, result_batch, output_format, write_mode="w" if i == 0 else "a")
+
+
+@cli.command(help=ms2pip.core.correlate.__doc__)
+@click.argument("psms", required=True)
+@click.argument("spectrum_file", required=True)
+@click.option("--psm-filetype", "-t", type=click.Choice(PSM_FILETYPES), default=None)
+@click.option("--output-name", "-o", type=str)
+@click.option("--spectrum-id-pattern", "-p")
+@click.option("--compute-correlations", "-x", is_flag=True)
+@click.option("--add-retention-time", "-r", is_flag=True)
+@click.option("--add-ion-mobility", "-i", is_flag=True)
+@click.option("--model", type=click.Choice(MODELS), default="HCD")
+@click.option("--model-dir")
+@click.option("--ms2-tolerance", type=float, default=0.02)
+@click.option("--processes", "-n", type=int)
+def correlate(*args, **kwargs):
+ # Parse arguments
+ output_name = _infer_output_name(kwargs["psms"], kwargs.pop("output_name"))
+
+ # Run
+ results = ms2pip.core.correlate(*args, **kwargs)
+
+ # Write intensities
+ logger.info(f"Writing intensities to {output_name.with_suffix('.tsv')}")
+ write_spectra(output_name, results, "tsv")
+
+ # Write correlations
+ if kwargs["compute_correlations"]:
+ output_name_corr = output_name.with_name(output_name.stem + "_correlations").with_suffix(
+ ".tsv"
+ )
+ logger.info(f"Writing correlations to {output_name_corr}")
+ write_correlations(results, output_name_corr)
+
+
+@cli.command(help=ms2pip.core.get_training_data.__doc__)
+@click.argument("psms", required=True)
+@click.argument("spectrum_file", required=True)
+@click.option("--psm-filetype", "-t", type=click.Choice(PSM_FILETYPES), default=None)
+@click.option("--output-name", "-o", type=str)
+@click.option("--spectrum-id-pattern", "-p")
+@click.option("--model", type=click.Choice(MODELS), default="HCD")
+@click.option("--ms2-tolerance", type=float, default=0.02)
+@click.option("--processes", "-n", type=int)
+def get_training_data(*args, **kwargs):
+ # Parse arguments
+ output_name = kwargs.pop("output_name")
+ output_name = _infer_output_name(kwargs["psms"], output_name).with_suffix(".feather")
+
+ # Run
+ training_data = ms2pip.core.get_training_data(*args, **kwargs)
+
+ # Write output
+ logger.info(f"Writing training data to {output_name}")
+ training_data.to_feather(output_name)
+
+
+@cli.command(help=ms2pip.core.annotate_spectra.__doc__)
+@click.argument("psms", required=True)
+@click.argument("spectrum_file", required=True)
+@click.option("--psm-filetype", "-t", type=click.Choice(PSM_FILETYPES), default=None)
+@click.option("--output-name", "-o", type=str)
+@click.option("--spectrum-id-pattern", "-p")
+@click.option("--model", type=click.Choice(MODELS), default="HCD")
+@click.option("--ms2-tolerance", type=float, default=0.02)
+@click.option("--processes", "-n", type=int)
+def annotate_spectra(*args, **kwargs):
+ # Parse arguments
+ output_name = kwargs.pop("output_name")
+ output_name = _infer_output_name(kwargs["psms"], output_name)
+
+ # Run
+ results = ms2pip.core.annotate_spectra(*args, **kwargs)
+
+ # Write intensities
+ output_name_int = output_name.with_name(output_name.stem + "_observations").with_suffix()
+ logger.info(f"Writing intensities to {output_name_int.with_suffix('.tsv')}")
+ write_spectra(output_name, results, "tsv")
- args = argument_parser()
- config_parser = ConfigParser(filepath=args.config_file)
+def main():
try:
- ms2pip = MS2PIP(
- args.pep_file,
- spec_file=args.spec_file,
- vector_file=args.vector_file,
- params=config_parser.config,
- num_cpu=args.num_cpu,
- add_retention_time=args.add_retention_time,
- compute_correlations=args.correlations,
- match_spectra=args.match_spectra,
- sqldb_uri=args.sqldb_uri,
- model_dir=args.model_dir,
- )
- try:
- ms2pip.run()
- finally:
- ms2pip.cleanup()
- except InvalidPEPRECError:
- logger.critical("PEPREC file should start with header column")
- sys.exit(1)
- except NoValidPeptideSequencesError:
- logger.critical(
- "No peptides for which to predict intensities. \
- please provide at least one valid peptide sequence."
- )
- sys.exit(1)
- except UnknownModificationError as e:
- logger.critical("Unknown modification: %s", e)
- sys.exit(1)
- except InvalidModificationFormattingError as e:
- logger.critical("Invalid formatting of modifications: %s", e)
- sys.exit(1)
- except UnknownOutputFormatError as o:
+ cli()
+ except exceptions.UnresolvableModificationError as e:
logger.critical(
- f"Unknown output format: `{o}` (supported formats: `{SUPPORTED_OUT_FORMATS}`)"
+ "Unresolvable modification: `%s`. See "
+ "https://ms2pip.readthedocs.io/en/stable/usage/#amino-acid-modifications "
+ "for more info.",
+ e,
)
sys.exit(1)
- except UnknownFragmentationMethodError as f:
- logger.critical(
- f"Unknown model: `{f}` (supported models: {MODELS.keys()})"
- )
+ except exceptions.UnknownOutputFormatError as o:
+ logger.critical(f"Unknown output format: `{o}` (supported formats: `{SUPPORTED_FORMATS}`)")
sys.exit(1)
- except FragmentationModelRequiredError:
- logger.critical("Please specify model in config file.")
+ except exceptions.UnknownModelError as f:
+ logger.critical(f"Unknown model: `{f}` (supported models: {set(MODELS.keys())})")
sys.exit(1)
- except InvalidXGBoostModelError:
- logger.critical(
- f"Could not download XGBoost model properly\nTry manual download"
- )
+ except exceptions.InvalidXGBoostModelError:
+ logger.critical("Could not correctly download XGBoost model\nTry a manual download.")
sys.exit(1)
- except EmptySpectrumError:
- logger.critical("Provided MGF file cannot contain empty spectra")
+ except Exception:
+ logger.exception("An unexpected error occurred in MS²PIP.")
sys.exit(1)
diff --git a/fasta2speclib/__init__.py b/ms2pip/_cython_modules/__init__.py
similarity index 100%
rename from fasta2speclib/__init__.py
rename to ms2pip/_cython_modules/__init__.py
diff --git a/ms2pip/cython_modules/ms2pip_features_c_general.c b/ms2pip/_cython_modules/ms2pip_features_c.c
similarity index 93%
rename from ms2pip/cython_modules/ms2pip_features_c_general.c
rename to ms2pip/_cython_modules/ms2pip_features_c.c
index 6203215f..19749192 100644
--- a/ms2pip/cython_modules/ms2pip_features_c_general.c
+++ b/ms2pip/_cython_modules/ms2pip_features_c.c
@@ -70,14 +70,14 @@ unsigned int* get_v_ms2pip(int peplen, unsigned short* peptide, unsigned short*
for (j=0; j
#include "ms2pip_init_c.c"
-#include "ms2pip_features_c_general.c"
-#include "ms2pip_features_c_ce.c"
-#include "ms2pip_features_c_old.c"
-#include "ms2pip_features_c_catboost.c"
+#include "ms2pip_features_c.c"
-#include "../models/HCD-2019.h"
-#include "../models/TMT.h"
+#include "../_models_c/HCD-2019.h"
+#include "../_models_c/TMT.h"
float membuffer[10000];
float ions[2000];
diff --git a/ms2pip/cython_modules/ms2pip_pyx.pyx b/ms2pip/_cython_modules/ms2pip_pyx.pyx
similarity index 83%
rename from ms2pip/cython_modules/ms2pip_pyx.pyx
rename to ms2pip/_cython_modules/ms2pip_pyx.pyx
index bb219a6a..0e980ba6 100644
--- a/ms2pip/cython_modules/ms2pip_pyx.pyx
+++ b/ms2pip/_cython_modules/ms2pip_pyx.pyx
@@ -16,9 +16,6 @@ cdef extern from "ms2pip_peaks_c.c":
void init_ms2pip(char* amino_masses_fname, char* modifications_fname, char* modifications_fname_sptm)
unsigned int* get_v_ms2pip(int peplen, unsigned short* peptide, unsigned short* modpeptide, int charge)
- unsigned int* get_v_ms2pip_ce(int peplen, unsigned short* peptide, unsigned short* modpeptide, int charge, int ce)
- unsigned int* get_v_ms2pip_old(int peplen, unsigned short* peptide, unsigned short* modpeptide, int charge)
- unsigned int* get_v_ms2pip_catboost(int peplen, unsigned short* peptide, unsigned short* modpeptide, int charge)
float* get_p_ms2pip(int peplen, unsigned short* peptide, unsigned short* modpeptide, int charge, int model_id, int ce)
@@ -61,44 +58,6 @@ def get_vector(np.ndarray[unsigned short, ndim=1, mode="c"] peptide,
return r
-def get_vector_ce(np.ndarray[unsigned short, ndim=1, mode="c"] peptide,
- np.ndarray[unsigned short, ndim=1, mode="c"] modpeptide,
- charge, ce):
-
- cdef unsigned int* results = get_v_ms2pip_ce(len(peptide)-2, &peptide[0], &modpeptide[0], charge, ce)
-
- r = []
- offset = 0
- fnum = int(results[0] / (len(peptide) - 3))
- for i in range(len(peptide) - 3):
- v = []
- for j in range(fnum):
- v.append(results[j + 1 + offset])
- offset += fnum
- r.append(np.array(v, dtype=np.uint16))
-
- return r
-
-
-def get_vector_catboost(np.ndarray[unsigned short, ndim=1, mode="c"] peptide,
- np.ndarray[unsigned short, ndim=1, mode="c"] modpeptide,
- charge):
-
- cdef unsigned int* results = get_v_ms2pip_catboost(len(peptide)-2, &peptide[0], &modpeptide[0], charge)
-
- r = []
- offset = 0
- fnum = int(results[0] / (len(peptide) - 3))
- for i in range(len(peptide) - 3):
- v = []
- for j in range(fnum):
- v.append(results[j + 1 + offset])
- offset += fnum
- r.append(np.array(v, dtype=np.uint16))
-
- return r
-
-
def get_predictions(np.ndarray[unsigned short, ndim=1, mode="c"] peptide,
np.ndarray[unsigned short, ndim=1, mode="c"] modpeptide,
charge, model_id, peaks_version, ce):
@@ -138,10 +97,9 @@ def get_targets_all(np.ndarray[unsigned short, ndim=1, mode="c"] modpeptide,
result_mzs = []
for i in range(NUM_ION_TYPES_MAPPING[peaks_version]*(len(modpeptide)-3)):
result_mzs.append(results.msms[i])
- #print(result_parsed)
- #print()
return (result_mzs,result_peaks)
+
def get_targets_general(np.ndarray[unsigned short, ndim=1, mode="c"] modpeptide,
np.ndarray[float, ndim=1, mode="c"] msms,
np.ndarray[float, ndim=1, mode="c"] peaks,
diff --git a/ms2pip/models/HCD-2019.h b/ms2pip/_models_c/HCD-2019.h
similarity index 100%
rename from ms2pip/models/HCD-2019.h
rename to ms2pip/_models_c/HCD-2019.h
diff --git a/ms2pip/models/HCD-2019/model_20190107_HCD_train_B.c b/ms2pip/_models_c/HCD-2019/model_20190107_HCD_train_B.c
similarity index 100%
rename from ms2pip/models/HCD-2019/model_20190107_HCD_train_B.c
rename to ms2pip/_models_c/HCD-2019/model_20190107_HCD_train_B.c
diff --git a/ms2pip/models/HCD-2019/model_20190107_HCD_train_B2.c b/ms2pip/_models_c/HCD-2019/model_20190107_HCD_train_B2.c
similarity index 100%
rename from ms2pip/models/HCD-2019/model_20190107_HCD_train_B2.c
rename to ms2pip/_models_c/HCD-2019/model_20190107_HCD_train_B2.c
diff --git a/ms2pip/models/HCD-2019/model_20190107_HCD_train_Y.c b/ms2pip/_models_c/HCD-2019/model_20190107_HCD_train_Y.c
similarity index 100%
rename from ms2pip/models/HCD-2019/model_20190107_HCD_train_Y.c
rename to ms2pip/_models_c/HCD-2019/model_20190107_HCD_train_Y.c
diff --git a/ms2pip/models/HCD-2019/model_20190107_HCD_train_Y2.c b/ms2pip/_models_c/HCD-2019/model_20190107_HCD_train_Y2.c
similarity index 100%
rename from ms2pip/models/HCD-2019/model_20190107_HCD_train_Y2.c
rename to ms2pip/_models_c/HCD-2019/model_20190107_HCD_train_Y2.c
diff --git a/ms2pip/models/TMT.h b/ms2pip/_models_c/TMT.h
similarity index 100%
rename from ms2pip/models/TMT.h
rename to ms2pip/_models_c/TMT.h
diff --git a/ms2pip/_models_c/TMT/model_20190107_TMT_train_B.c b/ms2pip/_models_c/TMT/model_20190107_TMT_train_B.c
new file mode 100644
index 00000000..c23a7f9a
--- /dev/null
+++ b/ms2pip/_models_c/TMT/model_20190107_TMT_train_B.c
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49252e3d1b42df924b422f118870f237ec541d58db0037b4800444999277d055
+size 2348087
diff --git a/ms2pip/models/TMT/model_20190107_TMT_train_Y.c b/ms2pip/_models_c/TMT/model_20190107_TMT_train_Y.c
similarity index 100%
rename from ms2pip/models/TMT/model_20190107_TMT_train_Y.c
rename to ms2pip/_models_c/TMT/model_20190107_TMT_train_Y.c
diff --git a/ms2pip/_utils/cli.py b/ms2pip/_utils/cli.py
new file mode 100644
index 00000000..e0ff2e95
--- /dev/null
+++ b/ms2pip/_utils/cli.py
@@ -0,0 +1,40 @@
+"""Command line utilities for MS²PIP."""
+
+from rich.table import Table
+from rich.text import Text
+
+from ms2pip import __version__
+
+
+def build_credits():
+ """Build credits."""
+ text = Text()
+ text.append("\n")
+ text.append("MS²PIP", style="bold link https://github.com/compomics/ms2pip")
+ text.append(f" (v{__version__})\n", style="bold")
+ text.append("Developed at CompOmics, VIB / Ghent University, Belgium.\n")
+ text.append("Please cite: ")
+ text.append("Declercq et al. NAR (2023)", style="link https://doi.org/10.1093/nar/gkad335")
+ text.append("\n")
+ text.stylize("cyan")
+ return text
+
+
+def build_prediction_table(predicted_spectrum):
+ """Print a table with the predicted spectrum values."""
+ peptidoform = str(predicted_spectrum.peptidoform)
+ table = Table(title=f"Predicted spectrum for [bold]{peptidoform}[/bold]")
+
+ table.add_column("m/z", justify="right")
+ table.add_column("intensity", justify="right")
+ table.add_column("annotation", justify="left")
+
+ for mz, intensity, annotation in zip(
+ predicted_spectrum.mz,
+ predicted_spectrum.intensity,
+ predicted_spectrum.annotations,
+ ):
+ style = "blue" if "b" in annotation else "red" if "y" in annotation else "black"
+ table.add_row(f"{mz:.4f}", f"{intensity:.4f}", annotation, style=style)
+
+ return table
diff --git a/ms2pip/ms2pip_tools/dlib.py b/ms2pip/_utils/dlib.py
similarity index 89%
rename from ms2pip/ms2pip_tools/dlib.py
rename to ms2pip/_utils/dlib.py
index 6ab475cf..f33ee35f 100644
--- a/ms2pip/ms2pip_tools/dlib.py
+++ b/ms2pip/_utils/dlib.py
@@ -1,12 +1,24 @@
+"""Database configuration for EncyclopeDIA DLIB SQLite format."""
+
import zlib
+from pathlib import Path
+from typing import Union
import numpy
import sqlalchemy
-from sqlalchemy import (Boolean, Column, Float, Index, Integer, MetaData,
- String, Table, TypeDecorator)
+from sqlalchemy import (
+ Boolean,
+ Column,
+ Float,
+ Index,
+ Integer,
+ MetaData,
+ String,
+ Table,
+ TypeDecorator,
+)
from sqlalchemy.dialects.sqlite import BLOB
-
DLIB_VERSION = "0.1.14"
@@ -81,7 +93,7 @@ def copy(self):
)
-def open_sqlite(filename):
+def open_sqlite(filename: Union[str, Path]) -> sqlalchemy.engine.Connection:
engine = sqlalchemy.create_engine(f"sqlite:///{filename}")
metadata.bind = engine
return engine.connect()
diff --git a/ms2pip/_utils/encoder.py b/ms2pip/_utils/encoder.py
new file mode 100644
index 00000000..da1dc0be
--- /dev/null
+++ b/ms2pip/_utils/encoder.py
@@ -0,0 +1,322 @@
+"""Peptide and modification handling for MS2PIP."""
+from __future__ import annotations
+
+import logging
+import os
+import tempfile
+from typing import Generator
+
+import numpy as np
+from psm_utils import Peptidoform, PSMList
+from pyteomics import proforma
+
+import ms2pip.exceptions as exceptions
+
+logger = logging.getLogger(__name__)
+
+AMINO_ACIDS = [
+ "A",
+ "C",
+ "D",
+ "E",
+ "F",
+ "G",
+ "H",
+ "I",
+ "K",
+ "M",
+ "N",
+ "P",
+ "Q",
+ "R",
+ "S",
+ "T",
+ "V",
+ "W",
+ "Y",
+]
+
+AMINO_ACID_MASSES = [
+ 71.037114,
+ 103.00919,
+ 115.026943,
+ 129.042593,
+ 147.068414,
+ 57.021464,
+ 137.058912,
+ 113.084064,
+ 128.094963,
+ 131.040485,
+ 114.042927,
+ 97.052764,
+ 128.058578,
+ 156.101111,
+ 87.032028,
+ 101.047679,
+ 99.068414,
+ 186.079313,
+ 163.063329,
+]
+
+AMINO_ACID_IDS = {a: i for i, a in enumerate(AMINO_ACIDS)}
+AMINO_ACID_IDS["L"] = AMINO_ACID_IDS["I"]
+
+
+class Encoder:
+ """Modification-aware encoding of peptidoforms."""
+
+ def __init__(self) -> None:
+ """
+ Modification-aware encoding of peptidoforms.
+
+ MS²PIP requires all modification mass shifts to be written to a file for use in C code
+ before running. This class handles the encoding of peptides and peptidoforms for
+ modifications that have been defined.
+
+ Encoder files are to be passed on to the ``ms2pip_pyx.ms2pip_init`` function. E.g.,
+ ``ms2pip_pyx.ms2pip_init(*encoder.encoder_files)``.
+
+ Notes
+ -----
+ - Either used as a context manager or manually call :py:meth:`write_encoder_files` before
+ use and :py:meth:`remove_encoder_files` after use.
+ - Fixed, labile, and unlocalized modifications are ignored. Fixed modifications
+ should therefore already have been applied (see
+ :py:meth:`psm_utils.psm_list.PSMList.apply_fixed_modifications`).
+
+ """
+ self.modifications = {}
+ self.encoder_files = None
+
+ self._next_mod_id = 38 # Avoid clash with amino acids and mutations (ionbot compatibility)
+
+ def __enter__(self):
+ if not self.encoder_files:
+ self.write_encoder_files()
+ return self
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ self.remove_encoder_files()
+
+ def __repr__(self) -> str:
+ return "{}.{}(modifications={})".format(
+ self.__class__.__module__,
+ self.__class__.__qualname__,
+ self.modifications,
+ )
+
+ @classmethod
+ def from_peptidoform(cls, peptidoform: Peptidoform) -> Encoder:
+ """
+ Create Encoder instance from peptidoform.
+
+ Parameters
+ ----------
+ peptidoform : Peptidoform
+ Peptidoform to use for modification configuration.
+
+ Returns
+ -------
+ Encoder
+ Encoder instance with modifications configured.
+
+ """
+ encoder = cls()
+ encoder._configure_from_peptidoform(peptidoform)
+ encoder.write_encoder_files()
+ return encoder
+
+ @classmethod
+ def from_psm_list(cls, psm_list: PSMList) -> Encoder:
+ """
+ Create Encoder instance from PSMList.
+
+ Parameters
+ ----------
+ psm_list : PSMList
+ PSMList to use for modification configuration.
+
+ Returns
+ -------
+ Encoder
+ Encoder instance with modifications configured.
+
+ """
+ encoder = cls()
+ encoder._configure_from_psm_list(psm_list)
+ encoder.write_encoder_files()
+ return encoder
+
+ def _configure_modification(self, target: str, modification: proforma.TagBase):
+ """
+ Add single pyteomics.proforma modification to configuration.
+
+ Parameters
+ ----------
+ target : str
+ Target amino acid one-letter code or terminus (``n_term`` or ``c_term``).
+ modification : pyteomics.proforma.TagBase
+ Modification to add.
+
+ """
+ if target == "n_term":
+ amino_acid_id = -1
+ elif target == "c_term":
+ amino_acid_id = -2
+ elif target in AMINO_ACID_IDS:
+ amino_acid_id = AMINO_ACID_IDS[target]
+ else:
+ logger.warning(f"Skipping modification for invalid amino acid: {target}")
+ return None
+
+ self.modifications[(target, str(modification))] = {
+ "mod_id": self._next_mod_id,
+ "mass_shift": modification.mass,
+ "amino_acid": target,
+ "amino_acid_id": amino_acid_id,
+ "modification": modification,
+ }
+ self._next_mod_id += 1
+
+ def _configure_from_peptidoform(self, peptidoform: Peptidoform):
+ """Configure encoder with modifications from single Peptidoform."""
+ # Get unique modifications from psm
+ unique_modifications = dict()
+ try:
+ for aa, mods in peptidoform.parsed_sequence:
+ if mods:
+ unique_modifications.update({(aa, str(mod)): mod for mod in mods})
+ for term in ["n_term", "c_term"]:
+ if peptidoform.properties[term]:
+ unique_modifications.update(
+ {(term, str(mod)): mod for mod in peptidoform.properties[term]}
+ )
+ except KeyError as e:
+ raise exceptions.UnresolvableModificationError(e.args[0]) from e
+
+ # Add modification entries
+ for (target, _), mod in unique_modifications.items():
+ self._configure_modification(target, mod)
+
+ def _configure_from_psm_list(self, psm_list: PSMList):
+ """Configure encoder with modifications from PSMList."""
+ # Get unique modifications from psm_list
+ unique_modifications = dict()
+ try:
+ for psm in psm_list:
+ for aa, mods in psm.peptidoform.parsed_sequence:
+ if mods:
+ unique_modifications.update({(aa, str(mod)): mod for mod in mods})
+ for term in ["n_term", "c_term"]:
+ if psm.peptidoform.properties[term]:
+ unique_modifications.update(
+ {(term, str(mod)): mod for mod in psm.peptidoform.properties[term]}
+ )
+ except KeyError as e:
+ raise exceptions.UnresolvableModificationError(e.args[0]) from e
+
+ # Add modification entries
+ for (target, _), mod in unique_modifications.items():
+ self._configure_modification(target, mod)
+
+ def write_encoder_files(self) -> str:
+ """Write configured masses to temporary files for use in C code."""
+ # AA file
+ amino_file = tempfile.NamedTemporaryFile(delete=False, mode="w", newline="\n")
+ for m in AMINO_ACID_MASSES:
+ amino_file.write("{}\n".format(m))
+ amino_file.write("0\n")
+ amino_file.close()
+
+ # PTM file
+ mod_file = tempfile.NamedTemporaryFile(delete=False, mode="w", newline="\n")
+ mod_file.write("{}\n".format(len(self.modifications)))
+ for mod in self.modifications.values():
+ mod_file.write(
+ "{},1,{},{}\n".format(mod["mass_shift"], mod["amino_acid_id"], mod["mod_id"])
+ )
+ mod_file.close()
+
+ # SPTM file (ionbot compatibility)
+ mod_file2 = tempfile.NamedTemporaryFile(delete=False, mode="w", newline="\n")
+ mod_file2.write("0\n")
+ mod_file2.close()
+
+ # Store temporary file names
+ self.encoder_files = (amino_file.name, mod_file.name, mod_file2.name)
+
+ def remove_encoder_files(self):
+ """Remove temporary encoder files."""
+ if self.encoder_files:
+ for f in self.encoder_files:
+ os.remove(f)
+ self.encoder_files = None
+
+ @staticmethod
+ def validate_peptidoform(peptidoform: Peptidoform):
+ """Validate whether a peptidoform can be encoded for MS²PIP."""
+ # Charge required
+ if peptidoform.precursor_charge is None:
+ raise exceptions.InvalidPeptidoformError("Peptidoform charge is required.")
+
+ # Peptides longer then 101 lead to "Segmentation fault (core dumped)"
+ if len(peptidoform.parsed_sequence) > 100:
+ raise exceptions.InvalidPeptidoformError(
+ "Peptidoform sequence cannot be longer than 100 amino acids."
+ )
+ elif len(peptidoform.parsed_sequence) < 4:
+ raise exceptions.InvalidPeptidoformError(
+ "Peptidoform sequence cannot be shorter than 4 amino acids."
+ )
+
+ def encode_peptide(self, peptidoform: Peptidoform) -> np.ndarray:
+ """Encode a peptide (without modifications) for MS²PIP."""
+ self.validate_peptidoform(peptidoform)
+
+ try:
+ encoded = [0] + [AMINO_ACID_IDS[aa] for aa, _ in peptidoform.parsed_sequence] + [0]
+ except KeyError as e:
+ raise exceptions.InvalidAminoAcidError(
+ f"Unsupported amino acid found in peptide `{peptidoform.proforma}`"
+ ) from e
+ return np.array(encoded, dtype=np.uint16)
+
+ def encode_peptidoform(self, peptidoform: Peptidoform) -> np.ndarray:
+ """
+ Encode a peptidoform for MS²PIP.
+
+ Notes
+ -----
+ - Multiple modifications per site is not supported. The first modification is used.
+ - Fixed, labile, and unlocalized modifications are ignored. Fixed modifications
+ should therefore already have been applied (see
+ :py:meth:`psm_utils.PSMList.apply_fixed_modifications`).
+
+ """
+
+ def _generate_encoding(peptidoform) -> Generator[int, None, None]:
+ if peptidoform.properties["n_term"]:
+ mod_str = str(peptidoform.properties["n_term"][0])
+ yield self.modifications["n_term", mod_str]["mod_id"]
+ else:
+ yield 0
+
+ for aa, mods in peptidoform.parsed_sequence:
+ try:
+ if not mods:
+ yield AMINO_ACID_IDS[aa]
+ else:
+ yield self.modifications[aa, str(mods[0])]["mod_id"]
+ except KeyError as e:
+ raise exceptions.InvalidAminoAcidError(
+ f"Unsupported amino acid found in peptide `{peptidoform.proforma}`"
+ ) from e
+
+ if peptidoform.properties["c_term"]:
+ mod_str = str(peptidoform.properties["c_term"][0])
+ yield self.modifications["c_term", mod_str]["mod_id"]
+ else:
+ yield 0
+
+ self.validate_peptidoform(peptidoform)
+ return np.array(list(_generate_encoding(peptidoform)), dtype=np.uint16)
diff --git a/ms2pip/_utils/feature_names.py b/ms2pip/_utils/feature_names.py
new file mode 100644
index 00000000..35632ab8
--- /dev/null
+++ b/ms2pip/_utils/feature_names.py
@@ -0,0 +1,40 @@
+from ms2pip._utils.encoder import AMINO_ACIDS
+
+
+def get_feature_names():
+ """Generate a list of all prediction features in order."""
+ aa_properties = ["basi", "heli", "hydro", "iso"]
+ quartiles = ["min", "q1", "q2", "q3", "max"]
+ ions = ["n", "c"]
+
+ # Precursor properties
+ names = ["p_length", "p_charge"]
+
+ # One-hot encoded charge states
+ for t in range(5):
+ names.append(f"p_charge_{t + 1}")
+
+ # Full precursor property quartiles
+ for prop in aa_properties:
+ for quartile in quartiles:
+ names.append(f"p_{prop}_{quartile}")
+
+ # Ion lengths
+ for ion in ions:
+ names.append(f"{ion}_length")
+
+ # Ion amino acid counts
+ for aa in AMINO_ACIDS:
+ for ion in ions:
+ names.append(f"{ion}_count_{aa}")
+
+ for prop in aa_properties:
+ # Properties for specific positions
+ for pos in ["p0", "p-1", "pi-1", "pi", "pi+1", "pi+2"]:
+ names.append(f"{pos}_{prop}")
+ # Ion property quartiles
+ for ion in ions:
+ for metric in ["sum"] + quartiles:
+ names.append(f"{ion}_{prop}_{metric}")
+
+ return names
diff --git a/ms2pip/_utils/ion_mobility.py b/ms2pip/_utils/ion_mobility.py
new file mode 100644
index 00000000..8a3764b1
--- /dev/null
+++ b/ms2pip/_utils/ion_mobility.py
@@ -0,0 +1,32 @@
+"""Module for ion mobility prediction with IM²Deep."""
+
+import logging
+
+import pandas as pd
+from psm_utils import PSMList
+
+logger = logging.getLogger(__name__)
+
+
+class IonMobility:
+ """Predict ion mobility using IM²Deep."""
+
+ def __init__(self, processes=1) -> None:
+ # Lazy import to avoid loading loading heavy dependencies when not needed
+ try:
+ from im2deep.im2deep import predict_ccs # noqa: F401
+
+ self.predict_fn = predict_ccs
+ self.processes = processes
+ except ImportError as e:
+ raise ImportError(
+ "The 'im2deep' package is required for ion mobility prediction."
+ ) from e
+
+ def add_im_predictions(self, psm_list: PSMList) -> None:
+ """Add ion mobility predictions to the PSMList."""
+ logger.info("Predicting ion mobility...")
+ predictions: pd.Series = self.predict_fn(
+ psm_list, write_output=False, n_jobs=self.processes
+ )
+ psm_list["ion_mobility"] = predictions.values
diff --git a/ms2pip/_utils/psm_input.py b/ms2pip/_utils/psm_input.py
new file mode 100644
index 00000000..7a0cacc6
--- /dev/null
+++ b/ms2pip/_utils/psm_input.py
@@ -0,0 +1,29 @@
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+from typing import Union
+
+import psm_utils.io.peptide_record
+from psm_utils import PSMList
+
+logger = logging.getLogger(__name__)
+
+
+def read_psms(psms: Union[str, Path, PSMList], filetype: Union[str, None]) -> PSMList:
+ """Read PSMList or PSM file."""
+ # Read PSMs
+ if isinstance(psms, (str, Path)):
+ logger.info("Reading PSMs...")
+ psm_list = psm_utils.io.read_file(psms, filetype=filetype or "infer")
+ elif isinstance(psms, PSMList):
+ psm_list = psms
+ else:
+ raise TypeError("Invalid type for psms. Should be str, Path, or PSMList.")
+
+ # Apply fixed modifications if any
+ psm_list.apply_fixed_modifications()
+
+ logger.debug(f"Read {len(psm_list)} PSMs.")
+
+ return psm_list
diff --git a/ms2pip/retention_time.py b/ms2pip/_utils/retention_time.py
similarity index 81%
rename from ms2pip/retention_time.py
rename to ms2pip/_utils/retention_time.py
index 5a62d543..043f24a2 100644
--- a/ms2pip/retention_time.py
+++ b/ms2pip/_utils/retention_time.py
@@ -2,7 +2,9 @@
import os
from pathlib import Path
+import numpy as np
import pandas as pd
+import psm_utils.io.peptide_record as peptide_record
logger = logging.getLogger(__name__)
@@ -11,7 +13,7 @@
class RetentionTime:
- def __init__(self, predictor="deeplc", config=None, num_cpu=None):
+ def __init__(self, predictor="deeplc", config=None, processes=None):
"""
Initialize peptide retention time predictor
@@ -35,7 +37,7 @@ def __init__(self, predictor="deeplc", config=None, num_cpu=None):
self.config["deeplc"] = {
"verbose": False,
"calibration_file": None,
- "n_jobs": num_cpu,
+ "n_jobs": processes,
}
def _get_irt_peptides(self):
@@ -84,12 +86,13 @@ def _init_deeplc(self):
if best_model_for_irt.exists():
deeplc_params["path_model"] = str(best_model_for_irt)
+ # PyGAM results in strange calibration on limited set of iRT peptides
+ deeplc_params["pygam_calibration"] = False
+
# Remove from deeplc_params to control calibration here instead of in DeepLC
if "calibration_file" in deeplc_params:
del deeplc_params["calibration_file"]
- # PyGAM results in strange calibration on limited set of iRT peptides
- deeplc_params["pygam_calibration"] = False
self.deeplc_predictor = deeplc.DeepLC(**deeplc_params)
logger.info("Calibrating DeepLC...")
@@ -100,43 +103,35 @@ def _prepare_deeplc_peptide_df(self):
Prepare DeepLC peptide DataFrame
"""
column_map = {"peptide": "seq", "modifications": "modifications"}
- self.deeplc_pep_df = self.peprec[column_map.keys()].copy()
- self.deeplc_pep_df.rename(columns=column_map, inplace=True)
+ peprec = peptide_record.to_dataframe(self.psm_list)[column_map.keys()]
+ self.deeplc_pep_df = peprec.rename(columns=column_map)
def _run_deeplc(self):
- """
- Run DeepLC
- """
+ """Run DeepLC."""
logger.info("Predicting retention times with DeepLC...")
self.deeplc_preds = self.deeplc_predictor.make_preds(
seq_df=self.deeplc_pep_df.fillna("")
)
def _parse_deeplc_preds(self):
- """
- Add DeepLC predictions to peprec DataFrame
- """
- self.peprec["rt"] = self.deeplc_preds
+ """Add DeepLC predictions to peprec DataFrame."""
+ self.psm_list["retention_time"] = np.array(self.deeplc_preds, dtype=np.float32)
def _predict_deeplc(self):
- """
- Predict retention times using DeepLC
- """
+ """Predict retention times using DeepLC."""
if not self.deeplc_predictor:
self._init_deeplc()
self._prepare_deeplc_peptide_df()
self._run_deeplc()
self._parse_deeplc_preds()
- def add_rt_predictions(self, peprec):
+ def add_rt_predictions(self, psm_list):
"""
- Run RT predictor and add predictions to peprec DataFrame.
+ Run RT predictor and add predictions to a PSMList.
- peprec: pandas.DataFrame
- MS2PIP-style peprec DataFrame with peptides for which to predict retention
- times
+ psm_list: PSMList
"""
- self.peprec = peprec
+ self.psm_list = psm_list
if self.predictor == "deeplc":
self._predict_deeplc()
diff --git a/ms2pip/predict_xgboost.py b/ms2pip/_utils/xgb_models.py
similarity index 60%
rename from ms2pip/predict_xgboost.py
rename to ms2pip/_utils/xgb_models.py
index 8e9c6ea5..c1e7233a 100644
--- a/ms2pip/predict_xgboost.py
+++ b/ms2pip/_utils/xgb_models.py
@@ -1,4 +1,4 @@
-"""Get predictions directly from XGBoost model, within ms2pip framework."""
+"""Utilities for handling XGBoost model files within the MS²PIP prediction framework."""
import hashlib
import logging
@@ -14,66 +14,82 @@
logger = logging.getLogger(__name__)
-def get_predictions_xgb(features, num_ions, model_params, model_dir, num_cpu=1):
+def validate_requested_xgb_model(xgboost_model_files, xgboost_model_hashes, model_dir):
+ """Validate requested XGBoost models, and download if necessary"""
+ for _, model_file in xgboost_model_files.items():
+ if not _check_model_presence(model_file, xgboost_model_hashes[model_file], model_dir):
+ _download_model(model_file, xgboost_model_hashes[model_file], model_dir)
+
+
+def get_predictions_xgb(features, num_ions, model_params, model_dir, processes=1):
"""
Get predictions starting from feature vectors in DMatrix object.
Parameters
----------
- features: xgb.DMatrix
- Feature vectors in DMatrix object
+ features: xgb.DMatrix, np.ndarray
+ Feature vectors in DMatrix object or as Numpy array.
num_ions: list[int]
List with number of ions (per series) for each peptide, i.e. peptide length - 1
model_params: dict
Model configuration as defined in ms2pip.ms2pipC.MODELS.
model_dir: str
Directory where model files are stored.
- num_cpu: int
+ processes: int
Number of CPUs to use in multiprocessing
"""
# Init models
- xgboost_models = initialize_xgb_models(
+ xgboost_models = _initialize_xgb_models(
model_params["xgboost_model_files"],
model_dir,
- num_cpu,
+ processes,
)
+ if isinstance(features, np.ndarray):
+ features = xgb.DMatrix(features)
logger.debug("Predicting intensities from XGBoost model files...")
- preds_list = []
+ prediction_dict = {}
for ion_type, xgb_model in xgboost_models.items():
# Get predictions from XGBoost model
preds = xgb_model.predict(features)
+ preds = preds.clip(min=np.log2(0.001)) # Clip negative intensities
xgb_model.__del__()
# Reshape into arrays for each peptide
- preds = _split_list_by_lengths(preds, num_ions)
- if ion_type in ["x", "y", "y2", "z"]:
- preds = [np.array(x[::-1], dtype=np.float32) for x in preds]
- elif ion_type in ["a", "b", "b2", "c"]:
- preds = [np.array(x, dtype=np.float32) for x in preds]
+ if ion_type.lower() in ["x", "y", "y2", "z"]:
+ preds = _split_list_by_lengths(preds, num_ions, reverse=True)
+ elif ion_type.lower() in ["a", "b", "b2", "c"]:
+ preds = _split_list_by_lengths(preds, num_ions, reverse=False)
else:
raise ValueError(f"Unsupported ion_type: {ion_type}")
- preds_list.append(preds)
+ prediction_dict[ion_type] = preds
- predictions = [list(t) for t in zip(*preds_list)]
+ # Convert to list per peptide with dicts per ion type
+ num_peptides = len(list(prediction_dict.values())[0])
+ predictions = [{k: v[i] for k, v in prediction_dict.items()} for i in range(num_peptides)]
return predictions
-def _split_list_by_lengths(list_in, lengths):
+def _split_list_by_lengths(list_in, lengths, reverse=False):
+ """Split list of predictions into sublists per peptide given their lengths."""
list_in = iter(list_in)
- return [list(islice(list_in, elem)) for elem in lengths]
+ if reverse:
+ list_out = [np.array(list(islice(list_in, e)), dtype=np.float32)[::-1] for e in lengths]
+ else:
+ list_out = [np.array(list(islice(list_in, e)), dtype=np.float32) for e in lengths]
+ return list_out
-def check_model_presence(model, model_hash, model_dir):
+def _check_model_presence(model, model_hash, model_dir):
"""Check whether XGBoost model file is downloaded."""
filename = os.path.join(model_dir, model)
if not os.path.isfile(filename):
return False
- return check_model_integrity(filename, model_hash)
+ return _check_model_integrity(filename, model_hash)
-def download_model(model, model_hash, model_dir):
+def _download_model(model, model_hash, model_dir):
"""Download the xgboost model from the Genesis server."""
if not os.path.isdir(model_dir):
os.mkdir(model_dir)
@@ -83,11 +99,11 @@ def download_model(model, model_hash, model_dir):
urllib.request.urlretrieve(
os.path.join("http://genesis.ugent.be/uvpublicdata/ms2pip/", model), filename
)
- if not check_model_integrity(filename, model_hash):
+ if not _check_model_integrity(filename, model_hash):
raise InvalidXGBoostModelError()
-def check_model_integrity(filename, model_hash):
+def _check_model_integrity(filename, model_hash):
"""Check that models are correctly downloaded."""
sha1_hash = hashlib.sha1()
with open(filename, "rb") as model_file:
@@ -103,16 +119,7 @@ def check_model_integrity(filename, model_hash):
return False
-def validate_requested_xgb_model(xgboost_model_files, xgboost_model_hashes, model_dir):
- """Validate requested XGBoost models, and download if necessary"""
- for _, model_file in xgboost_model_files.items():
- if not check_model_presence(
- model_file, xgboost_model_hashes[model_file], model_dir
- ):
- download_model(model_file, xgboost_model_hashes[model_file], model_dir)
-
-
-def initialize_xgb_models(xgboost_model_files, model_dir, nthread) -> dict:
+def _initialize_xgb_models(xgboost_model_files, model_dir, nthread) -> dict:
"""Initialize xgboost models and return them in a dict with ion types as keys."""
xgb.set_config(verbosity=0)
xgboost_models = {}
diff --git a/ms2pip/config_parser.py b/ms2pip/config_parser.py
deleted file mode 100644
index d162ce2c..00000000
--- a/ms2pip/config_parser.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import os
-
-import tomlkit
-
-
-class UnsupportedConfigFormatError(ValueError):
- pass
-
-
-class NoFilepathError(Exception):
- pass
-
-
-class ConfigParser:
- """
- MS2PIP Configuration parser
-
- Parameters
- ----------
- filepath: str
- Path to config file to load from or write to (optional)
- """
-
- def __init__(self, filepath=None):
- self.filepath = filepath
- self.config = dict()
-
- if self.filepath:
- self.load()
-
- def _set_filepath(self, filepath):
- """
- Set config filepath
-
- Parameters
- ----------
- filepath: str
- Path to config file to load
-
- Raises
- ------
- NoFilepathError
- If both filepath and self.filepath are None
- """
- if not filepath:
- if not self.filepath:
- raise NoFilepathError()
- else:
- self.filepath = filepath
-
- def _load_ms2pip_txt(self):
- params = {}
- params["ptm"] = []
- params["sptm"] = []
- params["gptm"] = []
-
- with open(self.filepath) as f:
- for line in f:
- line = line.strip()
- if not line or line[0] == "#":
- continue
- (par, val) = line.split("=")
- if par == "ptm":
- params["ptm"].append(val)
- elif par == "sptm":
- params["sptm"].append(val)
- elif par == "gptm":
- params["gptm"].append(val)
- else:
- params[par] = val
-
- if "frag_error" in params:
- params["frag_error"] = float(params["frag_error"])
-
- self.config["ms2pip"] = params
-
- def _load_toml(self):
- toml_file = ""
- with open(self.filepath, "rt") as f_in:
- for line in f_in:
- toml_file += line
- self.config = tomlkit.loads(toml_file)
-
- def _write_toml(self):
- self.filepath = os.path.splitext(self.filepath)[0] + ".toml"
- with open(self.filepath, "wt+") as f_out:
- f_out.write(tomlkit.dumps(self.config))
-
- def load(self, filepath=None, config_format=None):
- """
- Load configuration file.
-
- Parameters
- ----------
- filepath: str
- Path to config file to load (optional)
- config_format: str
- Config file format, either `txt` or `toml`. If None, the format will be
- inferred from the filename extension. (optional)
- """
- self._set_filepath(filepath)
-
- if not config_format:
- config_format = os.path.splitext(self.filepath)[1].lower().lstrip(".")
-
- if config_format == "toml":
- self._load_toml()
- elif config_format in ("txt", "config", "ms2pip"):
- self._load_ms2pip_txt()
- else:
- raise UnsupportedConfigFormatError(
- "Configuration file should have extension `txt`, `config`, or "
- "`ms2pip` (text-based format) or `toml` (TOML-based format), not "
- f"`{config_format}`",
- )
-
- def write(self, filepath=None, config_format="toml"):
- """
- Write configuration to file.
-
- Parameters
- ----------
- filepath: str
- Path where config file will be written (optional)
- config_format: str
- Config file format to write, default: `toml` (optional)
- """
- self._set_filepath(filepath)
-
- if config_format.lower() == "toml":
- self._write_toml()
- else:
- raise UnsupportedConfigFormatError(config_format)
diff --git a/ms2pip/constants.py b/ms2pip/constants.py
new file mode 100644
index 00000000..c72affe2
--- /dev/null
+++ b/ms2pip/constants.py
@@ -0,0 +1,175 @@
+"""Constants and fixed configurations for MS²PIP."""
+
+# Models and their properties
+# id is passed to get_predictions to select model
+# ion_types is required to write the ion types in the headers of the result files
+# features_version is required to select the features version
+MODELS = {
+ "CID": {
+ "id": 0,
+ "ion_types": ["B", "Y"],
+ "peaks_version": "general",
+ "features_version": "normal",
+ "xgboost_model_files": {
+ "b": "model_20190107_CID_train_B.xgboost",
+ "y": "model_20190107_CID_train_Y.xgboost",
+ },
+ "model_hash": {
+ "model_20190107_CID_train_B.xgboost": "4398c6ebe23e2f37c0aca42b095053ecea6fb427",
+ "model_20190107_CID_train_Y.xgboost": "e0a9eb37e50da35a949d75807d66fb57e44aca0f",
+ },
+ },
+ "HCD2019": {
+ "id": 1,
+ "ion_types": ["B", "Y"],
+ "peaks_version": "general",
+ "features_version": "normal",
+ },
+ "TTOF5600": {
+ "id": 2,
+ "ion_types": ["B", "Y"],
+ "peaks_version": "general",
+ "features_version": "normal",
+ "xgboost_model_files": {
+ "b": "model_20190107_TTOF5600_train_B.xgboost",
+ "y": "model_20190107_TTOF5600_train_Y.xgboost",
+ },
+ "model_hash": {
+ "model_20190107_TTOF5600_train_B.xgboost": "ab2e28dfbc4ee60640253b0b4c127fc272c9d0ed",
+ "model_20190107_TTOF5600_train_Y.xgboost": "f8e9ddd8ca78ace06f67460a2fea0d8fa2623452",
+ },
+ },
+ "TMT": {
+ "id": 3,
+ "ion_types": ["B", "Y"],
+ "peaks_version": "general",
+ "features_version": "normal",
+ },
+ "iTRAQ": {
+ "id": 4,
+ "ion_types": ["B", "Y"],
+ "peaks_version": "general",
+ "features_version": "normal",
+ "xgboost_model_files": {
+ "b": "model_20190107_iTRAQ_train_B.xgboost",
+ "y": "model_20190107_iTRAQ_train_Y.xgboost",
+ },
+ "model_hash": {
+ "model_20190107_iTRAQ_train_B.xgboost": "b8d94ad329a245210c652a5b35d724d2c74d0d50",
+ "model_20190107_iTRAQ_train_Y.xgboost": "56ae87d56fd434b53fcc1d291745cabb7baf463a",
+ },
+ },
+ "iTRAQphospho": {
+ "id": 5,
+ "ion_types": ["B", "Y"],
+ "peaks_version": "general",
+ "features_version": "normal",
+ "xgboost_model_files": {
+ "b": "model_20190107_iTRAQphospho_train_B.xgboost",
+ "y": "model_20190107_iTRAQphospho_train_Y.xgboost",
+ },
+ "model_hash": {
+ "model_20190107_iTRAQphospho_train_B.xgboost": "e283b158cc50e219f42f93be624d0d0ac01d6b49",
+ "model_20190107_iTRAQphospho_train_Y.xgboost": "261b2e1810a299ed7ebf193ce1fb81a608c07d3b",
+ },
+ },
+ # ETD': {'id': 6, 'ion_types': ['B', 'Y', 'C', 'Z'], 'peaks_version': 'etd', 'features_version': 'normal'},
+ "HCDch2": {
+ "id": 7,
+ "ion_types": ["B", "Y", "B2", "Y2"],
+ "peaks_version": "ch2",
+ "features_version": "normal",
+ },
+ "CIDch2": {
+ "id": 8,
+ "ion_types": ["B", "Y", "B2", "Y2"],
+ "peaks_version": "ch2",
+ "features_version": "normal",
+ "xgboost_model_files": {
+ "b": "model_20190107_CID_train_B.xgboost",
+ "y": "model_20190107_CID_train_Y.xgboost",
+ "b2": "model_20190107_CID_train_B2.xgboost",
+ "y2": "model_20190107_CID_train_Y2.xgboost",
+ },
+ "model_hash": {
+ "model_20190107_CID_train_B.xgboost": "4398c6ebe23e2f37c0aca42b095053ecea6fb427",
+ "model_20190107_CID_train_Y.xgboost": "e0a9eb37e50da35a949d75807d66fb57e44aca0f",
+ "model_20190107_CID_train_B2.xgboost": "602f2fc648890aebbbe2646252ade658af3221a3",
+ "model_20190107_CID_train_Y2.xgboost": "4e4ad0f1d4606c17015aae0f74edba69f684d399",
+ },
+ },
+ "HCD2021": {
+ "id": 9,
+ "ion_types": ["B", "Y"],
+ "peaks_version": "general",
+ "features_version": "normal",
+ "xgboost_model_files": {
+ "b": "model_20210416_HCD2021_B.xgboost",
+ "y": "model_20210416_HCD2021_Y.xgboost",
+ },
+ "model_hash": {
+ "model_20210416_HCD2021_B.xgboost": "c086c599f618b199bbb36e2411701fb2866b24c8",
+ "model_20210416_HCD2021_Y.xgboost": "22a5a137e29e69fa6d4320ed7d701b61cbdc4fcf",
+ },
+ },
+ "Immuno-HCD": {
+ "id": 10,
+ "ion_types": ["B", "Y"],
+ "peaks_version": "general",
+ "features_version": "normal",
+ "xgboost_model_files": {
+ "b": "model_20210316_Immuno_HCD_B.xgboost",
+ "y": "model_20210316_Immuno_HCD_Y.xgboost",
+ },
+ "model_hash": {
+ "model_20210316_Immuno_HCD_B.xgboost": "977466d378de2e89c6ae15b4de8f07800d17a7b7",
+ "model_20210316_Immuno_HCD_Y.xgboost": "71948e1b9d6c69cb69b9baf84d361a9f80986fea",
+ },
+ },
+ "CID-TMT": {
+ "id": 11,
+ "ion_types": ["B", "Y"],
+ "peaks_version": "general",
+ "features_version": "normal",
+ "xgboost_model_files": {
+ "b": "model_20220104_CID_TMT_B.xgboost",
+ "y": "model_20220104_CID_TMT_Y.xgboost",
+ },
+ "model_hash": {
+ "model_20220104_CID_TMT_B.xgboost": "fa834162761a6ae444bb6523c9c1a174b9738389",
+ "model_20220104_CID_TMT_Y.xgboost": "299539179ca55d4ac82e9aed6a4e0bd134a9a41e",
+ },
+ },
+ "timsTOF2023": {
+ "id": 12,
+ "ion_types": ["B", "Y"],
+ "peaks_version": "general",
+ "features_version": "normal",
+ "xgboost_model_files": {
+ "b": "model_20230912_timsTOF_B.xgboost",
+ "y": "model_20230912_timsTOF_Y.xgboost",
+ },
+ "model_hash": {
+ "model_20230912_timsTOF_B.xgboost": "6beb557052455310d8c66311c86866dda8291f4b",
+ "model_20230912_timsTOF_Y.xgboost": "8edd87e0fba5f338d0a0881b5afbcf2f48ec5268",
+ },
+ },
+ "timsTOF2024": {
+ "id": 13,
+ "ion_types": ["B", "Y"],
+ "peaks_version": "general",
+ "features_version": "normal",
+ "xgboost_model_files": {
+ "b": "model_20240105_timsTOF_B.xgboost",
+ "y": "model_20240105_timsTOF_Y.xgboost",
+ },
+ "model_hash": {
+ "model_20240105_timsTOF_B.xgboost": "d70e145c15cf2bfa30968077a68409699b2fa541",
+ "model_20240105_timsTOF_Y.xgboost": "3f0414ee1ad7cff739e0d6242e25bfc22b6ebfe5",
+ },
+ },
+}
+
+
+MODELS["HCD"] = MODELS["HCD2021"]
+MODELS["timsTOF"] = MODELS["timsTOF2024"]
diff --git a/ms2pip/core.py b/ms2pip/core.py
new file mode 100644
index 00000000..f3dd5ec0
--- /dev/null
+++ b/ms2pip/core.py
@@ -0,0 +1,1010 @@
+#!/usr/bin/env python
+from __future__ import annotations
+
+import itertools
+import logging
+import multiprocessing
+import multiprocessing.dummy
+import re
+from collections import defaultdict
+from math import ceil
+from pathlib import Path
+from typing import Any, Callable, Generator, Iterable, List, Optional, Tuple, Union
+
+import numpy as np
+import pandas as pd
+from psm_utils import PSM, Peptidoform, PSMList
+from rich.progress import track
+
+import ms2pip.exceptions as exceptions
+from ms2pip import spectrum_output
+from ms2pip._cython_modules import ms2pip_pyx
+from ms2pip._utils.encoder import Encoder
+from ms2pip._utils.feature_names import get_feature_names
+from ms2pip._utils.psm_input import read_psms
+from ms2pip._utils.retention_time import RetentionTime
+from ms2pip._utils.ion_mobility import IonMobility
+from ms2pip._utils.xgb_models import get_predictions_xgb, validate_requested_xgb_model
+from ms2pip.constants import MODELS
+from ms2pip.result import ProcessingResult, calculate_correlations
+from ms2pip.search_space import ProteomeSearchSpace
+from ms2pip.spectrum_input import read_spectrum_file
+from ms2pip.spectrum_output import SUPPORTED_FORMATS
+
+logger = logging.getLogger(__name__)
+
+
+def predict_single(
+ peptidoform: Union[Peptidoform, str],
+ model: Optional[str] = "HCD",
+ model_dir: Optional[Union[str, Path]] = None,
+) -> ProcessingResult:
+ """
+ Predict fragmentation spectrum for a single peptide.\f
+ """
+ if isinstance(peptidoform, str):
+ peptidoform = Peptidoform(peptidoform)
+ psm = PSM(peptidoform=peptidoform, spectrum_id=0)
+ model_dir = model_dir if model_dir else Path.home() / ".ms2pip"
+ ion_types = [it.lower() for it in MODELS[model]["ion_types"]]
+
+ with Encoder.from_peptidoform(peptidoform) as encoder:
+ ms2pip_pyx.ms2pip_init(*encoder.encoder_files)
+ result = _process_peptidoform(0, psm, model, encoder, ion_types=ion_types)
+
+ if "xgboost_model_files" in MODELS[model].keys():
+ validate_requested_xgb_model(
+ MODELS[model]["xgboost_model_files"],
+ MODELS[model]["model_hash"],
+ model_dir,
+ )
+ predictions = np.array(
+ get_predictions_xgb(
+ result.feature_vectors,
+ [len(peptidoform.parsed_sequence) - 1],
+ MODELS[model],
+ model_dir,
+ )
+ )
+ result.predicted_intensity = predictions[0] # Only one spectrum in predictions
+ result.feature_vectors = None
+
+ return result
+
+
+def predict_batch(
+ psms: Union[PSMList, str, Path],
+ add_retention_time: bool = False,
+ add_ion_mobility: bool = False,
+ psm_filetype: Optional[str] = None,
+ model: Optional[str] = "HCD",
+ model_dir: Optional[Union[str, Path]] = None,
+ processes: Optional[int] = None,
+) -> List[ProcessingResult]:
+ """
+ Predict fragmentation spectra for a batch of peptides.\f
+
+ Parameters
+ ----------
+ psms
+ PSMList or path to PSM file that is supported by psm_utils.
+ psm_filetype
+ Filetype of the PSM file. By default, None. Should be one of the supported psm_utils
+ filetypes. See https://psm-utils.readthedocs.io/en/stable/#supported-file-formats.
+ add_retention_time
+ Add retention time predictions with DeepLC (Requires optional DeepLC dependency).
+ add_ion_mobility
+ Add ion mobility predictions with IM2Deep (Requires optional IM2Deep dependency).
+ model
+ Model to use for prediction. Default: "HCD".
+ model_dir
+ Directory where XGBoost model files are stored. Default: `~/.ms2pip`.
+ processes
+ Number of parallel processes for multiprocessing steps. By default, all available.
+
+ Returns
+ -------
+ predictions: List[ProcessingResult]
+ Predicted spectra with theoretical m/z and predicted intensity values.
+
+ """
+ if isinstance(psms, list):
+ psms = PSMList(psm_list=psms)
+ psm_list = read_psms(psms, filetype=psm_filetype)
+
+ if add_retention_time:
+ logger.info("Adding retention time predictions")
+ rt_predictor = RetentionTime(processes=processes)
+ rt_predictor.add_rt_predictions(psm_list)
+
+ if add_ion_mobility:
+ logger.info("Adding ion mobility predictions")
+ im_predictor = IonMobility(processes=processes)
+ im_predictor.add_im_predictions(psm_list)
+
+ with Encoder.from_psm_list(psm_list) as encoder:
+ ms2pip_parallelized = _Parallelized(
+ encoder=encoder,
+ model=model,
+ model_dir=model_dir,
+ processes=processes,
+ )
+ logger.info("Processing peptides...")
+ results = ms2pip_parallelized.process_peptides(psm_list)
+
+ return results
+
+
+def predict_library(
+ fasta_file: Optional[Union[str, Path]] = None,
+ config: Optional[Union[ProteomeSearchSpace, dict, str, Path]] = None,
+ add_retention_time: bool = False,
+ add_ion_mobility: bool = False,
+ model: Optional[str] = "HCD",
+ model_dir: Optional[Union[str, Path]] = None,
+ batch_size: int = 100000,
+ processes: Optional[int] = None,
+) -> Generator[ProcessingResult, None, None]:
+ """
+ Predict spectral library from protein FASTA file.\f
+
+ Parameters
+ ----------
+ fasta_file
+ Path to FASTA file with protein sequences. Required if `search-space-config` is not
+ provided.
+ config
+ ProteomeSearchSpace, or a dictionary or path to JSON file with proteome search space
+ parameters. Required if `fasta_file` is not provided.
+ add_retention_time
+ Add retention time predictions with DeepLC (Requires optional DeepLC dependency).
+ add_ion_mobility
+ Add ion mobility predictions with IM2Deep (Requires optional IM2Deep dependency).
+ model
+ Model to use for prediction. Default: "HCD".
+ model_dir
+ Directory where XGBoost model files are stored. Default: `~/.ms2pip`.
+ batch_size
+ Number of peptides to process in each batch.
+ processes
+ Number of parallel processes for multiprocessing steps. By default, all available.
+
+ Yields
+ ------
+ predictions: List[ProcessingResult]
+ Predicted spectra with theoretical m/z and predicted intensity values.
+
+ """
+ if fasta_file and config:
+ # Use provided proteome, but overwrite fasta_file
+ config = ProteomeSearchSpace.from_any(config)
+ config.fasta_file = fasta_file
+ elif fasta_file and not config:
+ # Default proteome search space with provided fasta_file
+ config = ProteomeSearchSpace(fasta_file=fasta_file)
+ elif not fasta_file and config:
+ # Use provided proteome
+ config = ProteomeSearchSpace.from_any(config)
+ else:
+ raise ValueError("Either `fasta_file` or `config` must be provided.")
+
+ search_space = ProteomeSearchSpace.from_any(config)
+ search_space.build()
+
+ for batch in track(
+ _into_batches(search_space, batch_size=batch_size),
+ description="Predicting spectra...",
+ total=ceil(len(search_space) / batch_size),
+ ):
+ logging.disable(logging.CRITICAL)
+ yield predict_batch(
+ search_space.filter_psms_by_mz(PSMList(psm_list=list(batch))),
+ add_retention_time=add_retention_time,
+ add_ion_mobility=add_ion_mobility,
+ model=model,
+ model_dir=model_dir,
+ processes=processes,
+ )
+ logging.disable(logging.NOTSET)
+
+
+def correlate(
+ psms: Union[PSMList, str, Path],
+ spectrum_file: Union[str, Path],
+ psm_filetype: Optional[str] = None,
+ spectrum_id_pattern: Optional[str] = None,
+ compute_correlations: bool = False,
+ add_retention_time: bool = False,
+ add_ion_mobility: bool = False,
+ model: Optional[str] = "HCD",
+ model_dir: Optional[Union[str, Path]] = None,
+ ms2_tolerance: float = 0.02,
+ processes: Optional[int] = None,
+) -> List[ProcessingResult]:
+ """
+ Compare predicted and observed intensities and optionally compute correlations.\f
+
+ Parameters
+ ----------
+ psms
+ PSMList or path to PSM file that is supported by psm_utils.
+ spectrum_file
+ Path to spectrum file with target intensities.
+ psm_filetype
+ Filetype of the PSM file. By default, None. Should be one of the supported psm_utils
+ filetypes. See https://psm-utils.readthedocs.io/en/stable/#supported-file-formats.
+ spectrum_id_pattern
+ Regular expression pattern to apply to spectrum titles before matching to
+ peptide file ``spec_id`` entries.
+ compute_correlations
+ Compute correlations between predictions and targets.
+ add_retention_time
+ Add retention time predictions with DeepLC (Requires optional DeepLC dependency).
+ add_ion_mobility
+ Add ion mobility predictions with IM2Deep (Requires optional IM2Deep dependency).
+ model
+ Model to use for prediction. Default: "HCD".
+ model_dir
+ Directory where XGBoost model files are stored. Default: `~/.ms2pip`.
+ ms2_tolerance
+ MS2 tolerance in Da for observed spectrum peak annotation. By default, 0.02 Da.
+ processes
+ Number of parallel processes for multiprocessing steps. By default, all available.
+
+ Returns
+ -------
+ results: List[ProcessingResult]
+ Predicted spectra with theoretical m/z and predicted intensity values, and optionally,
+ correlations.
+
+ """
+ psm_list = read_psms(psms, filetype=psm_filetype)
+ spectrum_id_pattern = spectrum_id_pattern if spectrum_id_pattern else "(.*)"
+
+ if add_retention_time:
+ logger.info("Adding retention time predictions")
+ rt_predictor = RetentionTime(processes=processes)
+ rt_predictor.add_rt_predictions(psm_list)
+
+ if add_ion_mobility:
+ logger.info("Adding ion mobility predictions")
+ im_predictor = IonMobility(processes=processes)
+ im_predictor.add_im_predictions(psm_list)
+
+ with Encoder.from_psm_list(psm_list) as encoder:
+ ms2pip_parallelized = _Parallelized(
+ encoder=encoder,
+ model=model,
+ model_dir=model_dir,
+ ms2_tolerance=ms2_tolerance,
+ processes=processes,
+ )
+ logger.info("Processing spectra and peptides...")
+ results = ms2pip_parallelized.process_spectra(psm_list, spectrum_file, spectrum_id_pattern)
+
+ # Correlations also requested
+ if compute_correlations:
+ logger.info("Computing correlations")
+ calculate_correlations(results)
+ logger.info(f"Median correlation: {np.median(list(r.correlation for r in results))}")
+
+ return results
+
+
+def get_training_data(
+ psms: Union[PSMList, str, Path],
+ spectrum_file: Union[str, Path],
+ psm_filetype: Optional[str] = None,
+ spectrum_id_pattern: Optional[str] = None,
+ model: Optional[str] = "HCD",
+ ms2_tolerance: float = 0.02,
+ processes: Optional[int] = None,
+):
+ """
+ Extract feature vectors and target intensities from observed spectra for training.\f
+
+ Parameters
+ ----------
+ psms
+ PSMList or path to PSM file that is supported by psm_utils.
+ spectrum_file
+ Path to spectrum file with target intensities.
+ psm_filetype
+ Filetype of the PSM file. By default, None. Should be one of the supported psm_utils
+ filetypes. See https://psm-utils.readthedocs.io/en/stable/#supported-file-formats.
+ spectrum_id_pattern
+ Regular expression pattern to apply to spectrum titles before matching to
+ peptide file ``spec_id`` entries.
+ model
+ Model to use as reference for the ion types that are extracted from the observed spectra.
+ Default: "HCD", which results in the extraction of singly charged b- and y-ions.
+ ms2_tolerance
+ MS2 tolerance in Da for observed spectrum peak annotation. By default, 0.02 Da.
+ processes
+ Number of parallel processes for multiprocessing steps. By default, all available.
+
+ Returns
+ -------
+ features
+ :py:class:`pandas.DataFrame` with feature vectors and targets.
+
+ """
+ psm_list = read_psms(psms, filetype=psm_filetype)
+ spectrum_id_pattern = spectrum_id_pattern if spectrum_id_pattern else "(.*)"
+
+ with Encoder.from_psm_list(psm_list) as encoder:
+ ms2pip_parallelized = _Parallelized(
+ encoder=encoder,
+ model=model,
+ ms2_tolerance=ms2_tolerance,
+ processes=processes,
+ )
+ logger.info("Processing spectra and peptides...")
+ results = ms2pip_parallelized.process_spectra(
+ psm_list, spectrum_file, spectrum_id_pattern, vector_file=True
+ )
+
+ logger.info("Assembling training data in DataFrame...")
+ training_data = _assemble_training_data(results, model)
+
+ return training_data
+
+
+def annotate_spectra(
+ psms: Union[PSMList, str, Path],
+ spectrum_file: Union[str, Path],
+ psm_filetype: Optional[str] = None,
+ spectrum_id_pattern: Optional[str] = None,
+ model: Optional[str] = "HCD",
+ ms2_tolerance: float = 0.02,
+ processes: Optional[int] = None,
+):
+ """
+ Annotate observed spectra.\f
+
+ Parameters
+ ----------
+ psms
+ PSMList or path to PSM file that is supported by psm_utils.
+ spectrum_file
+ Path to spectrum file with target intensities.
+ psm_filetype
+ Filetype of the PSM file. By default, None. Should be one of the supported psm_utils
+ filetypes. See https://psm-utils.readthedocs.io/en/stable/#supported-file-formats.
+ spectrum_id_pattern
+ Regular expression pattern to apply to spectrum titles before matching to
+ peptide file ``spec_id`` entries.
+ model
+ Model to use as reference for the ion types that are extracted from the observed spectra.
+ Default: "HCD", which results in the extraction of singly charged b- and y-ions.
+ ms2_tolerance
+ MS2 tolerance in Da for observed spectrum peak annotation. By default, 0.02 Da.
+ processes
+ Number of parallel processes for multiprocessing steps. By default, all available.
+
+ Returns
+ -------
+ results: List[ProcessingResult]
+ List of ProcessingResult objects with theoretical m/z and observed intensity values.
+
+ """
+ psm_list = read_psms(psms, filetype=psm_filetype)
+ spectrum_id_pattern = spectrum_id_pattern if spectrum_id_pattern else "(.*)"
+
+ with Encoder.from_psm_list(psm_list) as encoder:
+ ms2pip_parallelized = _Parallelized(
+ encoder=encoder,
+ model=model,
+ ms2_tolerance=ms2_tolerance,
+ processes=processes,
+ )
+ logger.info("Processing spectra and peptides...")
+ results = ms2pip_parallelized.process_spectra(
+ psm_list, spectrum_file, spectrum_id_pattern, vector_file=False, annotations_only=True
+ )
+
+ return results
+
+
+def download_models(
+ models: Optional[List[str]] = None, model_dir: Optional[Union[str, Path]] = None
+):
+ """
+ Download all specified models to the specified directory.
+
+ Parameters
+ ----------
+ models
+ List of models to download. If not specified, all models will be downloaded.
+ model_dir
+ Directory where XGBoost model files are to be stored. Default: ``~/.ms2pip``.
+
+ """
+ model_dir = model_dir if model_dir else Path.home() / ".ms2pip"
+ model_dir = Path(model_dir).expanduser()
+ model_dir.mkdir(parents=True, exist_ok=True)
+
+ if not models:
+ models = list(MODELS.keys())
+
+ for model in models:
+ try:
+ if "xgb_model_files" in MODELS[model].keys():
+ continue
+ except KeyError:
+ raise exceptions.UnknownModelError(model)
+ logger.debug("Downloading %s model files", model)
+ validate_requested_xgb_model(
+ MODELS[model]["xgboost_model_files"],
+ MODELS[model]["model_hash"],
+ model_dir,
+ )
+
+
+class _Parallelized:
+ """Implementations of common multiprocessing functionality across MS²PIP usage modes."""
+
+ def __init__(
+ self,
+ encoder: Encoder = None,
+ model: Optional[str] = None,
+ model_dir: Optional[Union[str, Path]] = None,
+ ms2_tolerance: float = 0.02,
+ processes: Optional[int] = None,
+ ):
+ """
+ Implementations of common multiprocessing functionality across MS²PIP usage modes.
+
+ Parameters
+ ----------
+ encoding
+ Configured encoding class instance. Required if input peptides contain modifications.
+ model
+ Name of the model to use for predictions. Overrides configuration file.
+ model_dir
+ Custom directory for downloaded XGBoost model files. By default, `~/.ms2pip` is used.
+ ms2_tolerance
+ MS2 tolerance in Da for observed spectrum peak annotation. By default, 0.02 Da.
+ processes
+ Number of parallel processes for multiprocessing steps. By default, all available.
+
+ """
+ # Input parameters
+ self.encoder = encoder
+ self.model = model
+ self.model_dir = model_dir if model_dir else Path.home() / ".ms2pip"
+ self.ms2_tolerance = ms2_tolerance
+ self.processes = processes if processes else multiprocessing.cpu_count()
+
+ # Setup encoder if not configured
+ if not self.encoder:
+ self.encoder = Encoder()
+ self.encoder.write_encoder_files()
+
+ # Validate requested model
+ if self.model in MODELS.keys():
+ logger.debug("Using %s model", self.model)
+ if "xgboost_model_files" in MODELS[self.model].keys():
+ validate_requested_xgb_model(
+ MODELS[self.model]["xgboost_model_files"],
+ MODELS[self.model]["model_hash"],
+ self.model_dir,
+ )
+ else:
+ raise exceptions.UnknownModelError(self.model)
+
+ def _get_pool(self):
+ """Get multiprocessing pool."""
+ logger.debug(f"Starting workers (processes={self.processes})...")
+ if multiprocessing.current_process().daemon:
+ logger.warn(
+ "MS²PIP is running in a daemon process. Disabling multiprocessing as daemonic "
+ "processes cannot have children."
+ )
+ return multiprocessing.dummy.Pool(1)
+ elif self.processes == 1:
+ logger.debug("Using dummy multiprocessing pool.")
+ return multiprocessing.dummy.Pool(1)
+ else:
+ return multiprocessing.get_context("spawn").Pool(self.processes)
+
+ def _validate_output_formats(self, output_formats: List[str]) -> List[str]:
+ """Validate requested output formats."""
+ if not output_formats:
+ self.output_formats = ["csv"]
+ else:
+ for output_format in output_formats:
+ if output_format not in SUPPORTED_FORMATS:
+ raise exceptions.UnknownOutputFormatError(output_format)
+ self.output_formats = output_formats
+
+ def _execute_in_pool(self, psm_list: PSMList, func: Callable, args: tuple):
+ """Execute function in multiprocessing pool."""
+
+ def get_chunk_size(n_items, n_processes):
+ """Get optimal chunk size for multiprocessing."""
+ if n_items < 5000:
+ return n_items
+ else:
+ max_chunk_size = 50000
+ n_chunks = ceil(ceil(n_items / n_processes) / max_chunk_size) * n_processes
+ return ceil(n_items / n_chunks)
+
+ def to_chunks(_list, chunk_size):
+ """Split _list into chunks of size chunk_size."""
+
+ def _generate_chunks():
+ for i in range(0, len(_list), chunk_size):
+ yield _list[i : i + chunk_size]
+
+ _list = list(_list)
+ return list(_generate_chunks())
+
+ def _enumerated_psm_list_by_spectrum_id(psm_list, spectrum_ids_chunk):
+ selected_indices = np.flatnonzero(np.isin(psm_list["spectrum_id"], spectrum_ids_chunk))
+ return [(i, psm_list.psm_list[i]) for i in selected_indices]
+
+ with self._get_pool() as pool:
+ if not psm_list:
+ logger.warning("No PSMs to process.")
+ return []
+
+ # Split PSMList into chunks
+ if func == _process_spectra:
+ # Split by spectrum_id to keep PSMs for same spectrum together
+ spectrum_ids = set(psm_list["spectrum_id"])
+ chunk_size = get_chunk_size(len(spectrum_ids), pool._processes)
+ chunks = [
+ _enumerated_psm_list_by_spectrum_id(psm_list, spectrum_ids_chunk)
+ for spectrum_ids_chunk in to_chunks(spectrum_ids, chunk_size)
+ ]
+ else:
+ # Simple split by PSM
+ chunk_size = get_chunk_size(len(psm_list), pool._processes)
+ chunks = to_chunks(list(enumerate(psm_list)), chunk_size)
+
+ logger.debug(f"Processing {len(chunks)} chunk(s) of ~{chunk_size} entries each.")
+
+ # Add jobs to pool
+ mp_results = []
+ for psm_list_chunk in chunks:
+ mp_results.append(pool.apply_async(func, args=(psm_list_chunk, *args)))
+
+ # Gather results
+ # results = [
+ # r.get()
+ # for r in track(
+ # mp_results,
+ # disable=len(chunks) == 1,
+ # description="Processing chunks...",
+ # transient=True,
+ # show_speed=False,
+ # )
+ # ]
+ results = [r.get() for r in mp_results]
+
+ # Sort results by input order
+ results = list(
+ sorted(
+ itertools.chain.from_iterable(results),
+ key=lambda result: result.psm_index,
+ )
+ )
+
+ return results
+
+ def process_peptides(self, psm_list: PSMList) -> List[ProcessingResult]:
+ """Process peptides in parallel."""
+ results = self._execute_in_pool(
+ psm_list,
+ _process_peptides,
+ (self.encoder, self.model),
+ )
+ logger.debug(f"Gathered data for {len(results)} peptides.")
+
+ # Add XGBoost predictions if required
+ if "xgboost_model_files" in MODELS[self.model].keys():
+ results = self._add_xgboost_predictions(results)
+
+ return results
+
+ def process_spectra(
+ self,
+ psm_list: PSMList,
+ spectrum_file: Union[str, Path],
+ spectrum_id_pattern: str,
+ vector_file: bool = False,
+ annotations_only: bool = False,
+ ) -> List[ProcessingResult]:
+ """
+ Process PSMs and observed spectra in parallel
+
+ Parameters
+ ----------
+ psm_list
+ psm_utils.PSMList instance with PSMs to process
+ spectrum_file
+ Filename of spectrum file
+ spectrum_id_pattern
+ Regular expression pattern to apply to spectrum titles before matching to
+ peptide file entries
+ vector_file
+ If feature vectors should be extracted instead of predictions
+ annotations_only
+ If only peak annotations should be extracted from the spectrum file
+
+ """
+ # Validate runs and collections
+ if not len(psm_list.collections) == 1 or not len(psm_list.runs) == 1:
+ raise exceptions.InvalidInputError("PSMs should be for a single run and collection.")
+
+ args = (
+ spectrum_file,
+ vector_file,
+ self.encoder,
+ self.model,
+ self.ms2_tolerance,
+ spectrum_id_pattern,
+ annotations_only,
+ )
+ results = self._execute_in_pool(psm_list, _process_spectra, args)
+
+ # Validate number of results
+ if not results:
+ raise exceptions.NoMatchingSpectraFound(
+ "No spectra matching spectrum IDs from PSM list could be found in provided file."
+ )
+ logger.debug(f"Gathered data for {len(results)} PSMs.")
+
+ # Add XGBoost predictions if required
+ if (
+ not (vector_file or annotations_only)
+ and "xgboost_model_files" in MODELS[self.model].keys()
+ ):
+ results = self._add_xgboost_predictions(results)
+
+ return results
+
+ def _add_xgboost_predictions(self, results: List[ProcessingResult]) -> List[ProcessingResult]:
+ """
+ Add XGBoost predictions to results.
+
+ Notes
+ -----
+ This functions is applied after the parallel processing, as XGBoost implements its own
+ multiprocessing.
+ """
+
+ if "xgboost_model_files" not in MODELS[self.model].keys():
+ raise ValueError("XGBoost model files not found in MODELS dictionary.")
+
+ logger.debug("Converting feature vectors to XGBoost DMatrix...")
+ import xgboost as xgb
+
+ results_to_predict = [r for r in results if r.feature_vectors is not None]
+
+ if not results_to_predict:
+ return results
+
+ num_ions = [len(r.psm.peptidoform.parsed_sequence) - 1 for r in results_to_predict]
+ xgb_vector = xgb.DMatrix(np.vstack(list(r.feature_vectors for r in results_to_predict)))
+
+ predictions = get_predictions_xgb(
+ xgb_vector,
+ num_ions,
+ MODELS[self.model],
+ self.model_dir,
+ processes=self.processes,
+ )
+
+ logger.debug("Adding XGBoost predictions to results...")
+ for result, preds in zip(results_to_predict, predictions):
+ result.predicted_intensity = preds
+ result.feature_vectors = None
+
+ return results
+
+ # TODO IMPLEMENT
+ def write_predictions(
+ self, all_preds: pd.DataFrame, peptides: pd.DataFrame, output_filename: str
+ ):
+ raise NotImplementedError
+ spec_out = spectrum_output.SpectrumOutput(
+ all_preds,
+ peptides,
+ self.params["ms2pip"],
+ output_filename=output_filename,
+ )
+ spec_out.write_results(self.output_formats)
+
+
+def _process_peptidoform(
+ psm_index: int,
+ psm: PSM,
+ model: str,
+ encoder: Encoder,
+ ion_types: Optional[List[str]] = None,
+) -> ProcessingResult:
+ """
+ Process a single peptidoform from a PSM.
+
+ Get theoretical m/z and predicted intensities (from C model) or feature vectors (for XGBoost
+ model) for a single peptidoform from a PSM.
+
+ Notes
+ -----
+ - ``ms2pip_pyx.init()`` must be called before this function is called.
+ - Optionally, lowercase version of ``ion_types`` from the model configuration can be provided
+ to save computational time.
+
+ """
+ peptidoform = psm.peptidoform
+ if not ion_types:
+ ion_types = [it.lower() for it in MODELS[model]["ion_types"]]
+
+ enc_peptide = encoder.encode_peptide(peptidoform)
+ enc_peptidoform = encoder.encode_peptidoform(peptidoform)
+
+ # Get ion mzs and map to ion types
+ mz = ms2pip_pyx.get_mzs(enc_peptidoform, MODELS[model]["peaks_version"])
+ mz = {i: np.array(mz, dtype=np.float32) for i, mz in zip(ion_types, mz)}
+
+ # Get predictions from XGBoost models.
+ if "xgboost_model_files" in MODELS[model].keys():
+ predictions = None
+ feature_vectors = np.array(
+ ms2pip_pyx.get_vector(enc_peptide, enc_peptidoform, peptidoform.precursor_charge),
+ dtype=np.uint16,
+ )
+ # Or get predictions from C models.
+ else:
+ predictions = ms2pip_pyx.get_predictions(
+ enc_peptide,
+ enc_peptidoform,
+ peptidoform.precursor_charge,
+ MODELS[model]["id"],
+ MODELS[model]["peaks_version"],
+ 30.0, # TODO: Remove CE feature
+ )
+ predictions = {
+ i: np.array(p, dtype=np.float32).clip(min=np.log2(0.001)) # Clip negative intensities
+ for i, p in zip(ion_types, predictions)
+ }
+ feature_vectors = None
+
+ return ProcessingResult(
+ psm_index=psm_index,
+ psm=psm,
+ theoretical_mz=mz,
+ predicted_intensity=predictions,
+ observed_intensity=None,
+ feature_vectors=feature_vectors,
+ )
+
+
+def _process_peptides(
+ enumerated_psm_list: List[Tuple[int, PSM]],
+ encoder: Encoder,
+ model: str,
+) -> List[ProcessingResult]:
+ """
+ Predict spectrum for each entry in PeptideRecord DataFrame.
+
+ Parameters
+ ----------
+ enumerated_psm_list
+ List of tuples of (index, PSM) for each PSM in the input file.
+ encoder
+ Configured encoder to use for peptide and peptidoform encoding
+ model
+ Name of prediction model to be used
+
+ """
+ ms2pip_pyx.ms2pip_init(*encoder.encoder_files)
+ results = []
+ ion_types = [it.lower() for it in MODELS[model]["ion_types"]]
+
+ for psm_index, psm in enumerated_psm_list:
+ try:
+ result = _process_peptidoform(psm_index, psm, model, encoder, ion_types)
+ except (
+ exceptions.InvalidPeptidoformError,
+ exceptions.InvalidAminoAcidError,
+ ):
+ result = ProcessingResult(psm_index=psm_index, psm=psm)
+ results.append(result)
+
+ return results
+
+
+def _process_spectra(
+ enumerated_psm_list: List[Tuple[int, PSM]],
+ spec_file: str,
+ vector_file: bool,
+ encoder: Encoder,
+ model: str,
+ ms2_tolerance: float,
+ spectrum_id_pattern: str,
+ annotations_only: bool = False,
+) -> List[ProcessingResult, None]:
+ """
+ Perform requested tasks for each spectrum in spectrum file.
+
+ Parameters
+ ----------
+ enumerated_psm_list
+ List of tuples of (index, PSM) for each PSM in the input file.
+ spec_file
+ Filename of spectrum file
+ vector_file
+ If feature vectors should be extracted instead of predictions
+ encoder: Encoder
+ Configured encoder to use for peptide and peptidoform encoding
+ model
+ Name of prediction model to be used
+ ms2_tolerance
+ Fragmentation spectrum m/z error tolerance in Dalton
+ spectrum_id_pattern
+ Regular expression pattern to apply to spectrum titles before matching to
+ peptide file entries
+ annotations_only
+ If only peak annotations should be extracted from the spectrum file
+
+ """
+ ms2pip_pyx.ms2pip_init(*encoder.encoder_files)
+ results = []
+ ion_types = [it.lower() for it in MODELS[model]["ion_types"]]
+
+ try:
+ spectrum_id_regex = re.compile(spectrum_id_pattern)
+ except TypeError:
+ spectrum_id_regex = re.compile(r"(.*)")
+
+ # Restructure PeptideRecord entries as spec_id -> [(id, psm_1), (id, psm_2), ...]
+ psms_by_specid = defaultdict(list)
+ for psm_index, psm in enumerated_psm_list:
+ psms_by_specid[str(psm.spectrum_id)].append((psm_index, psm))
+
+ for spectrum in read_spectrum_file(spec_file):
+ # Match spectrum ID with provided regex, use first match group as new ID
+ match = spectrum_id_regex.search(spectrum.identifier)
+ try:
+ spectrum_id = match[1]
+ except (TypeError, IndexError):
+ raise exceptions.TitlePatternError(
+ f"Spectrum title pattern `{spectrum_id_pattern}` could not be matched to "
+ f"spectrum ID `{spectrum.identifier}`. "
+ " Are you sure that the regex contains a capturing group?"
+ )
+
+ if spectrum_id not in psms_by_specid:
+ continue
+
+ # Spectrum preprocessing:
+ # Remove reporter ions and precursor peak, normalize, transform
+ for label_type in ["iTRAQ", "TMT"]:
+ if label_type in model:
+ spectrum.remove_reporter_ions(label_type)
+ # spectrum.remove_precursor() # TODO: Decide to implement this or not
+ spectrum.tic_norm()
+ spectrum.log2_transform()
+
+ for psm_index, psm in psms_by_specid[spectrum_id]:
+ try:
+ enc_peptidoform = encoder.encode_peptidoform(psm.peptidoform)
+ except exceptions.InvalidAminoAcidError:
+ result = ProcessingResult(psm_index=psm_index, psm=psm)
+ results.append(result)
+ continue
+
+ targets = ms2pip_pyx.get_targets(
+ enc_peptidoform,
+ spectrum.mz.astype(np.float32),
+ spectrum.intensity.astype(np.float32),
+ float(ms2_tolerance),
+ MODELS[model]["peaks_version"],
+ )
+ targets = {i: np.array(t, dtype=np.float32) for i, t in zip(ion_types, targets)}
+
+ if not psm.peptidoform.precursor_charge:
+ psm.peptidoform.precursor_charge = spectrum.precursor_charge
+
+ if vector_file:
+ enc_peptide = encoder.encode_peptide(psm.peptidoform)
+ feature_vectors = np.array(
+ ms2pip_pyx.get_vector(
+ enc_peptide, enc_peptidoform, psm.peptidoform.precursor_charge
+ ),
+ dtype=np.uint16,
+ )
+ result = ProcessingResult(
+ psm_index=psm_index,
+ psm=psm,
+ theoretical_mz=None,
+ predicted_intensity=None,
+ observed_intensity=targets,
+ correlation=None,
+ feature_vectors=feature_vectors,
+ )
+
+ elif annotations_only:
+ # Only return mz and targets
+ mz = ms2pip_pyx.get_mzs(enc_peptidoform, MODELS[model]["peaks_version"])
+ mz = {i: np.array(mz, dtype=np.float32) for i, mz in zip(ion_types, mz)}
+
+ result = ProcessingResult(
+ psm_index=psm_index,
+ psm=psm,
+ theoretical_mz=mz,
+ predicted_intensity=None,
+ observed_intensity=targets,
+ correlation=None,
+ feature_vectors=None,
+ )
+
+ else:
+ # Predict with C model or get feature vectors for XGBoost
+ try:
+ result = _process_peptidoform(psm_index, psm, model, encoder, ion_types)
+ except (
+ exceptions.InvalidPeptidoformError,
+ exceptions.InvalidAminoAcidError,
+ ):
+ result = ProcessingResult(psm_index=psm_index, psm=psm)
+ else:
+ result.observed_intensity = targets
+
+ results.append(result)
+
+ return results
+
+
+def _assemble_training_data(results: List[ProcessingResult], model: str) -> pd.DataFrame:
+ """Assemble training data from results list to single pandas DataFrame."""
+ # Get ion types
+ ion_types = [it.lower() for it in MODELS[model]["ion_types"]]
+
+ # Assemble feature vectors, PSM indices, and targets
+ training_data = pd.DataFrame(
+ np.vstack([r.feature_vectors for r in results if r.feature_vectors is not None]),
+ columns=get_feature_names(),
+ )
+ training_data["psm_index"] = np.concatenate(
+ [
+ np.repeat(r.psm_index, r.feature_vectors.shape[0])
+ for r in results
+ if r.feature_vectors is not None
+ ]
+ )
+ for ion_type in ion_types:
+ if ion_type in ["a", "b", "b2", "c"]:
+ training_data[f"target_{ion_type}"] = np.concatenate(
+ [r.observed_intensity[ion_type] for r in results if r.feature_vectors is not None]
+ )
+ elif ion_type in ["x", "y", "y2", "z"]:
+ training_data[f"target_{ion_type}"] = np.concatenate(
+ [
+ r.observed_intensity[ion_type][::-1]
+ for r in results
+ if r.feature_vectors is not None
+ ]
+ )
+
+ # Reorder columns
+ training_data = training_data[
+ ["psm_index"] + get_feature_names() + [f"target_{it}" for it in ion_types]
+ ]
+
+ return training_data
+
+
+def _into_batches(iterable: Iterable[Any], batch_size: int) -> Generator[List[Any], None, None]:
+ """Accumulate iterator elements into batches of a given size."""
+ batch = []
+ for item in iterable:
+ batch.append(item)
+ if len(batch) == batch_size:
+ yield batch
+ batch = []
+ if batch:
+ yield batch
diff --git a/ms2pip/correlation.py b/ms2pip/correlation.py
new file mode 100644
index 00000000..fabd70bd
--- /dev/null
+++ b/ms2pip/correlation.py
@@ -0,0 +1,26 @@
+import numpy as np
+
+
+def ms2pip_pearson(true, pred):
+ """Calculate Pearson correlation, including tic-normalization and log-transformation."""
+
+ def tic_norm(x):
+ return x / np.sum(x)
+
+ def log_transform(x):
+ return np.log2(x + 0.001)
+
+ corr = np.corrcoef(log_transform(tic_norm(true)), log_transform(tic_norm(pred)))[0][1]
+ return corr
+
+
+def spectral_angle(true, pred, epsilon=1e-7):
+ """
+ Calculate square root normalized spectral angle.
+
+ See https://doi.org/10.1074/mcp.O113.036475.
+ """
+ pred_norm = pred / max(np.linalg.norm(pred), epsilon)
+ true_norm = true / max(np.linalg.norm(true), epsilon)
+ spectral_angle = 1 - (2 * np.arccos(np.dot(pred_norm, true_norm)) / np.pi)
+ return spectral_angle
diff --git a/ms2pip/cython_modules/__init__.py b/ms2pip/cython_modules/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/ms2pip/cython_modules/ms2pip_features_c_catboost.c b/ms2pip/cython_modules/ms2pip_features_c_catboost.c
deleted file mode 100644
index 4f6c98c2..00000000
--- a/ms2pip/cython_modules/ms2pip_features_c_catboost.c
+++ /dev/null
@@ -1,132 +0,0 @@
-// Compute feature vectors from peptide
-unsigned int* get_v_ms2pip_catboost(int peplen, unsigned short* peptide, unsigned short* modpeptide, int charge)
- {
- int i,j,k;
-
- int fnum = 1; //first value in v is its length
-
- for (i=0; i < 19; i++) {
- count_n[i] = 0;
- count_c[i] = 0;
- }
-
- //I need this for Omega
- //important for sptms!!
- peptide_buf[0] = peptide[0];
- for (i=0; i < peplen; i++) {
- if (peptide[i+1] > 18) {
- peptide_buf[i+1] = sptm_mapper[peptide[i+1]];
- }
- else {
- peptide_buf[i+1] = peptide[i+1];
- }
- count_c[peptide_buf[i+1]]++;
- }
-
- int num_shared = 0;
-
- shared_features[num_shared++] = peplen;
- shared_features[num_shared++] = charge;
-
- shared_features[num_shared] = 0;
- if (charge == 1) {
- shared_features[num_shared] = 1;
- }
- num_shared++;
- shared_features[num_shared] = 0;
- if (charge == 2) {
- shared_features[num_shared] =1;
- }
- num_shared++;
- shared_features[num_shared] = 0;
- if (charge == 3) {
- shared_features[num_shared] =1;
- }
- num_shared++;
- shared_features[num_shared] = 0;
- if (charge == 4) {
- shared_features[num_shared] =1;
- }
- num_shared++;
- shared_features[num_shared] = 0;
- if (charge >= 5) {
- shared_features[num_shared]=1;
- }
- num_shared++;
-
- for (j=0; j < num_props; j++) {
- for (i=0; i < peplen; i++) {
- props_buffer[i] = props[j][peptide_buf[i+1]];
- }
- qsort(props_buffer,peplen,sizeof(unsigned int),cmpfunc);
- shared_features[num_shared++] = props_buffer[0];
- shared_features[num_shared++] = props_buffer[(int)(0.25*(peplen-1))];
- shared_features[num_shared++] = props_buffer[(int)(0.5*(peplen-1))];
- shared_features[num_shared++] = props_buffer[(int)(0.75*(peplen-1))];
- shared_features[num_shared++] = props_buffer[peplen-1];
- }
-
- for (i=0; i < peplen-1; i++) {
- v[fnum++] = peptide_buf[1];
- v[fnum++] = peptide_buf[peplen];
- v[fnum++] = peptide_buf[i];
- v[fnum++] = peptide_buf[i+1];
- for (j=0; j 18) {
- peptide_buf[i+1] = sptm_mapper[peptide[i+1]];
- }
- else {
- peptide_buf[i+1] = peptide[i+1];
- }
- count_c[peptide_buf[i+1]]++;
- }
-
- int num_shared = 0;
-
- shared_features[num_shared++] = peplen;
- shared_features[num_shared++] = charge;
-
- shared_features[num_shared] = 0;
- if (charge == 1) {
- shared_features[num_shared] = 1;
- }
- num_shared++;
- shared_features[num_shared] = 0;
- if (charge == 2) {
- shared_features[num_shared] =1;
- }
- num_shared++;
- shared_features[num_shared] = 0;
- if (charge == 3) {
- shared_features[num_shared] =1;
- }
- num_shared++;
- shared_features[num_shared] = 0;
- if (charge == 4) {
- shared_features[num_shared] =1;
- }
- num_shared++;
- shared_features[num_shared] = 0;
- if (charge >= 5) {
- shared_features[num_shared] =1;
- }
- num_shared++;
-
- for (j=0; j < num_props; j++) {
- for (i=0; i < peplen; i++) {
- props_buffer[i] = props[j][peptide_buf[i+1]];
- }
- qsort(props_buffer,peplen,sizeof(unsigned int),cmpfunc);
- shared_features[num_shared++] = props_buffer[0];
- shared_features[num_shared++] = props_buffer[(int)(0.25*(peplen-1))];
- shared_features[num_shared++] = props_buffer[(int)(0.5*(peplen-1))];
- shared_features[num_shared++] = props_buffer[(int)(0.75*(peplen-1))];
- shared_features[num_shared++] = props_buffer[peplen-1];
- }
-
- for (i=0; i < peplen-1; i++) {
- for (j=0; j 18) {
- peptide[i+1] = sptm_mapper[peptide[i+1]];
- }
- buf3[peptide[i+1]]++;
- sum_aG_tot += aG[peptide[i+1]];
- sum_wikiG_tot += wikiG[peptide[i+1]];
- }
-
- unsigned int total_bas = 0;
- unsigned int total_heli = 0;
- unsigned int total_hydro = 0;
- unsigned int total_pI = 0;
- unsigned int max_bas = 0;
- unsigned int max_heli = 0;
- unsigned int max_hydro = 0;
- unsigned int max_pI = 0;
- unsigned int min_bas = 999;
- unsigned int min_heli = 999;
- unsigned int min_hydro = 999;
- unsigned int min_pI = 999;
-
- mz = 0.;
- for (i=0; i < peplen; i++) {
- mz += amino_F[modpeptide[i+1]];
- total_bas += bas[peptide[i+1]];
- total_heli += heli[peptide[i+1]];
- total_hydro += hydro[peptide[i+1]];
- total_pI += pI[peptide[i+1]];
- if (max_bas < bas[peptide[i+1]]) {
- max_bas = bas[peptide[i+1]];
- }
- if (max_heli < heli[peptide[i+1]]) {
- max_heli = heli[peptide[i+1]];
- }
- if (max_hydro < hydro[peptide[i+1]]) {
- max_hydro = hydro[peptide[i+1]];
- }
- if (max_pI < pI[peptide[i+1]]) {
- max_pI = pI[peptide[i+1]];
- }
- if (min_bas > bas[peptide[i+1]]) {
- min_bas = bas[peptide[i+1]];
- }
- if (min_heli > heli[peptide[i+1]]) {
- min_heli = heli[peptide[i+1]];
- }
- if (min_hydro > hydro[peptide[i+1]]) {
- min_hydro = hydro[peptide[i+1]];
- }
- if (min_pI > pI[peptide[i+1]]) {
- min_pI = pI[peptide[i+1]];
- }
- }
-
- int mean_mz = (int) ((float)mz/peplen);
- int mean_bas = (int) ((float)total_bas/peplen);
- int mean_heli = (int) ((float)total_heli/peplen);
- int mean_hydro = (int) ((float)total_hydro/peplen);
- int mean_pI = (int) ((float)total_pI/peplen);
-
- float mzb = 0.;
- int sum_bas = 0;
- int sum_heli = 0;
- int sum_hydro = 0;
- int sum_pI = 0;
-
- for (i=0; i < peplen-1; i++) {
- max_bas_b = 0;
- max_heli_b = 0;
- max_hydro_b = 0;
- max_pI_b = 0;
- max_bas_y = 0;
- max_heli_y = 0;
- max_hydro_y = 0;
- max_pI_y = 0;
- min_bas_b = 999;
- min_heli_b = 999;
- min_hydro_b = 999;
- min_pI_b = 999;
- min_bas_y = 999;
- min_heli_y = 999;
- min_hydro_y = 999;
- min_pI_y = 999;
-
- buf2[peptide[i+1]]++;
- sum_aG += aG[peptide[i+1]];
- sum_wikiG += wikiG[peptide[i+1]];
- for (j=0; j < 19; j++) {
- v[fnum++] = (int) 100*(((float) buf2[j])/(i+1));
- }
- v[fnum++] = sum_aG;
- v[fnum++] = sum_wikiG;
- v[fnum++] = (int) 100*(((float) sum_aG)/(i+1));
- v[fnum++] = (int) 100*(((float) sum_wikiG)/(i+1));
-
-
- buf3[peptide[i+1]]--;
- for (j=0; j < 19; j++) {
- v[fnum++] = (int) 100*(((float) buf3[j])/(peplen-i-1));
- }
- v[fnum++] = sum_aG_tot-sum_aG;
- v[fnum++] = sum_wikiG_tot-sum_wikiG;
- v[fnum++] = (int) 100*(((float) (sum_aG_tot-sum_aG)/(peplen-i-1)));
- v[fnum++] = (int) 100*(((float) (sum_wikiG_tot-sum_wikiG)/(peplen-i-1)));
-
- v[fnum++] = (int) mz;
- v[fnum++] = peplen;
- v[fnum++] = i;
- v[fnum++] = (int) 100*(float)i/peplen;
- v[fnum++] = sum_aG_tot;
- v[fnum++] = sum_wikiG_tot;
- v[fnum++] = total_bas;
- v[fnum++] = total_heli;
- v[fnum++] = total_hydro;
- v[fnum++] = total_pI;
- v[fnum++] = mean_mz;
- v[fnum++] = mean_bas;
- v[fnum++] = mean_heli;
- v[fnum++] = mean_hydro;
- v[fnum++] = mean_pI;
- v[fnum++] = max_bas;
- v[fnum++] = max_heli;
- v[fnum++] = max_hydro;
- v[fnum++] = max_pI;
- v[fnum++] = min_bas;
- v[fnum++] = min_heli;
- v[fnum++] = min_hydro;
- v[fnum++] = min_pI;
-
- for (j=0; j<=i; j++) {
- if (bas[peptide[j+1]] > max_bas_b) {
- max_bas_b = bas[peptide[j+1]];
- }
- if (heli[peptide[j+1]] > max_heli_b) {
- max_heli_b = heli[peptide[j+1]];
- }
- if (hydro[peptide[j+1]] > max_hydro_b) {
- max_hydro_b = hydro[peptide[j+1]];
- }
- if (pI[peptide[j+1]] > max_pI_b) {
- max_pI_b = pI[peptide[j+1]];
- }
- if (bas[peptide[j+1]] < min_bas_b) {
- min_bas_b = bas[peptide[j+1]];
- }
- if (heli[peptide[j+1]] < min_heli_b) {
- min_heli_b = heli[peptide[j+1]];
- }
- if (hydro[peptide[j+1]] < min_hydro_b) {
- min_hydro_b = hydro[peptide[j+1]];
- }
- if (pI[peptide[j+1]] < min_pI_b) {
- min_pI_b = pI[peptide[j+1]];
- }
- }
- for (j=i+1; j max_bas_y) {
- max_bas_y = bas[peptide[j+1]];
- }
- if (heli[peptide[j+1]] > max_heli_y) {
- max_heli_y = heli[peptide[j+1]];
- }
- if (hydro[peptide[j+1]] > max_hydro_y) {
- max_hydro_y = hydro[peptide[j+1]];
- }
- if (pI[peptide[j+1]] > max_pI_y) {
- max_pI_y = pI[peptide[j+1]];
- }
- if (bas[peptide[j+1]] < min_bas_y) {
- min_bas_y = bas[peptide[j+1]];
- }
- if (heli[peptide[j+1]] < min_heli_y) {
- min_heli_y = heli[peptide[j+1]];
- }
- if (hydro[peptide[j+1]] < min_hydro_y) {
- min_hydro_y = hydro[peptide[j+1]];
- }
- if (pI[peptide[j+1]] < min_pI_y) {
- min_pI_y = pI[peptide[j+1]];
- }
- }
-
- v[fnum++] = max_bas_b;
- v[fnum++] = max_heli_b;
- v[fnum++] = max_hydro_b;
- v[fnum++] = max_pI_b;
- v[fnum++] = min_bas_b;
- v[fnum++] = min_heli_b;
- v[fnum++] = min_hydro_b;
- v[fnum++] = min_pI_b;
-
- v[fnum++] = max_bas_y;
- v[fnum++] = max_heli_y;
- v[fnum++] = max_hydro_y;
- v[fnum++] = max_pI_y;
- v[fnum++] = min_bas_y;
- v[fnum++] = min_heli_y;
- v[fnum++] = min_hydro_y;
- v[fnum++] = min_pI_y;
-
- mzb += amino_F[modpeptide[i+1]];
- v[fnum++] = (int) mzb;
- v[fnum++] = (int) (mz - mzb);
- v[fnum++] = (int) (mzb/(i+1));
- v[fnum++] = (int) ((mz-mzb)/(peplen-1-i));
- sum_bas += bas[peptide[i+1]];
- v[fnum++] = sum_bas;
- v[fnum++] = total_bas-sum_bas;
- v[fnum] = v[fnum-1] - v[fnum-2] + 100000;
- fnum++;
- //v[fnum++] = (int) ((float)sum_bas/(i+1));
- //v[fnum++] = (int) ((float)(total_bas-sum_bas)/(peplen-1-i));
- sum_heli += heli[peptide[i+1]];
- v[fnum++] = sum_heli;
- v[fnum++] = total_heli-sum_heli;
- v[fnum] = v[fnum-1] - v[fnum-2] + 100000;
- fnum++;
- //v[fnum++] = (int) ((float)sum_heli/(i+1));
- //v[fnum++] = (int) ((float)(total_heli-sum_heli)/(peplen-1-i));
- sum_hydro += hydro[peptide[i+1]];
- v[fnum++] = sum_hydro;
- v[fnum++] = total_hydro-sum_hydro;
- v[fnum] = v[fnum-1] - v[fnum-2] + 100000;
- fnum++;
- //v[fnum++] = (int) ((float)sum_hydro/(i+1));
- //v[fnum++] = (int) ((float)(total_hydro-sum_hydro)/(peplen-1-i));
- sum_pI += pI[peptide[i+1]];
- v[fnum++] = sum_pI;
- v[fnum++] = total_pI-sum_pI;
- v[fnum] = v[fnum-1] - v[fnum-2] + 100000;
- fnum++;
- //v[fnum++] = (int) ((float)sum_pI/(i+1));
- //v[fnum++] = (int) ((float)(total_pI-sum_pI)/(peplen-1-i));
-
- v[fnum++] = bas[peptide[i+1]]+bas[peptide[i+2]];
- v[fnum++] = heli[peptide[i+1]]+heli[peptide[i+2]];
- v[fnum++] = hydro[peptide[i+1]]+hydro[peptide[i+2]];
- v[fnum++] = pI[peptide[i+1]]+pI[peptide[i+2]];
- v[fnum++] = bas[peptide[i+1]]*bas[peptide[i+2]];
- v[fnum++] = heli[peptide[i+1]]*heli[peptide[i+2]];
- v[fnum++] = hydro[peptide[i+1]]*hydro[peptide[i+2]];
- v[fnum++] = pI[peptide[i+1]]*pI[peptide[i+2]];
-
- v[fnum++] = bas[peptide[i+1]]-bas[peptide[i+2]]+1000;
- v[fnum++] = heli[peptide[i+1]]-heli[peptide[i+2]]+1000;
- v[fnum++] = hydro[peptide[i+1]]-hydro[peptide[i+2]]+1000;
- v[fnum++] = pI[peptide[i+1]]-pI[peptide[i+2]]+1000;
- v[fnum++] = bas[peptide[i+2]]-bas[peptide[i+1]]+1000;
- v[fnum++] = heli[peptide[i+2]]-heli[peptide[i+1]]+1000;
- v[fnum++] = hydro[peptide[i+2]]-hydro[peptide[i+1]]+1000;
- v[fnum++] = pI[peptide[i+2]]-pI[peptide[i+1]]+1000;
-
- v[fnum++] = bas[peptide[i+1]]+bas[peptide[1]];
- v[fnum++] = heli[peptide[i+1]]+heli[peptide[1]];
- v[fnum++] = hydro[peptide[i+1]]+hydro[peptide[1]];
- v[fnum++] = pI[peptide[i+1]]+pI[peptide[1]];
-
- v[fnum++] = bas[peptide[peplen]]+bas[peptide[i+2]];
- v[fnum++] = heli[peptide[peplen]]+heli[peptide[i+2]];
- v[fnum++] = hydro[peptide[peplen]]+hydro[peptide[i+2]];
- v[fnum++] = pI[peptide[peplen]]+pI[peptide[i+2]];
-
- int pos = 1;
- v[fnum++] = amino_F[modpeptide[pos]];
- v[fnum++] = bas[peptide[pos]];
- v[fnum++] = heli[peptide[pos]];
- v[fnum++] = hydro[peptide[pos]];
- v[fnum++] = pI[peptide[pos]];
- v[fnum++] = wikiG[peptide[pos]];
- v[fnum] = 0;
- if (peptide[pos] == 11) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 2) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 3) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 8) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 13) {
- v[fnum] = 1;
- }
- fnum++;
-
- pos = 2;
- v[fnum++] = amino_F[modpeptide[pos]];
- v[fnum++] = bas[peptide[pos]];
- v[fnum++] = heli[peptide[pos]];
- v[fnum++] = hydro[peptide[pos]];
- v[fnum++] = pI[peptide[pos]];
- v[fnum++] = wikiG[peptide[pos]];
- v[fnum] = 0;
- if (peptide[pos] == 11) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 2) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 3) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 8) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 13) {
- v[fnum] = 1;
- }
- fnum++;
-
- pos = peplen-1;
- v[fnum++] = amino_F[modpeptide[pos]];
- v[fnum++] = bas[peptide[pos]];
- v[fnum++] = heli[peptide[pos]];
- v[fnum++] = hydro[peptide[pos]];
- v[fnum++] = pI[peptide[pos]];
- v[fnum++] = wikiG[peptide[pos]];
- v[fnum] = 0;
- if (peptide[pos] == 11) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 2) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 3) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 8) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 13) {
- v[fnum] = 1;
- }
- fnum++;
-
- pos = peplen;
- v[fnum++] = amino_F[modpeptide[pos]];
- v[fnum++] = bas[peptide[pos]];
- v[fnum++] = heli[peptide[pos]];
- v[fnum++] = hydro[peptide[pos]];
- v[fnum++] = pI[peptide[pos]];
- v[fnum++] = wikiG[peptide[pos]];
- v[fnum] = 0;
- if (peptide[pos] == 11) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 2) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 3) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 8) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[pos] == 13) {
- v[fnum] = 1;
- }
- fnum++;
-
- v[fnum++] = wikiG[peptide[i+1]];
- v[fnum] = 0;
- if (peptide[i+1] == 11) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[i+1] == 2) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[i+1] == 3) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[i+1] == 8) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[i+1] == 13) {
- v[fnum] = 1;
- }
- fnum++;
-
- v[fnum++] = wikiG[peptide[i+2]];
- v[fnum] = 0;
- if (peptide[i+2] == 11) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[i+2] == 2) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[i+2] == 3) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[i+2] == 8) {
- v[fnum] = 1;
- }
- fnum++;
- v[fnum] = 0;
- if (peptide[i+2] == 13) {
- v[fnum] = 1;
- }
- fnum++;
-
- v[fnum++] = bas[peptide[i+1]];
- if (i==0) {
- v[fnum++] = bas[peptide[i+1]];
- }
- else {
- v[fnum++] = bas[peptide[i]];
- }
- v[fnum++] = bas[peptide[i+2]];
- if (i==(peplen-2)) {
- v[fnum++] = bas[peptide[i+2]];
- }
- else {
- v[fnum++] = bas[peptide[i+3]];
- }
-
- v[fnum++] = heli[peptide[i+1]];
- if (i==0) {
- v[fnum++] = heli[peptide[i+1]];
- }
- else {
- v[fnum++] = heli[peptide[i]];
- }
- v[fnum++] = heli[peptide[i+2]];
- if (i==(peplen-2)) {
- v[fnum++] = heli[peptide[i+2]];
- }
- else {
- v[fnum++] = heli[peptide[i+3]];
- }
-
- v[fnum++] = hydro[peptide[i+1]];
- if (i==0) {
- v[fnum++] = hydro[peptide[i+1]];
- }
- else {
- v[fnum++] = hydro[peptide[i]];
- }
- v[fnum++] = hydro[peptide[i+2]];
- if (i==(peplen-2)) {
- v[fnum++] = hydro[peptide[i+2]];
- }
- else {
- v[fnum++] = hydro[peptide[i+3]];
- }
-
- v[fnum++] = pI[peptide[i+1]];
- if (i==0) {
- v[fnum++] = pI[peptide[i+1]];
- }
- else {
- v[fnum++] = pI[peptide[i]];
- }
- v[fnum++] = pI[peptide[i+2]];
- if (i==(peplen-2)) {
- v[fnum++] = pI[peptide[i+2]];
- }
- else {
- v[fnum++] = pI[peptide[i+3]];
- }
-
- v[fnum++] = amino_F[modpeptide[i+1]];
- if (i==0) {
- v[fnum++] = amino_F[modpeptide[i+1]];
- }
- else {
- v[fnum++] = amino_F[modpeptide[i]];
- }
- v[fnum++] = amino_F[modpeptide[i+2]];
- if (i==(peplen-2)) {
- v[fnum++] = amino_F[modpeptide[i+2]];
- }
- else {
- v[fnum++] = amino_F[modpeptide[i+3]];
- }
-
- v[fnum++] = charge;
-
- }
- v[0] = fnum-1;
- return v;
-}
diff --git a/ms2pip/exceptions.py b/ms2pip/exceptions.py
index ce90a794..05e801e6 100644
--- a/ms2pip/exceptions.py
+++ b/ms2pip/exceptions.py
@@ -2,19 +2,15 @@ class MS2PIPError(Exception):
pass
-class InvalidPeptideError(MS2PIPError):
+class InvalidPeptidoformError(MS2PIPError):
pass
-class UnknownModificationError(ValueError):
+class InvalidInputError(MS2PIPError):
pass
-class InvalidPEPRECError(Exception):
- pass
-
-
-class NoValidPeptideSequencesError(Exception):
+class UnresolvableModificationError(MS2PIPError):
pass
@@ -22,43 +18,25 @@ class UnknownOutputFormatError(ValueError):
pass
-class UnknownFragmentationMethodError(ValueError):
- pass
-
-
-class MissingConfigurationError(Exception):
- pass
-
-
-class FragmentationModelRequiredError(Exception):
- pass
-
-
-class InvalidModificationFormattingError(Exception):
- pass
-
-
-class InvalidAminoAcidError(Exception):
+class UnknownModelError(ValueError):
pass
-class UnsupportedSpectrumFiletypeError(Exception):
+class InvalidAminoAcidError(MS2PIPError):
pass
-class InvalidSpectrumError(Exception):
- pass
-
-
-class EmptySpectrumError(InvalidSpectrumError):
+class UnsupportedSpectrumFiletypeError(MS2PIPError):
pass
class NoMatchingSpectraFound(MS2PIPError):
pass
+
class TitlePatternError(MS2PIPError):
pass
+
class InvalidXGBoostModelError(MS2PIPError):
pass
diff --git a/ms2pip/feature_names.py b/ms2pip/feature_names.py
deleted file mode 100644
index c05e565f..00000000
--- a/ms2pip/feature_names.py
+++ /dev/null
@@ -1,208 +0,0 @@
-def get_feature_names():
- """
- feature names for the fixed peptide length feature vectors
- """
- aminos = ["A", "C", "D", "E", "F", "G", "H", "I", "K",
- "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y"]
- names = []
- for a in aminos:
- names.append("Ib_" + a)
- names.append("sumIbaG")
- names.append("meanIbwikiG")
- names.append("sumIywaG")
- names.append("meanIywikiG")
-
- names += ["pmz", "peplen", "ionnumber", "ionnumber_rel"]
-
- for c in ["aG", "wikiG", "mz", "bas", "heli", "hydro", "pI"]:
- names.append("sum_" + c)
-
- for c in ["mz", "bas", "heli", "hydro", "pI"]:
- names.append("mean_" + c)
-
- for c in ["max_{}", "min_{}", "max{}_b", "min{}_b", "max{}_y", "min{}_y"]:
- for b in ["bas", "heli", "hydro", "pI"]:
- names.append(c.format(b))
-
- names.append("mz_ion")
- names.append("mz_ion_other")
- names.append("mean_mz_ion")
- names.append("mean_mz_ion_other")
-
- for c in ["bas", "heli", "hydro", "pI"]:
- names.append("{}_ion".format(c))
- names.append("{}_ion_other".format(c))
- names.append("{}_ion_minus_ion_other".format(c))
- #names.append("mean_{}_ion".format(c))
- #names.append("mean_{}_ion_other".format(c))
-
- for c in ["plus_cleave{}", "times_cleave{}", "minus1_cleave{}", "minus2_cleave{}", "bsum{}", "ysum{}"]:
- for b in ["bas", "heli", "hydro", "pI"]:
- names.append(c.format(b))
-
- for pos in ["0", "1", "-2", "-1"]:
- for c in ["mz", "bas", "heli", "hydro", "pI", "wikiG", "P", "D", "E", "K", "R"]:
- names.append("loc_" + pos + "_" + c)
-
- for pos in ["i", "i+1"]:
- for c in ["wikiG", "P", "D", "E", "K", "R"]:
- names.append("loc_" + pos + "_" + c)
-
- for c in ["bas", "heli", "hydro", "pI", "mz"]:
- for pos in ["i", "i-1", "i+1", "i+2"]:
- names.append("loc_" + pos + "_" + c)
-
- names.append("charge")
-
- return names
-
-
-def get_feature_names_catboost():
- num_props = 4
- names = ["amino_first", "amino_last", "amino_lcleave", "amino_rcleave", "peplen", "charge"]
- for t in range(5):
- names.append("charge"+str(t))
- for t in range(num_props):
- names.append("qmin_%i"%t)
- names.append("q1_%i"%t)
- names.append("q2_%i"%t)
- names.append("q3_%i"%t)
- names.append("qmax_%i"%t)
- names.append("len_n")
- names.append("len_c")
-
- for a in ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'M',
- 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']:
- names.append("I_n_%s"%a)
- names.append("I_c_%s"%a)
-
- for t in range(num_props):
- for pos in ["p0", "pend", "pi-1", "pi", "pi+1", "pi+2"]:
- names.append("prop_%i_%s"%(t, pos))
- names.append("sum_%i_n"%t)
- names.append("q0_%i_n"%t)
- names.append("q1_%i_n"%t)
- names.append("q2_%i_n"%t)
- names.append("q3_%i_n"%t)
- names.append("q4_%i_n"%t)
- names.append("sum_%i_c"%t)
- names.append("q0_%i_c"%t)
- names.append("q1_%i_c"%t)
- names.append("q2_%i_c"%t)
- names.append("q3_%i_c"%t)
- names.append("q4_%i_c"%t)
-
- return names
-
-
-def get_feature_names_new():
- num_props = 4
- names = ["peplen", "charge"]
- for t in range(5):
- names.append("charge"+str(t))
- for t in range(num_props):
- names.append("qmin_%i"%t)
- names.append("q1_%i"%t)
- names.append("q2_%i"%t)
- names.append("q3_%i"%t)
- names.append("qmax_%i"%t)
- names.append("len_n")
- names.append("len_c")
-
- for a in ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'M',
- 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']:
- names.append("I_n_%s"%a)
- names.append("I_c_%s"%a)
-
- for t in range(num_props):
- for pos in ["p0", "pend", "pi-1", "pi", "pi+1", "pi+2"]:
- names.append("prop_%i_%s"%(t, pos))
- names.append("sum_%i_n"%t)
- names.append("q0_%i_n"%t)
- names.append("q1_%i_n"%t)
- names.append("q2_%i_n"%t)
- names.append("q3_%i_n"%t)
- names.append("q4_%i_n"%t)
- names.append("sum_%i_c"%t)
- names.append("q0_%i_c"%t)
- names.append("q1_%i_c"%t)
- names.append("q2_%i_c"%t)
- names.append("q3_%i_c"%t)
- names.append("q4_%i_c"%t)
-
- return names
-
-
-def get_feature_names_small(ionnumber):
- """
- feature names for the fixed peptide length feature vectors
- """
- names = []
- names += ["pmz", "peplen"]
-
- for c in ["bas", "heli", "hydro", "pI"]:
- names.append("sum_" + c)
-
- for c in ["mz", "bas", "heli", "hydro", "pI"]:
- names.append("mean_" + c)
-
- names.append("mz_ion")
- names.append("mz_ion_other")
- names.append("mean_mz_ion")
- names.append("mean_mz_ion_other")
-
- for c in ["bas", "heli", "hydro", "pI"]:
- names.append("{}_ion".format(c))
- names.append("{}_ion_other".format(c))
-
- names.append("endK")
- names.append("endR")
- names.append("nextP")
- names.append("nextK")
- names.append("nextR")
-
- for c in ["bas", "heli", "hydro", "pI", "mz"]:
- for pos in ["i", "i-1", "i+1", "i+2"]:
- names.append("loc_" + pos + "_" + c)
-
- names.append("charge")
-
- for i in range(ionnumber):
- for c in ["bas", "heli", "hydro", "pI", "mz"]:
- names.append("P_%i_%s"%(i, c))
- names.append("P_%i_P"%i)
- names.append("P_%i_K"%i)
- names.append("P_%i_R"%i)
-
- return names
-
-
-def get_feature_names_chem(peplen):
- """
- feature names for the fixed peptide length feature vectors
- """
-
- names = []
- names += ["pmz", "peplen", "ionnumber", "ionnumber_rel", "mean_mz"]
-
- for c in ["mean_{}", "max_{}", "min_{}", "max{}_b", "min{}_b", "max{}_y", "min{}_y"]:
- for b in ["bas", "heli", "hydro", "pI"]:
- names.append(c.format(b))
-
- for c in ["mz", "bas", "heli", "hydro", "pI"]:
- names.append("{}_ion".format(c))
- names.append("{}_ion_other".format(c))
- names.append("mean_{}_ion".format(c))
- names.append("mean_{}_ion_other".format(c))
-
- for c in ["plus_cleave{}", "times_cleave{}", "minus1_cleave{}", "minus2_cleave{}", "bsum{}", "ysum{}"]:
- for b in ["bas", "heli", "hydro", "pI"]:
- names.append(c.format(b))
-
- for i in range(peplen):
- for c in ["mz", "bas", "heli", "hydro", "pI"]:
- names.append("fix_" + c + "_" + str(i))
-
- names.append("charge")
-
- return names
diff --git a/ms2pip/match_spectra.py b/ms2pip/match_spectra.py
deleted file mode 100644
index ee71507b..00000000
--- a/ms2pip/match_spectra.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import bisect
-import logging
-from operator import itemgetter
-
-import numpy as np
-import pyteomics.mgf
-
-logger = logging.getLogger("ms2pip.match_spectra")
-
-
-def get_intense_mzs(mzs, intensity, n=3):
- return [x[0] for x in sorted(zip(mzs, intensity), key=itemgetter(1), reverse=True)[:n]]
-
-
-def match_mzs(mzs, predicted, max_error=0.02):
- current = 0
- for pred in predicted:
- current = bisect.bisect_right(mzs, pred - max_error, lo=current)
- if current >= len(mzs) or mzs[current] >= pred + max_error:
- return False
- return current < len(mzs)
-
-
-def get_predicted_peaks(pepids, mzs, intensities):
- # NOTE: we need to concatenate the predictions for b and y ions
- return dict(zip(pepids,
- (sorted(get_intense_mzs(np.concatenate(_mzs, axis=None),
- np.concatenate(_intensities, axis=None)))
- for _mzs, _intensities in zip(mzs, intensities))))
-
-
-class MatchSpectra:
- DATA_COLS = ['spec_id', 'peptide', 'modifications', 'charge']
-
- def __init__(self, peprec, mods, pepids, predicted_mzs, predicted_intensities):
- """
- Initialise spectra matcher
-
- Parameters:
- -----------
- peprec: pandas.DataFrame
- PEPREC formatted input peptides
- mods: ms2pip.peptides.Modifications
- Modifications used in PEPREC file
- pepids: Iterable[str]
- Iterable of spec_id's from peprec input ordered matching the predictions
- predicted_mzs: Iterable[list[numpy.array[float32]]]
- Iterable of predicted m/z values
- predicted_mzs: Iterable[list[numpy.array[float32]]]
- Iterable of predicted intensities
- """
- self.peprec = peprec
- self.mods = mods
- self.predictions = get_predicted_peaks(pepids, predicted_mzs, predicted_intensities)
- self._generate_peptide_list()
-
- def _generate_peptide_list(self):
- peptides = [
- (
- spec_id,
- self.mods.calc_precursor_mz(peptide,
- modifications,
- charge)[1],
- self.predictions[spec_id]
- ) for spec_id, peptide, modifications, charge in self.peprec[self.DATA_COLS].values
- ]
- peptides.sort(key=itemgetter(1))
- self.peptides = peptides
-
- def match_mgfs(self, mgf_files, max_error=0.02):
- """
- Match predicted spectra to spectra in given MGF files.
-
- Paramters:
- ----------
- mgf_files: list[str]
- List of filenames of MGF files
- max_error: float, optional
- Maximum error for masses
- """
- logger.info("match predicted spectra to spectra in mgf files (%s)", mgf_files)
- precursors = [x[1] for x in self.peptides]
-
- for mgf_file in mgf_files:
- logger.debug("open %s", mgf_file)
- with pyteomics.mgf.read(mgf_file, use_header=False, convert_arrays=0, read_charges=False) as reader:
- for spectrum in reader:
- if 'pepmass' not in spectrum['params']:
- continue
- pepmass = spectrum['params']['pepmass'][0]
-
- # compare all peptides with a similar precursor m/z
- i = bisect.bisect_right(precursors, pepmass - max_error)
- while i < len(precursors) and precursors[i] < pepmass + max_error:
- spec_id, _, pred_peaks = self.peptides[i]
- if match_mzs(sorted(spectrum['m/z array']), pred_peaks, max_error=max_error):
- yield spec_id, mgf_file, spectrum
- i += 1
-
- def match_sqldb(self, sqldb_uri="postgresql:///ms2pip", max_error=0.02):
- """
- Match predicted spectra to given database of observed spectra.
-
- Paramters:
- ----------
- sqldb_uri: str
- URI of database to connect to
- max_error: float, optional
- Maximum error for masses
- """
- from ms2pip.sqldb import tables
- from sqlalchemy import select
-
- engine = tables.create_engine(sqldb_uri)
- precursors = np.fromiter((x[1] for x in self.peptides), dtype=np.float, count=len(self.peprec))
- gaps = np.where(np.diff(precursors) >= max_error)[0]
-
- with engine.connect() as connection:
- start = 0
- for end in gaps:
- for spec in connection.execute(
- select([tables.spec, tables.specfile.c.filename])
- .select_from(tables.spec.join(tables.specfile)).where(
- tables.spec.c.pepmass > self.peptides[start][1] - max_error
- ).where(
- tables.spec.c.pepmass < self.peptides[end][1] + max_error
- ).order_by(tables.spec.c.pepmass)):
- for spec_id, mz, pred_peaks in self.peptides[start:end+1]:
- if mz > spec.pepmass + max_error:
- break
- if mz < spec.pepmass - max_error:
- start += 1
- continue
- if match_mzs(spec.mzs, pred_peaks, max_error=max_error):
- yield (spec_id,
- spec.filename,
- {'params': {'title': spec.spec_id},
- 'm/z array': spec.mzs})
- start = end + 1
diff --git a/ms2pip/models/TMT/model_20190107_TMT_train_B.c b/ms2pip/models/TMT/model_20190107_TMT_train_B.c
deleted file mode 100644
index 73de2bb7..00000000
--- a/ms2pip/models/TMT/model_20190107_TMT_train_B.c
+++ /dev/null
@@ -1,127434 +0,0 @@
-float score_TMT_B(unsigned int* v){
-float s = 0.;
- if (v[73]<587){
- if (v[132]<34){
- if (v[108]<46){
- if (v[91]<331){
- if (v[90]<15){
- if (v[32]<1){
- if (v[125]<3){
- if (v[109]<245){
- if (v[27]<2){
- if (v[44]<3){
- s = s -3.7343173;
-}
- else{
- s = s -4.38771868;
-}}
- else{
- if (v[15]<70){
- s = s -3.39362311;
-}
- else{
- s = s -3.72146535;
-}}}
- else{
- if (v[83]<132){
- if (v[4]<1){
- s = s -3.85345602;
-}
- else{
- s = s -3.44496655;
-}}
- else{
- if (v[127]<96){
- s = s -3.75670052;
-}
- else{
- s = s -4.3447032;
-}}}}
- else{
- if (v[71]<37){
- if (v[27]<3){
- if (v[115]<595){
- s = s -4.83487129;
-}
- else{
- s = s -5.40396214;
-}}
- else{
- if (v[1]<3){
- s = s -4.75013781;
-}
- else{
- s = s -4.22858667;
-}}}
- else{
- if (v[52]<1){
- if (v[1]<3){
- s = s -4.56086159;
-}
- else{
- s = s -5.35565519;
-}}
- else{
- if (v[4]<1){
- s = s -4.30988979;
-}
- else{
- s = s -4.01390362;
-}}}}}
- else{
- if (v[71]<55){
- if (v[71]<18){
- if (v[28]<20){
- if (v[109]<71){
- s = s -4.38684893;
-}
- else{
- s = s -4.00658178;
-}}
- else{
- if (v[91]<195){
- s = s -4.96738195;
-}
- else{
- s = s -4.18507767;
-}}}
- else{
- if (v[109]<302){
- if (v[75]<44){
- s = s -3.694906;
-}
- else{
- s = s -3.98676014;
-}}
- else{
- if (v[89]<57){
- s = s -4.42580032;
-}
- else{
- s = s -3.764503;
-}}}}
- else{
- if (v[71]<60){
- if (v[1]<3){
- if (v[109]<317){
- s = s -2.68509197;
-}
- else{
- s = s -3.32894254;
-}}
- else{
- if (v[27]<2){
- s = s -3.45577741;
-}
- else{
- s = s -2.97373056;
-}}}
- else{
- if (v[109]<284){
- if (v[47]<1){
- s = s -3.47346067;
-}
- else{
- s = s -3.8825841;
-}}
- else{
- if (v[97]<524){
- s = s -4.11642027;
-}
- else{
- s = s -3.5820303;
-}}}}}}
- else{
- if (v[126]<59){
- if (v[107]<22){
- if (v[77]<18){
- if (v[133]<521){
- if (v[72]<170){
- s = s -5.65875912;
-}
- else{
- s = s -4.91042805;
-}}
- else{
- if (v[1]<3){
- s = s -5.56593084;
-}
- else{
- s = s -6.33073092;
-}}}
- else{
- if (v[72]<170){
- if (v[126]<3){
- s = s -5.6865325;
-}
- else{
- s = s -5.25191164;
-}}
- else{
- if (v[73]<61){
- s = s -5.13068342;
-}
- else{
- s = s -4.47670746;
-}}}}
- else{
- if (v[126]<3){
- if (v[125]<3){
- if (v[73]<77){
- s = s -5.09941244;
-}
- else{
- s = s -4.72607756;
-}}
- else{
- if (v[47]<1){
- s = s -5.33500671;
-}
- else{
- s = s -5.73050833;
-}}}
- else{
- if (v[125]<3){
- if (v[27]<2){
- s = s -4.86425829;
-}
- else{
- s = s -4.25483751;
-}}
- else{
- if (v[72]<170){
- s = s -4.95781422;
-}
- else{
- s = s -4.41564417;
-}}}}}
- else{
- if (v[125]<3){
- if (v[27]<2){
- if (v[115]<530){
- if (v[97]<408){
- s = s -5.13400984;
-}
- else{
- s = s -5.45452213;
-}}
- else{
- if (v[15]<68){
- s = s -5.54641294;
-}
- else{
- s = s -5.97650051;
-}}}
- else{
- if (v[72]<268){
- if (v[0]<7){
- s = s -4.02418327;
-}
- else{
- s = s -4.63938951;
-}}
- else{
- if (v[47]<1){
- s = s -5.01880884;
-}
- else{
- s = s -5.67839336;
-}}}}
- else{
- if (v[97]<616){
- if (v[82]<201){
- if (v[0]<7){
- s = s -5.03965521;
-}
- else{
- s = s -5.79479074;
-}}
- else{
- if (v[27]<6){
- s = s -5.92178249;
-}
- else{
- s = s -6.42938471;
-}}}
- else{
- if (v[107]<22){
- if (v[77]<55){
- s = s -6.89976883;
-}
- else{
- s = s -6.47873116;
-}}
- else{
- if (v[72]<268){
- s = s -5.97170353;
-}
- else{
- s = s -6.42122316;
-}}}}}}}
- else{
- if (v[137]<59){
- if (v[108]<8){
- if (v[109]<432){
- if (v[126]<59){
- if (v[115]<390){
- if (v[11]<277){
- s = s -4.65540028;
-}
- else{
- s = s -5.04115248;
-}}
- else{
- if (v[4]<1){
- s = s -4.61557245;
-}
- else{
- s = s -4.20934582;
-}}}
- else{
- if (v[125]<3){
- s = s -4.94735765;
-}
- else{
- if (v[1]<4){
- s = s -5.86918497;
-}
- else{
- s = s -6.30951023;
-}}}}
- else{
- if (v[115]<245){
- if (v[3]<1){
- if (v[86]<36){
- s = s -5.54932213;
-}
- else{
- s = s -6.33325148;
-}}
- else{
- if (v[32]<1){
- s = s -5.39923954;
-}
- else{
- s = s -4.73865271;
-}}}
- else{
- if (v[109]<502){
- if (v[126]<59){
- s = s -4.95002222;
-}
- else{
- s = s -5.90195131;
-}}
- else{
- if (v[28]<13){
- s = s -5.73277521;
-}
- else{
- s = s -5.20925951;
-}}}}}
- else{
- if (v[91]<431){
- if (v[126]<3){
- if (v[73]<429){
- if (v[107]<22){
- s = s -5.92457676;
-}
- else{
- s = s -5.50277615;
-}}
- else{
- if (v[11]<277){
- s = s -5.77821779;
-}
- else{
- s = s -6.10235977;
-}}}
- else{
- if (v[11]<277){
- if (v[4]<1){
- s = s -5.30678368;
-}
- else{
- s = s -4.96597767;
-}}
- else{
- if (v[73]<434){
- s = s -5.27454138;
-}
- else{
- s = s -5.5970602;
-}}}}
- else{
- if (v[91]<510){
- if (v[11]<277){
- if (v[73]<435){
- s = s -5.29479885;
-}
- else{
- s = s -5.70045948;
-}}
- else{
- if (v[73]<470){
- s = s -5.71736431;
-}
- else{
- s = s -6.09310532;
-}}}
- else{
- if (v[11]<277){
- if (v[73]<478){
- s = s -5.72102213;
-}
- else{
- s = s -6.1367445;
-}}
- else{
- if (v[109]<455){
- s = s -6.12380695;
-}
- else{
- s = s -6.51646328;
-}}}}}}
- else{
- if (v[72]<277){
- if (v[1]<3){
- if (v[125]<14){
- if (v[71]<95){
- if (v[109]<345){
- s = s -4.65316057;
-}
- else{
- s = s -5.10980797;
-}}
- else{
- if (v[12]<27){
- s = s -5.04489946;
-}
- else{
- s = s -5.48890734;
-}}}
- else{
- if (v[91]<424){
- if (v[47]<1){
- s = s -5.57764673;
-}
- else{
- s = s -6.01470232;
-}}
- else{
- if (v[89]<68){
- s = s -6.23307228;
-}
- else{
- s = s -5.88478661;
-}}}}
- else{
- if (v[79]<321){
- if (v[115]<106){
- if (v[133]<103){
- s = s -7.29046726;
-}
- else{
- s = s -7.01045561;
-}}
- else{
- if (v[73]<331){
- s = s -4.42748785;
-}
- else{
- s = s -6.29190493;
-}}}
- else{
- if (v[83]<277){
- if (v[91]<419){
- s = s -5.88094711;
-}
- else{
- s = s -6.39541864;
-}}
- else{
- if (v[133]<176){
- s = s -7.09190989;
-}
- else{
- s = s -6.50310421;
-}}}}}
- else{
- if (v[125]<14){
- if (v[71]<95){
- if (v[109]<432){
- if (v[73]<400){
- s = s -5.10211754;
-}
- else{
- s = s -5.6313386;
-}}
- else{
- s = s -6.18483973;
-}}
- else{
- if (v[1]<2){
- s = s -3.6603477;
-}
- else{
- if (v[73]<508){
- s = s -6.02445745;
-}
- else{
- s = s -6.46627665;
-}}}}
- else{
- if (v[27]<8){
- if (v[73]<456){
- if (v[124]<31){
- s = s -6.66503716;
-}
- else{
- s = s -6.36452866;
-}}
- else{
- if (v[1]<2){
- s = s -5.05381298;
-}
- else{
- s = s -6.84341908;
-}}}
- else{
- if (v[1]<2){
- s = s -5.27844381;
-}
- else{
- if (v[73]<436){
- s = s -6.86995268;
-}
- else{
- s = s -7.15697861;
-}}}}}}}}
- else{
- if (v[91]<356){
- if (v[89]<36){
- if (v[109]<37){
- if (v[3]<1){
- if (v[133]<294){
- if (v[137]<59){
- if (v[0]<9){
- s = s -3.48258305;
-}
- else{
- s = s -4.16017675;
-}}
- else{
- if (v[83]<277){
- s = s -4.75620937;
-}
- else{
- s = s -3.81255293;
-}}}
- else{
- if (v[97]<679){
- if (v[104]<17){
- s = s -4.82375097;
-}
- else{
- s = s -4.49323845;
-}}
- else{
- if (v[32]<1){
- s = s -5.12080717;
-}
- else{
- s = s -4.72295046;
-}}}}
- else{
- if (v[32]<1){
- if (v[48]<1){
- if (v[0]<7){
- s = s -3.57537842;
-}
- else{
- s = s -4.55848694;
-}}
- else{
- if (v[90]<70){
- s = s -4.25286961;
-}
- else{
- s = s -3.99823689;
-}}}
- else{
- if (v[72]<49){
- if (v[72]<37){
- s = s -4.1747694;
-}
- else{
- s = s -3.81609559;
-}}
- else{
- if (v[17]<19){
- s = s -3.46987534;
-}
- else{
- s = s -3.82588196;
-}}}}}
- else{
- if (v[89]<32){
- if (v[91]<239){
- if (v[56]<1){
- if (v[72]<49){
- s = s -4.37780428;
-}
- else{
- s = s -4.10840988;
-}}
- else{
- if (v[1]<3){
- s = s -4.98582792;
-}
- else{
- s = s -4.60242176;
-}}}
- else{
- if (v[137]<59){
- if (v[4]<1){
- s = s -4.48569393;
-}
- else{
- s = s -4.11767244;
-}}
- else{
- if (v[133]<180){
- s = s -4.66492605;
-}
- else{
- s = s -5.08442402;
-}}}}
- else{
- if (v[32]<1){
- if (v[0]<7){
- if (v[11]<201){
- s = s -3.16107941;
-}
- else{
- s = s -2.69133234;
-}}
- else{
- if (v[109]<272){
- s = s -3.93360901;
-}
- else{
- s = s -4.23929501;
-}}}
- else{
- if (v[97]<1164){
- if (v[72]<37){
- s = s -3.79775667;
-}
- else{
- s = s -3.35670066;
-}}
- else{
- if (v[32]<2){
- s = s -3.96038461;
-}
- else{
- s = s -4.64602518;
-}}}}}}
- else{
- if (v[32]<1){
- if (v[133]<541){
- if (v[0]<7){
- if (v[83]<277){
- if (v[71]<19){
- s = s -4.38617754;
-}
- else{
- s = s -3.79546237;
-}}
- else{
- if (v[136]<59){
- s = s -3.23293853;
-}
- else{
- s = s -3.73399854;
-}}}
- else{
- if (v[3]<1){
- if (v[133]<328){
- s = s -4.18762493;
-}
- else{
- s = s -4.6544528;
-}}
- else{
- if (v[90]<71){
- s = s -4.77165318;
-}
- else{
- s = s -4.57471228;
-}}}}
- else{
- if (v[73]<111){
- if (v[1]<3){
- if (v[48]<1){
- s = s -4.86604977;
-}
- else{
- s = s -4.26792574;
-}}
- else{
- if (v[73]<19){
- s = s -6.10430193;
-}
- else{
- s = s -5.33569956;
-}}}
- else{
- if (v[48]<1){
- if (v[115]<1072){
- s = s -4.84546614;
-}
- else{
- s = s -5.32930422;
-}}
- else{
- if (v[1]<3){
- s = s -4.15466881;
-}
- else{
- s = s -4.67195034;
-}}}}}
- else{
- if (v[72]<49){
- if (v[89]<68){
- if (v[71]<96){
- if (v[77]<18){
- s = s -5.26788902;
-}
- else{
- s = s -4.80246067;
-}}
- else{
- if (v[47]<1){
- s = s -4.37954283;
-}
- else{
- s = s -4.87419176;
-}}}
- else{
- if (v[1]<3){
- if (v[72]<37){
- s = s -4.33285999;
-}
- else{
- s = s -3.99552989;
-}}
- else{
- if (v[91]<140){
- s = s -4.72149611;
-}
- else{
- s = s -4.29057598;
-}}}}
- else{
- if (v[1]<3){
- if (v[47]<1){
- if (v[74]<37){
- s = s -3.92613077;
-}
- else{
- s = s -3.63627601;
-}}
- else{
- if (v[52]<1){
- s = s -4.49679184;
-}
- else{
- s = s -4.06544304;
-}}}
- else{
- if (v[73]<109){
- if (v[109]<69){
- s = s -4.90300655;
-}
- else{
- s = s -4.39721632;
-}}
- else{
- if (v[115]<1021){
- s = s -3.98509407;
-}
- else{
- s = s -4.36095238;
-}}}}}}}
- else{
- if (v[109]<422){
- if (v[83]<163){
- if (v[73]<443){
- if (v[4]<1){
- if (v[108]<78){
- if (v[107]<22){
- s = s -5.19428682;
-}
- else{
- s = s -4.86325407;
-}}
- else{
- if (v[12]<27){
- s = s -4.55053854;
-}
- else{
- s = s -4.84444904;
-}}}
- else{
- if (v[108]<78){
- if (v[56]<1){
- s = s -4.64693403;
-}
- else{
- s = s -5.4373455;
-}}
- else{
- if (v[11]<201){
- s = s -3.97316456;
-}
- else{
- s = s -4.44825315;
-}}}}
- else{
- if (v[125]<14){
- if (v[3]<1){
- if (v[11]<201){
- s = s -4.09914541;
-}
- else{
- s = s -4.50671053;
-}}
- else{
- if (v[71]<95){
- s = s -4.57959366;
-}
- else{
- s = s -4.85807657;
-}}}
- else{
- if (v[11]<277){
- if (v[4]<1){
- s = s -5.03133297;
-}
- else{
- s = s -4.65473795;
-}}
- else{
- if (v[108]<61){
- s = s -5.4955101;
-}
- else{
- s = s -5.12002277;
-}}}}}
- else{
- if (v[1]<3){
- if (v[125]<3){
- if (v[31]<1){
- if (v[109]<273){
- s = s -3.96336627;
-}
- else{
- s = s -4.60347986;
-}}
- else{
- s = s -3.84290409;
-}}
- else{
- if (v[73]<391){
- if (v[119]<78){
- s = s -5.14274502;
-}
- else{
- s = s -4.87689543;
-}}
- else{
- if (v[115]<154){
- s = s -5.36465454;
-}
- else{
- s = s -5.07040119;
-}}}}
- else{
- if (v[28]<4){
- if (v[125]<3){
- s = s -5.88758993;
-}
- else{
- s = s -6.84665537;
-}}
- else{
- if (v[83]<277){
- if (v[1]<4){
- s = s -5.21235991;
-}
- else{
- s = s -5.96271133;
-}}
- else{
- if (v[133]<176){
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- s = s + 0.0055241962;
-}
- else{
- s = s + 0.448287278;
-}}
- else{
- if (v[10]<124){
- s = s -0.054088816;
-}
- else{
- s = s -0.407105744;
-}}}
- else{
- if (v[43]<2){
- if (v[82]<71){
- s = s -0.154772267;
-}
- else{
- s = s + 0.162191182;
-}}
- else{
- s = s + 0.869767725;
-}}}
- else{
- if (v[40]<1){
- if (v[19]<19){
- s = s + 1.07815313;
-}
- else{
- if (v[119]<89){
- s = s + 0.350749433;
-}
- else{
- s = s -0.100416481;
-}}}
- else{
- if (v[76]<132){
- if (v[50]<2){
- s = s + 0.00102515204;
-}
- else{
- s = s + 0.528088987;
-}}
- else{
- if (v[115]<428){
- s = s -0.110315412;
-}
- else{
- s = s + 0.699226797;
-}}}}}}
- else{
- if (v[18]<8){
- if (v[97]<547){
- if (v[65]<1){
- if (v[91]<360){
- if (v[133]<428){
- s = s + 0.0129911629;
-}
- else{
- s = s + 0.0666799173;
-}}
- else{
- if (v[118]<15){
- s = s + 0.620753407;
-}
- else{
- s = s + 0.15890272;
-}}}
- else{
- if (v[14]<43){
- if (v[115]<133){
- s = s -0.583287716;
-}
- else{
- s = s + 0.309309363;
-}}
- else{
- if (v[79]<1362){
- s = s + 0.112087958;
-}
- else{
- s = s -0.271112204;
-}}}}
- else{
- if (v[115]<517){
- if (v[112]<46){
- if (v[7]<37){
- s = s -0.000472651212;
-}
- else{
- s = s -0.141391173;
-}}
- else{
- if (v[97]<553){
- s = s -0.489185542;
-}
- else{
- s = s -0.104881883;
-}}}
- else{
- if (v[19]<38){
- if (v[107]<100){
- s = s + 0.0173019301;
-}
- else{
- s = s -0.271753341;
-}}
- else{
- if (v[24]<32){
- s = s -0.122037806;
-}
- else{
- s = s + 0.180675611;
-}}}}}
- else{
- if (v[92]<40){
- if (v[133]<397){
- if (v[4]<1){
- if (v[0]<18){
- s = s -0.00444520731;
-}
- else{
- s = s + 0.12420968;
-}}
- else{
- if (v[109]<330){
- s = s + 0.200535774;
-}
- else{
- s = s -0.0305082966;
-}}}
- else{
- if (v[78]<98){
- if (v[115]<563){
- s = s + 0.0907565504;
-}
- else{
- s = s -0.213858217;
-}}
- else{
- if (v[64]<1){
- s = s + 0.0439515114;
-}
- else{
- s = s -0.0619546138;
-}}}}
- else{
- if (v[62]<2){
- if (v[73]<178){
- if (v[72]<55){
- s = s + 0.0428828225;
-}
- else{
- s = s + 0.161640793;
-}}
- else{
- if (v[128]<29){
- s = s + 0.165997341;
-}
- else{
- s = s + 0.425404787;
-}}}
- else{
- if (v[79]<1488){
- if (v[38]<1){
- s = s + 0.0242718756;
-}
- else{
- s = s + 0.280814826;
-}}
- else{
- if (v[85]<55){
- s = s -0.280409098;
-}
- else{
- s = s -0.0137540614;
-}}}}}}}
- else{
- if (v[127]<190){
- if (v[45]<1){
- if (v[132]<34){
- if (v[56]<2){
- if (v[56]<1){
- if (v[19]<31){
- s = s -0.0173844378;
-}
- else{
- s = s + 0.0266053863;
-}}
- else{
- if (v[8]<60){
- s = s + 0.0626873448;
-}
- else{
- s = s -0.0204391275;
-}}}
- else{
- if (v[19]<46){
- if (v[46]<1){
- s = s -0.0980461016;
-}
- else{
- s = s + 0.465902895;
-}}
- else{
- s = s -1.42793787;
-}}}
- else{
- if (v[71]<103){
- if (v[1]<4){
- if (v[11]<277){
- s = s -0.184271991;
-}
- else{
- s = s -0.0701336861;
-}}
- else{
- if (v[78]<173){
- s = s + 0.0903395563;
-}
- else{
- s = s -0.0690537617;
-}}}
- else{
- if (v[70]<60){
- if (v[133]<1055){
- s = s -0.03810912;
-}
- else{
- s = s + 0.146541968;
-}}
- else{
- if (v[76]<103){
- s = s + 0.051464133;
-}
- else{
- s = s -0.00634365855;
-}}}}}
- else{
- if (v[89]<68){
- if (v[18]<15){
- if (v[113]<8){
- if (v[133]<495){
- s = s -0.214914486;
-}
- else{
- s = s + 0.0505353846;
-}}
- else{
- if (v[109]<73){
- s = s + 0.387698293;
-}
- else{
- s = s + 0.136101261;
-}}}
- else{
- if (v[79]<2503){
- if (v[53]<1){
- s = s -0.140171364;
-}
- else{
- s = s + 0.216749489;
-}}
- else{
- if (v[48]<3){
- s = s + 0.139634684;
-}
- else{
- s = s -0.768389404;
-}}}}
- else{
- if (v[123]<25){
- if (v[24]<32){
- if (v[68]<268){
- s = s + 0.0363458507;
-}
- else{
- s = s -0.693486392;
-}}
- else{
- s = s + 0.430714875;
-}}
- else{
- if (v[70]<55){
- if (v[58]<4){
- s = s + 0.152963489;
-}
- else{
- s = s -0.542585611;
-}}
- else{
- if (v[118]<22){
- s = s + 0.619847357;
-}
- else{
- s = s + 0.258758038;
-}}}}}}
- else{
- if (v[84]<277){
- if (v[67]<277){
- if (v[105]<2){
- if (v[127]<322){
- if (v[81]<71){
- s = s -0.0806052461;
-}
- else{
- s = s -0.29979679;
-}}
- else{
- if (v[82]<103){
- s = s + 0.0179668572;
-}
- else{
- s = s + 0.279648334;
-}}}
- else{
- if (v[79]<3092){
- if (v[101]<48){
- s = s + 0.259478301;
-}
- else{
- s = s + 0.0214686207;
-}}
- else{
- if (v[78]<132){
- s = s -0.486880809;
-}
- else{
- s = s + 0.00327319885;
-}}}}
- else{
- if (v[77]<71){
- if (v[44]<2){
- s = s -0.232198387;
-}
- else{
- if (v[57]<2){
- s = s -0.912413955;
-}
- else{
- s = s -0.41039741;
-}}}
- else{
- if (v[41]<1){
- if (v[72]<44){
- s = s -0.0246678758;
-}
- else{
- s = s -0.207011387;
-}}
- else{
- if (v[74]<44){
- s = s + 0.032304693;
-}
- else{
- s = s + 0.47504741;
-}}}}}
- else{
- if (v[73]<85){
- s = s -0.707894385;
-}
- else{
- if (v[92]<33){
- if (v[67]<18){
- if (v[89]<71){
- s = s + 0.0749788955;
-}
- else{
- s = s + 0.268393129;
-}}
- else{
- if (v[76]<55){
- s = s + 0.0742999166;
-}
- else{
- s = s + 0.0147850392;
-}}}
- else{
- if (v[52]<8){
- if (v[114]<76){
- s = s -0.120369934;
-}
- else{
- s = s + 0.141440973;
-}}
- else{
- if (v[22]<3){
- s = s -0.0207736008;
-}
- else{
- s = s -0.819059014;
-}}}}}}}}
- else{
- if (v[127]<206){
- if (v[73]<503){
- if (v[15]<70){
- if (v[89]<27){
- if (v[98]<27){
- if (v[56]<1){
- if (v[133]<340){
- s = s -0.0219881497;
-}
- else{
- s = s -0.155120641;
-}}
- else{
- if (v[23]<29){
- s = s + 0.378967404;
-}
- else{
- s = s -0.0327585675;
-}}}
- else{
- if (v[83]<98){
- if (v[91]<126){
- s = s + 0.0988747552;
-}
- else{
- s = s + 0.758825004;
-}}
- else{
- if (v[7]<44){
- s = s + 0.00375849917;
-}
- else{
- s = s + 0.437961757;
-}}}}
- else{
- if (v[56]<1){
- if (v[1]<3){
- if (v[72]<100){
- s = s + 0.109892979;
-}
- else{
- s = s -0.592629433;
-}}
- else{
- if (v[109]<64){
- s = s + 1.18657494;
-}
- else{
- s = s + 0.169670388;
-}}}
- else{
- s = s -0.414903015;
-}}}
- else{
- if (v[79]<911){
- if (v[44]<2){
- s = s + 0.132286787;
-}
- else{
- s = s + 0.873428702;
-}}
- else{
- if (v[133]<402){
- s = s -0.176285118;
-}
- else{
- if (v[27]<5){
- s = s + 0.100838028;
-}
- else{
- s = s + 0.420962185;
-}}}}}
- else{
- if (v[80]<60){
- if (v[71]<106){
- if (v[85]<70){
- if (v[116]<15){
- if (v[117]<8){
- s = s + 0.0460256599;
-}
- else{
- s = s -0.215698093;
-}}
- else{
- if (v[72]<44){
- s = s -0.181077823;
-}
- else{
- s = s + 0.384910077;
-}}}
- else{
- if (v[109]<172){
- if (v[81]<60){
- s = s -0.332684487;
-}
- else{
- s = s -1.18722439;
-}}
- else{
- if (v[130]<32){
- s = s + 0.237278983;
-}
- else{
- s = s -0.365094543;
-}}}}
- else{
- if (v[8]<44){
- if (v[60]<2){
- if (v[36]<1){
- s = s -0.0459446311;
-}
- else{
- s = s -0.304350495;
-}}
- else{
- if (v[103]<38){
- s = s -0.0878154859;
-}
- else{
- s = s + 0.390936017;
-}}}
- else{
- if (v[100]<68){
- if (v[73]<518){
- s = s -0.179868773;
-}
- else{
- s = s -0.0138712907;
-}}
- else{
- if (v[103]<24){
- s = s + 0.0394046046;
-}
- else{
- s = s -0.308631003;
-}}}}}
- else{
- if (v[76]<103){
- if (v[98]<15){
- s = s -0.42361787;
-}
- else{
- s = s -0.069480069;
-}}
- else{
- if (v[57]<1){
- if (v[124]<16){
- s = s + 0.410222203;
-}
- else{
- if (v[107]<53){
- s = s -0.0715129897;
-}
- else{
- s = s + 0.343385369;
-}}}
- else{
- if (v[71]<103){
- s = s + 0.919110537;
-}
- else{
- s = s + 0.307224125;
-}}}}}}
- else{
- if (v[89]<27){
- if (v[99]<27){
- if (v[73]<505){
- s = s + 0.313605905;
-}
- else{
- if (v[127]<282){
- if (v[115]<951){
- if (v[80]<18){
- s = s -0.0202547405;
-}
- else{
- s = s -0.132472768;
-}}
- else{
- if (v[29]<1){
- s = s -0.491660774;
-}
- else{
- s = s -0.259935945;
-}}}
- else{
- if (v[127]<290){
- if (v[103]<76){
- s = s + 0.383646548;
-}
- else{
- s = s -0.0615581982;
-}}
- else{
- if (v[1]<3){
- s = s -0.102345921;
-}
- else{
- s = s -0.00983508583;
-}}}}}
- else{
- if (v[73]<570){
- if (v[102]<71){
- if (v[119]<46){
- s = s + 0.559167087;
-}
- else{
- s = s + 0.00221455889;
-}}
- else{
- if (v[36]<2){
- if (v[109]<155){
- s = s -0.185172349;
-}
- else{
- s = s -0.03461954;
-}}
- else{
- if (v[88]<64){
- s = s -0.244732678;
-}
- else{
- s = s -0.52745676;
-}}}}
- else{
- if (v[106]<24){
- if (v[77]<71){
- if (v[69]<55){
- s = s + 0.125634626;
-}
- else{
- s = s + 0.48423776;
-}}
- else{
- if (v[67]<277){
- s = s + 0.0428994521;
-}
- else{
- s = s -0.0147653604;
-}}}
- else{
- if (v[13]<39){
- if (v[47]<1){
- s = s -0.00409591943;
-}
- else{
- s = s + 0.0416744985;
-}}
- else{
- if (v[73]<869){
- s = s + 0.151156709;
-}
- else{
- s = s + 0.0501840413;
-}}}}}}
- else{
- if (v[128]<32){
- if (v[109]<176){
- if (v[109]<77){
- if (v[73]<517){
- s = s + 0.394939601;
-}
- else{
- if (v[73]<794){
- s = s -0.30357033;
-}
- else{
- s = s -0.00515535474;
-}}}
- else{
- if (v[42]<1){
- if (v[7]<44){
- s = s + 0.0180323031;
-}
- else{
- s = s + 0.237772942;
-}}
- else{
- if (v[66]<1){
- s = s + 0.229165554;
-}
- else{
- s = s -0.242204309;
-}}}}
- else{
- if (v[91]<243){
- if (v[60]<2){
- if (v[107]<31){
- s = s -0.213319048;
-}
- else{
- s = s + 0.00410684804;
-}}
- else{
- if (v[22]<14){
- s = s -0.668836772;
-}
- else{
- s = s -0.0584421381;
-}}}
- else{
- if (v[109]<510){
- if (v[109]<303){
- s = s + 0.0180449635;
-}
- else{
- s = s -0.0301514585;
-}}
- else{
- if (v[111]<8){
- s = s + 0.0215922743;
-}
- else{
- s = s -0.00584051386;
-}}}}}
- else{
- if (v[79]<1033){
- if (v[103]<20){
- if (v[127]<331){
- if (v[133]<332){
- s = s + 0.253184944;
-}
- else{
- s = s + 0.904238462;
-}}
- else{
- if (v[119]<38){
- s = s + 0.258725196;
-}
- else{
- s = s -0.13868545;
-}}}
- else{
- if (v[81]<60){
- if (v[101]<71){
- s = s -0.140925333;
-}
- else{
- s = s + 0.489566535;
-}}
- else{
- if (v[118]<33){
- s = s -0.00897558872;
-}
- else{
- s = s -0.459285825;
-}}}}
- else{
- if (v[136]<31){
- if (v[81]<55){
- s = s + 0.610615015;
-}
- else{
- s = s + 0.0037140646;
-}}
- else{
- if (v[40]<1){
- if (v[68]<161){
- s = s -0.435318321;
-}
- else{
- s = s + 0.0713345632;
-}}
- else{
- if (v[73]<827){
- s = s -0.448018163;
-}
- else{
- s = s -0.0671115592;
-}}}}}}}}}}
-return s;}
diff --git a/ms2pip/ms2pipC.py b/ms2pip/ms2pipC.py
deleted file mode 100644
index 603010fa..00000000
--- a/ms2pip/ms2pipC.py
+++ /dev/null
@@ -1,1139 +0,0 @@
-#!/usr/bin/env python
-import csv
-import glob
-import itertools
-import logging
-import multiprocessing
-import multiprocessing.dummy
-import os
-import re
-from random import shuffle
-
-import numpy as np
-import pandas as pd
-import xgboost as xgb
-from rich.progress import track
-
-from ms2pip.cython_modules import ms2pip_pyx
-from ms2pip.exceptions import (
- FragmentationModelRequiredError,
- InvalidModificationFormattingError,
- InvalidPEPRECError,
- MissingConfigurationError,
- NoMatchingSpectraFound,
- NoValidPeptideSequencesError,
- TitlePatternError,
- UnknownFragmentationMethodError,
- UnknownModificationError,
- UnknownOutputFormatError,
-)
-from ms2pip.feature_names import get_feature_names_new
-from ms2pip.match_spectra import MatchSpectra
-from ms2pip.ms2pip_tools import calc_correlations, spectrum_output
-from ms2pip.peptides import AMINO_ACID_IDS, Modifications, write_amino_acid_masses
-from ms2pip.predict_xgboost import get_predictions_xgb, validate_requested_xgb_model
-from ms2pip.retention_time import RetentionTime
-from ms2pip.spectrum import read_spectrum_file
-
-logger = logging.getLogger(__name__)
-
-# Supported output formats
-SUPPORTED_OUT_FORMATS = ["csv", "mgf", "msp", "bibliospec", "spectronaut", "dlib"]
-
-# Models and their properties
-# id is passed to get_predictions to select model
-# ion_types is required to write the ion types in the headers of the result files
-# features_version is required to select the features version
-MODELS = {
- "CID": {
- "id": 0,
- "ion_types": ["B", "Y"],
- "peaks_version": "general",
- "features_version": "normal",
- "xgboost_model_files": {
- "b": "model_20190107_CID_train_B.xgboost",
- "y": "model_20190107_CID_train_Y.xgboost",
- },
- "model_hash": {
- "model_20190107_CID_train_B.xgboost": "4398c6ebe23e2f37c0aca42b095053ecea6fb427",
- "model_20190107_CID_train_Y.xgboost": "e0a9eb37e50da35a949d75807d66fb57e44aca0f",
- },
- },
- "HCD2019": {
- "id": 1,
- "ion_types": ["B", "Y"],
- "peaks_version": "general",
- "features_version": "normal",
- },
- "TTOF5600": {
- "id": 2,
- "ion_types": ["B", "Y"],
- "peaks_version": "general",
- "features_version": "normal",
- "xgboost_model_files": {
- "b": "model_20190107_TTOF5600_train_B.xgboost",
- "y": "model_20190107_TTOF5600_train_Y.xgboost",
- },
- "model_hash": {
- "model_20190107_TTOF5600_train_B.xgboost": "ab2e28dfbc4ee60640253b0b4c127fc272c9d0ed",
- "model_20190107_TTOF5600_train_Y.xgboost": "f8e9ddd8ca78ace06f67460a2fea0d8fa2623452",
- },
- },
- "TMT": {
- "id": 3,
- "ion_types": ["B", "Y"],
- "peaks_version": "general",
- "features_version": "normal",
- },
- "iTRAQ": {
- "id": 4,
- "ion_types": ["B", "Y"],
- "peaks_version": "general",
- "features_version": "normal",
- "xgboost_model_files": {
- "b": "model_20190107_iTRAQ_train_B.xgboost",
- "y": "model_20190107_iTRAQ_train_Y.xgboost",
- },
- "model_hash": {
- "model_20190107_iTRAQ_train_B.xgboost": "b8d94ad329a245210c652a5b35d724d2c74d0d50",
- "model_20190107_iTRAQ_train_Y.xgboost": "56ae87d56fd434b53fcc1d291745cabb7baf463a",
- },
- },
- "iTRAQphospho": {
- "id": 5,
- "ion_types": ["B", "Y"],
- "peaks_version": "general",
- "features_version": "normal",
- "xgboost_model_files": {
- "b": "model_20190107_iTRAQphospho_train_B.xgboost",
- "y": "model_20190107_iTRAQphospho_train_Y.xgboost",
- },
- "model_hash": {
- "model_20190107_iTRAQphospho_train_B.xgboost": "e283b158cc50e219f42f93be624d0d0ac01d6b49",
- "model_20190107_iTRAQphospho_train_Y.xgboost": "261b2e1810a299ed7ebf193ce1fb81a608c07d3b",
- },
- },
- # ETD': {'id': 6, 'ion_types': ['B', 'Y', 'C', 'Z'], 'peaks_version': 'etd', 'features_version': 'normal'},
- "HCDch2": {
- "id": 7,
- "ion_types": ["B", "Y", "B2", "Y2"],
- "peaks_version": "ch2",
- "features_version": "normal",
- },
- "CIDch2": {
- "id": 8,
- "ion_types": ["B", "Y", "B2", "Y2"],
- "peaks_version": "ch2",
- "features_version": "normal",
- "xgboost_model_files": {
- "b": "model_20190107_CID_train_B.xgboost",
- "y": "model_20190107_CID_train_Y.xgboost",
- "b2": "model_20190107_CID_train_B2.xgboost",
- "y2": "model_20190107_CID_train_Y2.xgboost",
- },
- "model_hash": {
- "model_20190107_CID_train_B.xgboost": "4398c6ebe23e2f37c0aca42b095053ecea6fb427",
- "model_20190107_CID_train_Y.xgboost": "e0a9eb37e50da35a949d75807d66fb57e44aca0f",
- "model_20190107_CID_train_B2.xgboost": "602f2fc648890aebbbe2646252ade658af3221a3",
- "model_20190107_CID_train_Y2.xgboost": "4e4ad0f1d4606c17015aae0f74edba69f684d399",
- },
- },
- "HCD2021": {
- "id": 9,
- "ion_types": ["B", "Y"],
- "peaks_version": "general",
- "features_version": "normal",
- "xgboost_model_files": {
- "b": "model_20210416_HCD2021_B.xgboost",
- "y": "model_20210416_HCD2021_Y.xgboost",
- },
- "model_hash": {
- "model_20210416_HCD2021_B.xgboost": "c086c599f618b199bbb36e2411701fb2866b24c8",
- "model_20210416_HCD2021_Y.xgboost": "22a5a137e29e69fa6d4320ed7d701b61cbdc4fcf",
- },
- },
- "Immuno-HCD": {
- "id": 10,
- "ion_types": ["B", "Y"],
- "peaks_version": "general",
- "features_version": "normal",
- "xgboost_model_files": {
- "b": "model_20210316_Immuno_HCD_B.xgboost",
- "y": "model_20210316_Immuno_HCD_Y.xgboost",
- },
- "model_hash": {
- "model_20210316_Immuno_HCD_B.xgboost": "977466d378de2e89c6ae15b4de8f07800d17a7b7",
- "model_20210316_Immuno_HCD_Y.xgboost": "71948e1b9d6c69cb69b9baf84d361a9f80986fea",
- },
- },
- "CID-TMT": {
- "id": 11,
- "ion_types": ["B", "Y"],
- "peaks_version": "general",
- "features_version": "normal",
- "xgboost_model_files": {
- "b": "model_20220104_CID_TMT_B.xgboost",
- "y": "model_20220104_CID_TMT_Y.xgboost",
- },
- "model_hash": {
- "model_20220104_CID_TMT_B.xgboost": "fa834162761a6ae444bb6523c9c1a174b9738389",
- "model_20220104_CID_TMT_Y.xgboost": "299539179ca55d4ac82e9aed6a4e0bd134a9a41e",
- },
- },
- "timsTOF2023": {
- "id": 12,
- "ion_types": ["B", "Y"],
- "peaks_version": "general",
- "features_version": "normal",
- "xgboost_model_files": {
- "b": "model_20230912_timsTOF_B.xgboost",
- "y": "model_20230912_timsTOF_Y.xgboost",
- },
- "model_hash": {
- "model_20230912_timsTOF_B.xgboost": "6beb557052455310d8c66311c86866dda8291f4b",
- "model_20230912_timsTOF_Y.xgboost": "8edd87e0fba5f338d0a0881b5afbcf2f48ec5268",
- },
- },
- "timsTOF2024": {
- "id": 13,
- "ion_types": ["B", "Y"],
- "peaks_version": "general",
- "features_version": "normal",
- "xgboost_model_files": {
- "b": "model_20240105_timsTOF_B.xgboost",
- "y": "model_20240105_timsTOF_Y.xgboost",
- },
- "model_hash": {
- "model_20240105_timsTOF_B.xgboost": "d70e145c15cf2bfa30968077a68409699b2fa541",
- "model_20240105_timsTOF_Y.xgboost": "3f0414ee1ad7cff739e0d6242e25bfc22b6ebfe5",
- },
- },
-}
-
-
-MODELS["HCD"] = MODELS["HCD2021"]
-MODELS["timsTOF"] = MODELS["timsTOF2024"]
-
-
-def process_peptides(worker_num, data, afile, modfile, modfile2, PTMmap, model):
- """
- Function for each worker to process a list of peptides. The models are
- chosen based on model. PTMmap, Ntermmap and Ctermmap determine the
- modifications applied to each peptide sequence. Returns the predicted
- spectra for all the peptides.
- """
-
- ms2pip_pyx.ms2pip_init(afile, modfile, modfile2)
-
- # Prepare output variables
- pepid_buf = []
- peplen_buf = []
- charge_buf = []
- mz_buf = []
- target_buf = None
- prediction_buf = []
- vector_buf = []
-
- # transform pandas dataframe into dictionary for easy access
- if "ce" in data.columns:
- specdict = (
- data[["spec_id", "peptide", "modifications", "charge", "ce"]]
- .set_index("spec_id")
- .to_dict()
- )
- ces = specdict["ce"]
- else:
- specdict = (
- data[["spec_id", "peptide", "modifications", "charge"]]
- .set_index("spec_id")
- .to_dict()
- )
- pepids = data["spec_id"].tolist()
- peptides = specdict["peptide"]
- modifications = specdict["modifications"]
- charges = specdict["charge"]
- del specdict
-
- # Track progress for only one worker (good approximation of all workers' progress)
- for pepid in track(
- pepids,
- total=len(pepids),
- disable=worker_num != 0,
- transient=True,
- description="",
- ):
- peptide = peptides[pepid]
- peptide = peptide.replace("L", "I")
- mods = modifications[pepid]
-
- # TODO: Check if 30 is good default CE!
- colen = 30
- if "ce" in data.columns:
- colen = ces[pepid]
-
- # Peptides longer then 101 lead to "Segmentation fault (core dumped)"
- if len(peptide) > 100:
- continue
-
- # convert peptide string to integer list to speed up C code
- peptide = np.array(
- [0] + [AMINO_ACID_IDS[x] for x in peptide] + [0], dtype=np.uint16
- )
- modpeptide = apply_mods(peptide, mods, PTMmap)
-
- pepid_buf.append(pepid)
- peplen = len(peptide) - 2
- peplen_buf.append(peplen)
-
- ch = charges[pepid]
- charge_buf.append(ch)
-
- model_id = MODELS[model]["id"]
- peaks_version = MODELS[model]["peaks_version"]
-
- # get ion mzs
- mzs = ms2pip_pyx.get_mzs(modpeptide, peaks_version)
- mz_buf.append([np.array(m, dtype=np.float32) for m in mzs])
-
- # If using xgboost model file, get feature vectors to predict outside of MP.
- # Predictions will be added in `_merge_predictions` function.
- if "xgboost_model_files" in MODELS[model].keys():
- vector_buf.append(
- np.array(
- ms2pip_pyx.get_vector(peptide, modpeptide, ch),
- dtype=np.uint16,
- )
- )
- else:
- predictions = ms2pip_pyx.get_predictions(
- peptide, modpeptide, ch, model_id, peaks_version, colen
- )
- prediction_buf.append([np.array(p, dtype=np.float32) for p in predictions])
-
- return (
- pepid_buf,
- peplen_buf,
- charge_buf,
- mz_buf,
- target_buf,
- prediction_buf,
- vector_buf,
- )
-
-
-def process_spectra(
- worker_num,
- data,
- spec_file,
- vector_file,
- afile,
- modfile,
- modfile2,
- PTMmap,
- model,
- fragerror,
- spectrum_id_pattern,
-):
- """
- Function for each worker to process a list of spectra. Each peptide's
- sequence is extracted from the mgf file. Then models are chosen based on
- model. PTMmap, Ntermmap and Ctermmap determine the modifications
- applied to each peptide sequence and the spectrum is predicted. Then either
- the feature vectors are returned, or a DataFrame with the predicted and
- empirical intensities.
- """
- ms2pip_pyx.ms2pip_init(afile, modfile, modfile2)
-
- model_id = MODELS[model]["id"]
- peaks_version = MODELS[model]["peaks_version"]
-
- # transform pandas data structure into dictionary for easy access
- if "ce" in data.columns:
- specdict = (
- data[["spec_id", "peptide", "modifications", "ce"]]
- .set_index("spec_id")
- .to_dict()
- )
- ces = specdict["ce"]
- else:
- specdict = (
- data[["spec_id", "peptide", "modifications", "charge"]]
- .set_index("spec_id")
- .to_dict()
- )
- peptides = specdict["peptide"]
- modifications = specdict["modifications"]
- charges = specdict["charge"]
-
- # cols contains the names of the computed features
- cols_n = get_feature_names_new()
- if "ce" in data.columns:
- cols_n.append("ce")
- # cols_n = get_feature_names_catboost()
-
- # if vector_file
- dvectors = []
- dtargets = dict()
- psmids = []
-
- # else
- pepid_buf = []
- peplen_buf = []
- charge_buf = []
- mz_buf = []
- target_buf = []
- prediction_buf = []
- vector_buf = []
-
- spectrum_id_regex = re.compile(spectrum_id_pattern)
-
- # Track progress for only one worker (good approximation of all workers' progress)
- for spectrum in track(
- read_spectrum_file(spec_file),
- total=len(peptides),
- disable=worker_num != 0,
- transient=True,
- description="",
- ):
- # Match title with regex
- match = spectrum_id_regex.search(spectrum.title)
- try:
- title = match[1]
- except (TypeError, IndexError):
- raise TitlePatternError(
- "Spectrum title pattern could not be matched to spectrum IDs "
- f"`{spectrum.title}`. "
- " Are you sure that the regex contains a capturing group?"
- )
-
- if title not in peptides:
- continue
-
- peptide = peptides[title]
- peptide = peptide.replace("L", "I")
- mods = modifications[title]
-
- if spectrum.precursor_charge:
- charge = spectrum.precursor_charge
- else:
- charge = charges[title] # If charge cannot be parsed from MGF
-
- if "mut" in mods:
- continue
-
- # Peptides longer then 101 lead to "Segmentation fault (core dumped)"
- if len(peptide) > 100:
- continue
-
- # convert peptide string to integer list to speed up C code
- peptide = np.array(
- [0] + [AMINO_ACID_IDS[x] for x in peptide] + [0], dtype=np.uint16
- )
-
- try:
- modpeptide = apply_mods(peptide, mods, PTMmap)
- except UnknownModificationError as e:
- logger.warn("Unknown modification: %s", e)
- continue
-
- # Spectrum preprocessing:
- # Remove reporter ions and percursor peak, normalize, tranform
- for label_type in ["iTRAQ", "TMT"]:
- if label_type in model:
- spectrum.remove_reporter_ions("iTRAQ")
- # spectrum.remove_precursor()
- spectrum.tic_norm()
- spectrum.log2_transform()
-
- # TODO: Check if 30 is good default CE!
- # RG: removed `if ce == 0` in get_vector, split up into two functions
- colen = 30
- if "ce" in data.columns:
- try:
- colen = int(float(ces[title]))
- except:
- logger.warn("Could not parse collision energy!")
- continue
-
- if vector_file:
- # get targets
- targets = ms2pip_pyx.get_targets(
- modpeptide,
- spectrum.msms,
- spectrum.peaks,
- float(fragerror),
- peaks_version,
- )
- psmids.extend([title] * (len(targets[0])))
- if "ce" in data.columns:
- dvectors.append(
- np.array(
- ms2pip_pyx.get_vector_ce(peptide, modpeptide, charge, colen),
- dtype=np.uint16,
- )
- ) # SD: added collision energy
- else:
- dvectors.append(
- np.array(
- ms2pip_pyx.get_vector(peptide, modpeptide, charge),
- dtype=np.uint16,
- )
- )
-
- # Collecting targets to dict; works for variable number of ion types
- # For C-term ion types (y, y++, z), flip the order of targets,
- # for correct order in vectors DataFrame
- for i, t in enumerate(targets):
- if i in dtargets.keys():
- if i % 2 == 0:
- dtargets[i].extend(t)
- else:
- dtargets[i].extend(t[::-1])
- else:
- if i % 2 == 0:
- dtargets[i] = [t]
- else:
- dtargets[i] = [t[::-1]]
-
- else:
- # Predict the b- and y-ion intensities from the peptide
- pepid_buf.append(title)
- peplen_buf.append(len(peptide) - 2)
- charge_buf.append(charge)
-
- # get/append ion mzs, targets and predictions
- targets = ms2pip_pyx.get_targets(
- modpeptide,
- spectrum.msms,
- spectrum.peaks,
- float(fragerror),
- peaks_version,
- )
- target_buf.append([np.array(t, dtype=np.float32) for t in targets])
- mzs = ms2pip_pyx.get_mzs(modpeptide, peaks_version)
- mz_buf.append([np.array(m, dtype=np.float32) for m in mzs])
-
- # If using xgboost model file, get feature vectors to predict outside of MP.
- # Predictions will be added in `_merge_predictions` function.
- if "xgboost_model_files" in MODELS[model].keys():
- vector_buf.append(
- np.array(
- ms2pip_pyx.get_vector(peptide, modpeptide, charge),
- dtype=np.uint16,
- )
- )
- else:
- predictions = ms2pip_pyx.get_predictions(
- peptide, modpeptide, charge, model_id, peaks_version, colen
- )
- prediction_buf.append(
- [np.array(p, dtype=np.float32) for p in predictions]
- )
-
- # If feature vectors requested, return specific data
- if vector_file:
- if dvectors:
- # If num_cpu > number of spectra, dvectors can be empty
- if len(dvectors) >= 1:
- # Concatenate dvectors into 2D ndarray before making DataFrame to reduce
- # memory usage
- dvectors = np.concatenate(dvectors)
- df = pd.DataFrame(dvectors, dtype=np.uint16, copy=False)
- df.columns = df.columns.astype(str)
- else:
- df = pd.DataFrame()
- return psmids, df, dtargets
-
- # Else, return general data
- return (
- pepid_buf,
- peplen_buf,
- charge_buf,
- mz_buf,
- target_buf,
- prediction_buf,
- vector_buf,
- )
-
-
-def prepare_titles(titles, num_cpu):
- """
- Take a list and return a list containing num_cpu smaller lists with the
- spectrum titles/peptides that will be split across the workers
- """
- # titles might be ordered from small to large peptides,
- # shuffling improves parallel speeds
- shuffle(titles)
-
- split_titles = [
- titles[i * len(titles) // num_cpu : (i + 1) * len(titles) // num_cpu]
- for i in range(num_cpu)
- ]
- logger.debug(
- "{} spectra (~{:.0f} per cpu)".format(
- len(titles), np.mean([len(a) for a in split_titles])
- )
- )
-
- return split_titles
-
-
-def apply_mods(peptide, mods, PTMmap):
- """
- Takes a peptide sequence and a set of modifications. Returns the modified
- version of the peptide sequence, c- and n-term modifications. This modified
- version are hard coded in ms2pipfeatures_c.c for now.
- """
- modpeptide = np.array(peptide[:], dtype=np.uint16) # Copy to avoid inplace changes
- if mods != "-":
- l = mods.split("|")
- if len(l) % 2 != 0:
- raise InvalidModificationFormattingError(mods)
- for i in range(0, len(l), 2):
- tl = l[i + 1]
- try:
- mod = PTMmap[tl]
- except:
- raise UnknownModificationError(tl)
- try:
- modpeptide[int(l[i])] = mod
- except IndexError:
- raise InvalidModificationFormattingError(
- f"Amino acid position not in peptide for modifications: `{mods}`"
- )
-
- return modpeptide
-
-
-def peakcount(x):
- c = 0.0
- for i in x:
- if i > -9.95:
- c += 1.0
- return c / len(x)
-
-
-class MS2PIP:
- def __init__(
- self,
- pep_file,
- spec_file=None,
- spectrum_id_pattern=None,
- vector_file=None,
- num_cpu=1,
- params=None,
- output_filename=None,
- return_results=False,
- limit=None,
- add_retention_time=False,
- compute_correlations=False,
- match_spectra=False,
- sqldb_uri=None,
- model_dir=None,
- ):
- """
- MS²PIP peak intensity predictor.
-
- Parameters
- ----------
- pep_file : str or pandas.DataFrame
- Path to file or DataFrame with peptide information (see
- https://github.com/compomics/ms2pip_c#peprec-file)
- spec_file : str, optional
- Path to spectrum file with target intensities. Provide for
- prediction evaluation, or in combination with `vector_file` for
- target extraction.
- spectrum_id_pattern : str, optional
- Regular expression pattern to apply to spectrum titles before matching to
- peptide file entries.
- vector_file : str, optional
- Output filepath for training feature vectors. Provide this to
- extract feature vectors from `spec_file`. Requires `spec_file`.
- num_cpu : int, default: 1
- Number of parallel processes for multiprocessing steps.
- params : dict
- Dictonary with `model`, `frag_error` and `modifications`.
- output_filename : str, optional
- Filepath prefix for output files
- return_results : bool, default: False
- Return results after prediction (`MS2PIP.run()`) instead of writing
- to output files.
- limit : int, optional
- Limit to first N peptides in PEPREC file.
- add_retention_time : bool, default: False
- Add retention time predictions with DeepLC.
- compute_correlations : bool, default: False
- Compute correlations between predictions and targets. Requires
- `spec_file`.
- match_spectra : bool, default: False
- Match spectra in `spec_file` or `sqldb_uri` to peptides in
- `pep_file` based on predicted intensities (experimental).
- sqldb_uri : str, optional
- URI to SQL database for `match_spectra` feature.
- model_dir : str, optional
- Custom directory for downloaded XGBoost model files. By
- default, `~/.ms2pip` is used.
-
- Examples
- --------
- >>> from ms2pip.ms2pipC import MS2PIP
- >>> params = {
- ... "ms2pip": {
- ... "ptm": [
- ... "Oxidation,15.994915,opt,M",
- ... "Carbamidomethyl,57.021464,opt,C",
- ... "Acetyl,42.010565,opt,N-term",
- ... ],
- ... "frag_method": "HCD",
- ... "frag_error": 0.02,
- ... "out": "csv",
- ... "sptm": [], "gptm": [],
- ... }
- ... }
- >>> ms2pip = MS2PIP("test.peprec", params=params)
- >>> ms2pip.run()
-
- """
- self.pep_file = pep_file
- self.vector_file = vector_file
- self.spectrum_id_pattern = (
- spectrum_id_pattern if spectrum_id_pattern else "(.*)"
- )
- self.num_cpu = num_cpu
- self.params = params
- self.return_results = return_results
- self.limit = limit
- self.add_retention_time = add_retention_time
- self.compute_correlations = compute_correlations
- self.match_spectra = match_spectra
- self.sqldb_uri = sqldb_uri
- self.model_dir = model_dir
-
- self.afile = None
- self.modfile = None
- self.modfile2 = None
-
- if params is None:
- raise MissingConfigurationError()
-
- if "model" in self.params["ms2pip"]:
- self.model = self.params["ms2pip"]["model"]
- elif "frag_method" in self.params["ms2pip"]:
- self.model = self.params["ms2pip"]["frag_method"]
- else:
- raise FragmentationModelRequiredError()
- self.fragerror = self.params["ms2pip"]["frag_error"]
-
- # Validate requested output formats
- if "out" in self.params["ms2pip"]:
- self.out_formats = [
- o.lower().strip() for o in self.params["ms2pip"]["out"].split(",")
- ]
- for o in self.out_formats:
- if o not in SUPPORTED_OUT_FORMATS:
- raise UnknownOutputFormatError(o)
- else:
- if not return_results:
- logger.info("No output format specified; defaulting to csv")
- self.out_formats = ["csv"]
- else:
- self.out_formats = []
-
- # Validate model_dir
- if not self.model_dir:
- self.model_dir = os.path.join(os.path.expanduser("~"), ".ms2pip")
-
- # Validate requested model
- if self.model in MODELS.keys():
- logger.debug("Using %s models", self.model)
- if "xgboost_model_files" in MODELS[self.model].keys():
- validate_requested_xgb_model(
- MODELS[self.model]["xgboost_model_files"],
- MODELS[self.model]["model_hash"],
- self.model_dir,
- )
- else:
- raise UnknownFragmentationMethodError(self.model)
-
- # TODO: Move to run?
- if output_filename is None and not return_results:
- self.output_filename = "{}_{}".format(
- ".".join(pep_file.split(".")[:-1]), self.model
- )
- else:
- self.output_filename = output_filename
- # TODO
-
- logger.debug(f"Starting workers (num_cpu={self.num_cpu})...")
- if multiprocessing.current_process().daemon:
- logger.warn(
- "MS2PIP is running in a daemon process. Disabling multiprocessing as daemonic processes can't have children."
- )
- self.myPool = multiprocessing.dummy.Pool(1)
- elif self.num_cpu == 1:
- logger.debug("Using dummy multiprocessing pool.")
- self.myPool = multiprocessing.dummy.Pool(1)
- else:
- self.myPool = multiprocessing.Pool(self.num_cpu)
-
- # TODO: Move to run?
- if self.match_spectra:
- self.spec_file = None
- if self.sqldb_uri:
- self.spec_files = None
- elif os.path.isdir(spec_file):
- self.spec_files = glob.glob("{}/*.mgf".format(spec_file))
- else:
- self.spec_files = [spec_file]
- logger.debug("Using spectrum files %s", self.spec_files)
- else:
- self.spec_file = spec_file
- self.spec_files = None
- # TODO
-
- self.mods = Modifications()
- for mod_type in ("sptm", "ptm"):
- self.mods.add_from_ms2pip_modstrings(
- self.params["ms2pip"][mod_type], mod_type=mod_type
- )
-
- # TODO: Pass PEPREC and SPECFILE args here?
- def run(self):
- """Run initiated MS2PIP based on class configuration."""
-
- # TODO: MOVE TO INIT?
- self.afile = write_amino_acid_masses()
- self.modfile = self.mods.write_modifications_file(mod_type="ptm")
- self.modfile2 = self.mods.write_modifications_file(mod_type="sptm")
- #
-
- self._read_peptide_information()
-
- if self.add_retention_time:
- logger.info("Adding retention time predictions")
- rt_predictor = RetentionTime(config=self.params, num_cpu=self.num_cpu)
- rt_predictor.add_rt_predictions(self.data)
-
- # Spectrum file mode
- if self.spec_file:
- logger.info("Processing spectra and peptides...")
- results = self._process_spectra()
- # Feature vectors requested
- if self.vector_file:
- self._write_vector_file(results)
- # Predictions (and targets) requested
- else:
- logger.debug("Merging results")
- all_preds = self._merge_predictions(results)
- # Correlations also requested
- if self.compute_correlations:
- logger.info("Computing correlations")
- correlations = calc_correlations.calc_correlations(all_preds)
- logger.info(
- "Median correlations: \n%s",
- str(correlations.groupby("ion")["pearsonr"].median()),
- )
- if not self.return_results:
- corr_filename = self.output_filename + "_correlations.csv"
- logger.info(f"Writing file {corr_filename}")
- try:
- correlations.to_csv(
- corr_filename,
- index=True,
- lineterminator="\n",
- )
- except (
- TypeError
- ): # Pandas < 1.5 (Required for Python 3.7 support)
- correlations.to_csv(
- corr_filename,
- index=True,
- line_terminator="\n",
- )
- else:
- return correlations
- if not self.return_results:
- pae_filename = self.output_filename + "_pred_and_emp.csv"
- logger.info(f"Writing file {pae_filename}...")
- try:
- all_preds.to_csv(
- pae_filename,
- index=False,
- lineterminator="\n",
- )
- except TypeError: # Pandas < 1.5 (Required for Python 3.7 support)
- all_preds.to_csv(
- pae_filename,
- index=False,
- line_terminator="\n",
- )
- else:
- return all_preds
-
- # Match spectra mode
- elif self.match_spectra:
- results = self._process_peptides()
- matched_spectra = self._match_spectra(results)
- self._write_matched_spectra(matched_spectra)
-
- # Predictions-only mode
- else:
- logger.info("Processing peptides...")
- results = self._process_peptides()
-
- logger.debug("Merging results ...")
- all_preds = self._merge_predictions(results)
-
- if not self.return_results:
- self._write_predictions(all_preds)
- else:
- return all_preds
-
- def cleanup(self):
- """Cleanup temporary files."""
- if self.afile:
- os.remove(self.afile)
- if self.modfile:
- os.remove(self.modfile)
- if self.modfile2:
- os.remove(self.modfile2)
-
- def _read_peptide_information(self):
- # read peptide information
- # the file contains the columns: spec_id, modifications, peptide and charge
- if type(self.pep_file) == str:
- with open(self.pep_file, "rt") as f:
- line = f.readline()
- if line[:7] != "spec_id":
- raise InvalidPEPRECError()
- sep = line[7]
- data = pd.read_csv(
- self.pep_file,
- sep=sep,
- index_col=False,
- dtype={"spec_id": str, "modifications": str},
- nrows=self.limit,
- )
- else:
- data = self.pep_file
- # Unmodified peptides should have a '-', not NaN or an empty string
- data["modifications"] = data["modifications"].replace("", "-").fillna("-")
-
- # Filter PEPREC for unsupported peptides
- num_pep = len(data)
- data = data[
- ~(data["peptide"].str.contains("B|J|O|U|X|Z"))
- & ~(data["peptide"].str.len() < 3)
- & ~(data["peptide"].str.len() > 99)
- ].copy()
- num_pep_filtered = num_pep - len(data)
- if num_pep_filtered > 0:
- logger.warning(
- f"Removed {num_pep_filtered} unsupported peptide sequences (< 3, > 99 "
- f"amino acids, or containing B, J, O, U, X or Z). Retained "
- f"{len(data)} entries."
- )
-
- if len(data) == 0:
- raise NoValidPeptideSequencesError()
-
- self.data = data
-
- def _execute_in_pool(self, titles, func, args):
- split_titles = prepare_titles(titles, self.num_cpu)
- results = []
- for i in range(self.num_cpu):
- tmp = split_titles[i]
- results.append(
- self.myPool.apply_async(
- func,
- args=(i, self.data[self.data.spec_id.isin(tmp)], *args),
- )
- )
- self.myPool.close()
- self.myPool.join()
- return results
-
- def _process_spectra(self):
- """
- When an mgf file is provided, MS2PIP either saves the feature vectors to
- train models with or writes a file with the predicted spectra next to
- the empirical one.
- """
- titles = self.data["spec_id"].to_list()
-
- return self._execute_in_pool(
- titles,
- process_spectra,
- (
- self.spec_file,
- self.vector_file,
- self.afile,
- self.modfile,
- self.modfile2,
- self.mods.ptm_ids,
- self.model,
- self.fragerror,
- self.spectrum_id_pattern,
- ),
- )
-
- def _write_vector_file(self, results):
- all_results = []
- for r in results:
- psmids, df, dtargets = r.get()
-
- # dtargets is a dict, containing targets for every ion type (keys are int)
- for i, t in dtargets.items():
- df["targets_{}".format(MODELS[self.model]["ion_types"][i])] = (
- np.concatenate(t, axis=None)
- )
- df["psmid"] = psmids
-
- all_results.append(df)
-
- # Only concat DataFrames with content (we get empty ones if more cpu's than peptides)
- all_results = pd.concat([df for df in all_results if len(df) != 0])
-
- logger.info("Writing vector file %s...", self.vector_file)
- # write result. write format depends on extension:
- ext = self.vector_file.split(".")[-1]
- if ext == "pkl":
- all_results.to_pickle(self.vector_file + ".pkl")
- elif ext == "csv":
- try:
- all_results.to_csv(self.vector_file, lineterminator="\n")
- except TypeError: # Pandas < 1.5 (Required for Python 3.7 support)
- all_results.to_csv(self.vector_file, line_terminator="\n")
- else:
- # "table" is a tag used to read back the .h5
- all_results.to_hdf(self.vector_file, "table")
-
- return all_results
-
- def _merge_predictions(self, results):
- pepid_bufs = []
- peplen_bufs = []
- charge_bufs = []
- mz_bufs = []
- target_bufs = []
- prediction_bufs = []
- vector_bufs = []
- for r in results:
- (
- pepid_buf,
- peplen_buf,
- charge_buf,
- mz_buf,
- target_buf,
- prediction_buf,
- vector_buf,
- ) = r.get()
- pepid_bufs.extend(pepid_buf)
- peplen_bufs.extend(peplen_buf)
- charge_bufs.extend(charge_buf)
- mz_bufs.extend(mz_buf)
- if target_buf:
- target_bufs.extend(target_buf)
- if prediction_buf:
- prediction_bufs.extend(prediction_buf)
- if vector_buf:
- vector_bufs.extend(vector_buf)
-
- # Validate number of results
- if not mz_bufs:
- raise NoMatchingSpectraFound(
- "No spectra matching titles/IDs from PEPREC could be found in "
- "provided spectrum file."
- )
- logger.debug(f"Gathered data for {len(mz_bufs)} peptides/spectra.")
-
- # If XGBoost model files are used, first predict outside of MP
- # Temporary hack to move XGB prediction step out of MP; ultimately does not
- # make sense to do this in the `_merge_predictions` step...
- if "xgboost_model_files" in MODELS[self.model].keys():
- logger.debug("Converting feature vectors to XGBoost DMatrix...")
- xgb_vector = xgb.DMatrix(np.vstack(vector_bufs))
- num_ions = [l - 1 for l in peplen_bufs]
- prediction_bufs = get_predictions_xgb(
- xgb_vector,
- num_ions,
- MODELS[self.model],
- self.model_dir,
- num_cpu=self.num_cpu,
- )
-
- # Reconstruct DataFrame
- logger.debug("Constructing DataFrame with results...")
- num_ion_types = len(MODELS[self.model]["ion_types"])
- ions = []
- ionnumbers = []
- charges = []
- pepids = []
- for pi, pl in enumerate(peplen_bufs):
- [
- ions.extend([ion_type] * (pl - 1))
- for ion_type in MODELS[self.model]["ion_types"]
- ]
- ionnumbers.extend([x + 1 for x in range(pl - 1)] * num_ion_types)
- charges.extend([charge_bufs[pi]] * (num_ion_types * (pl - 1)))
- pepids.extend([pepid_bufs[pi]] * (num_ion_types * (pl - 1)))
- all_preds = pd.DataFrame()
- all_preds["spec_id"] = pepids
- all_preds["charge"] = charges
- all_preds["ion"] = ions
- all_preds["ionnumber"] = ionnumbers
- all_preds["mz"] = np.concatenate(mz_bufs, axis=None)
- all_preds["prediction"] = np.concatenate(prediction_bufs, axis=None)
- if target_bufs:
- all_preds["target"] = np.concatenate(target_bufs, axis=None)
- if "rt" in self.data:
- all_preds = all_preds.merge(
- self.data[["spec_id", "rt"]], on="spec_id", copy=False
- )
-
- return all_preds
-
- def _process_peptides(self):
- titles = self.data.spec_id.tolist()
- return self._execute_in_pool(
- titles,
- process_peptides,
- (self.afile, self.modfile, self.modfile2, self.mods.ptm_ids, self.model),
- )
-
- def _write_predictions(self, all_preds):
- spec_out = spectrum_output.SpectrumOutput(
- all_preds,
- self.data,
- self.params["ms2pip"],
- output_filename=self.output_filename,
- )
- spec_out.write_results(self.out_formats)
-
- def _match_spectra(self, results):
- mz_bufs, prediction_bufs, _, _, _, pepid_bufs = zip(*(r.get() for r in results))
-
- match_spectra = MatchSpectra(
- self.data,
- self.mods,
- itertools.chain.from_iterable(pepid_bufs),
- itertools.chain.from_iterable(mz_bufs),
- itertools.chain.from_iterable(prediction_bufs),
- )
- if self.spec_files:
- return match_spectra.match_mgfs(self.spec_files)
- elif self.sqldb_uri:
- return match_spectra.match_sqldb(self.sqldb_uri)
- else:
- raise NotImplementedError
-
- def _write_matched_spectra(self, matched_spectra):
- filename = f"{self.output_filename}_matched_spectra.csv"
- logger.info("Writing file %s...", filename)
-
- with open(filename, mode="w") as csv_file:
- csv_writer = csv.writer(csv_file)
- csv_writer.writerow(("spec_id", "matched_file" "matched_title"))
- for pep, spec_file, spec in matched_spectra:
- csv_writer.writerow((pep, spec_file, spec["params"]["title"]))
diff --git a/ms2pip/ms2pip_tools/__init__.py b/ms2pip/ms2pip_tools/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/ms2pip/ms2pip_tools/calc_correlations.py b/ms2pip/ms2pip_tools/calc_correlations.py
deleted file mode 100644
index 0d8d7ef7..00000000
--- a/ms2pip/ms2pip_tools/calc_correlations.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import numpy as np
-
-
-def calc_correlations(df):
- correlations = (
- df.groupby(["spec_id", "ion"])[["target", "prediction"]]
- .corr()
- .iloc[::2]["prediction"]
- )
- correlations.index = correlations.index.droplevel(2)
- correlations = correlations.to_frame().reset_index()
- correlations.columns = ["spec_id", "ion", "pearsonr"]
- return correlations
-
-
-def ms2pip_pearson(true, pred):
- """
- Return pearson of tic-normalized, log-transformed intensities,
- the MS2PIP way.
- """
- tic_norm = lambda x: x / np.sum(x)
- log_transform = lambda x: np.log2(x + 0.001)
- corr = np.corrcoef(log_transform(tic_norm(true)), log_transform(tic_norm(pred)))[0][
- 1
- ]
- return corr
-
-
-def spectral_angle(true, pred, epsilon=1e-7):
- """
- Return square root normalized spectral angle.
- See https://doi.org/10.1074/mcp.O113.036475
- """
- pred_norm = pred / max(np.linalg.norm(pred), epsilon)
- true_norm = true / max(np.linalg.norm(true), epsilon)
- spectral_angle = 1 - (2 * np.arccos(np.dot(pred_norm, true_norm)) / np.pi)
- return spectral_angle
diff --git a/ms2pip/ms2pip_tools/get_elude_predictions.py b/ms2pip/ms2pip_tools/get_elude_predictions.py
deleted file mode 100644
index a132cb28..00000000
--- a/ms2pip/ms2pip_tools/get_elude_predictions.py
+++ /dev/null
@@ -1,162 +0,0 @@
-"""
-ELUDE RT prediction integration for MS2PIP
-
-ELUDE needs to be installed and callable with os.system(elude). Tested with
-ELUDE v3.2.
-
-Can be run as a script or by calling the `get_elude_predictions` function.
-Both the script and the function take an MS2PIP PEPREC file and an ELUDE model
-file.
-
-If the given PEPREC contains PTMs, these need to be configured in the (for now)
-hardcoded dictionary `unimod_mapping`. This is a dictionary where the keys
-represent the name of the PTM in the PEPREC file and the values contain the
-corresponding UniMod IDs.
-"""
-
-
-__author__ = "Ralf Gabriels"
-__credits__ = ["Ralf Gabriels", "Sven Degroeve", "Lennart Martens"]
-__license__ = "Apache License, Version 2.0"
-__version__ = "0.1"
-__email__ = "Ralf.Gabriels@ugent.be"
-
-
-# Standard library
-import os
-import argparse
-
-# Third party packages
-import pandas as pd
-
-
-def argument_parser():
- parser = argparse.ArgumentParser(description='Add ELUDE retention time predictions to existing PEPREC file.')
- parser.add_argument("peprec_file", metavar="",
- help="MS2PIP PEPREC file to add RT predictions to.")
- parser.add_argument('elude_model_file', metavar="",
- help='ELUDE model file.')
- args = parser.parse_args()
- return args
-
-
-def elude_insert_mods(row, peptide_column='peptide', mods_column='modifications',
- unimod_mapping=None):
- """
- Insert PEPREC modifications into peptide sequence for ELUDE.
-
- Accepts normal, N-terminal and C-terminal modifications.
-
- Positional arguments:
- row -- pandas.DataFrame row, for use with .apply(elude_insert_mods, axis=1)
-
- Keyword arguments:
- peptide_column -- Column name of column with peptide sequences
- mods_column -- Column name of column with MS2PIP PEPREC encoded
- modifications
- unimod_mapping -- Dictionary that maps the MS2PIP modification names to
- UniMod accessions
- """
-
- if not unimod_mapping:
- unimod_mapping = {'Oxidation': 35, 'Carbamidomethyl': 4}
-
- peptide = row[peptide_column]
- mods = row[mods_column]
-
- if type(mods) == str:
- if mods == '-':
- peptide_mods = peptide
-
- else:
- mods = mods.split('|')
- pos, names = [list(tup) for tup in zip(*sorted(zip([int(p) for p in mods[::2]], mods[1::2])))]
-
- # Add 0 to pos, if no N-term PTMs present
- if pos[0] != 0:
- pos.insert(0, 0)
- names.insert(0, '')
-
- # Replace "-1" index from C-term PTMs to index of last aa
- if pos[-1] == -1:
- pos[-1] = len(peptide)
-
- pep_split = [peptide[i:j] for i, j in zip(list(pos), list(pos)[1:] + [None])]
- peptide_mods = ''.join([''.join(tup) for tup in zip(['[unimod:{}]'.format(unimod_mapping[n])
- if n else '' for n in names], pep_split)])
-
- else:
- peptide_mods = peptide
-
- return peptide_mods
-
-
-def get_elude_predictions(peprec, elude_model_file, **kwargs):
- """
- Return ELUDE retention time predictions for peptide sequences in MS2PIP PEPREC.
-
- ELUDE needs to be installed and callable with os.system(elude). Tested with ELUDE v3.2
-
- Positional arguments:
- peprec -- MS2PIP PEPREC in pandas.DataFrame()
- elude_model_file -- filename of ELUDE model to apply
-
- kwargs -- keyword arguments are passed to elude_insert_mods
- """
-
- filename_in = '{}_Test.txt'.format(elude_model_file)
- filename_out = '{}_Preds.txt'.format(elude_model_file)
- filename_model = elude_model_file
-
- peprec_copy = peprec.apply(elude_insert_mods, **kwargs, axis=1)
- try:
- peprec_copy.to_csv(
- filename_in,
- sep=' ',
- index=False,
- header=False,
- lineterminator="\n",
- )
- except TypeError: # Pandas < 1.5 (Required for Python 3.7 support)
- peprec_copy.to_csv(
- filename_in,
- sep=' ',
- index=False,
- header=False,
- line_terminator="\n",
- )
- os.system('elude -l "{}" -e "{}" -o "{}" -p'.format(filename_model, filename_in, filename_out))
- preds = pd.read_csv(filename_out, sep='\t', comment='#')
- os.system('rm {}; rm {}'.format(filename_in, filename_out))
-
- return preds['Predicted_RT']
-
-
-def main():
- args = argument_parser()
-
- # HARD CODED, for now
- unimod_mapping = {
- # PEPREC PTM name: UniMod ID,
- 'Oxidation': 35,
- 'Carbamidomethyl': 4
- }
-
- print("Adding ELUDE RT predictions to {}...".format(args.peprec_file))
-
- peprec = pd.read_csv(
- args.peprec_file, sep=" ", index_col=False,
- dtype={"spec_id": str, "modifications": str}
- )
-
- # Remove `* 60` to get RTs in seconds
- peprec['rt'] = get_elude_predictions(peprec, args.elude_model_file, unimod_mapping=unimod_mapping) # * 60
-
- output_filename = '.'.join(args.peprec_file.split('.')[:-1]) + '_with_rt.peprec'
- peprec.to_csv(output_filename, sep=' ', index=False)
-
- print("Ready!")
-
-
-if __name__ == '__main__':
- main()
diff --git a/ms2pip/ms2pip_tools/spectrum_output.py b/ms2pip/ms2pip_tools/spectrum_output.py
deleted file mode 100644
index dac1f7a1..00000000
--- a/ms2pip/ms2pip_tools/spectrum_output.py
+++ /dev/null
@@ -1,858 +0,0 @@
-"""
-Write spectrum files from MS2PIP predictions.
-"""
-
-import itertools
-import logging
-import os
-from ast import literal_eval
-from functools import wraps
-from io import StringIO
-from operator import itemgetter
-from time import localtime, strftime
-from typing import Any, Dict, List
-
-from ms2pip.peptides import Modifications
-
-logger = logging.getLogger("ms2pip.spectrum_output")
-
-
-class InvalidWriteModeError(ValueError):
- pass
-
-
-# Writer decorator
-def writer(**kwargs):
- def deco(write_function):
- @wraps(write_function)
- def wrapper(self):
- return self._write_general(write_function, **kwargs)
-
- return wrapper
-
- return deco
-
-
-def output_format(output_format):
- class OutputFormat:
- def __init__(self, fn):
- self.fn = fn
- self.output_format = output_format
-
- def __set_name__(self, owner, name):
- owner.OUTPUT_FORMATS[self.output_format] = self.fn
- setattr(owner, name, self.fn)
-
- return OutputFormat
-
-
-class SpectrumOutput:
- """
- Write MS2PIP predictions to various output formats.
-
- Parameters
- ----------
- all_preds: pd.DataFrame
- MS2PIP predictions
- peprec: pd.DataFrame
- PEPREC with peptide information
- params: dict
- MS2PIP parameters
- output_filename: str, optional
- path and name for output files, will be suffexed with `_predictions` and the
- relevant file extension (default: ms2pip_predictions)
- write_mode: str, optional
- write mode to use: "wt+" to append to start a new file, "at" to append to an
- existing file (default: "wt+")
- return_stringbuffer: bool, optional
- If True, files are written to a StringIO object, which the write function
- returns. If False, files are written to a file on disk.
- is_log_space: bool, optional
- Set to true if predicted intensities in `all_preds` are in log-space. In that
- case, intensities will first be transformed to "normal"-space.
-
- Methods
- -------
- write_msp()
- Write predictions to MSP file
- write_mgf()
- Write predictions to MGF file
- write_bibliospec()
- Write predictions to Bibliospec SSL/MS2 files (also for Skyline)
- write_spectronaut()
- Write predictions to Spectronaut CSV file
- write_dlib()
- Write predictions to a DLIB SQLite file
- write_csv()
- Write predictions to CSV file
- write_results(output_formats)
- Write MS2PIP predictions in output formats defined by output_formats.
-
- Example
- -------
- >>> so = ms2pip.spectrum_tools.spectrum_output.SpectrumOutput(
- all_preds,
- peprec,
- params
- )
- >>> so.write_msp()
- >>> so.write_spectronaut()
-
- """
-
- OUTPUT_FORMATS = {}
-
- def __init__(
- self,
- all_preds,
- peprec,
- params,
- output_filename="ms2pip_predictions",
- write_mode="wt+",
- return_stringbuffer=False,
- is_log_space=True,
- ):
- self.all_preds = all_preds
- self.peprec = peprec
- self.params = params
- self.output_filename = output_filename
- self.write_mode = write_mode
- self.return_stringbuffer = return_stringbuffer
- self.is_log_space = is_log_space
-
- self.peprec_dict = None
- self.preds_dict = None
- self.normalization = None
- self.diff_modification_mapping = {}
-
- self.has_rt = "rt" in self.peprec.columns
- self.has_protein_list = "protein_list" in self.peprec.columns
-
- self.mods = Modifications()
- self.mods.add_from_ms2pip_modstrings(params["ptm"])
-
- if self.write_mode not in ["wt+", "wt", "at", "w", "a"]:
- raise InvalidWriteModeError(self.write_mode)
-
- if "a" in self.write_mode and self.return_stringbuffer:
- raise InvalidWriteModeError(self.write_mode)
-
- def _generate_peprec_dict(self, rt_to_seconds=True):
- """
- Create easy to access dict from all_preds and peprec dataframes
- """
- peprec_tmp = self.peprec.copy()
-
- if self.has_rt and rt_to_seconds:
- peprec_tmp["rt"] = peprec_tmp["rt"] * 60
-
- peprec_tmp.index = peprec_tmp["spec_id"]
- peprec_tmp.drop("spec_id", axis=1, inplace=True)
-
- self.peprec_dict = peprec_tmp.to_dict(orient="index")
-
- def _generate_preds_dict(self):
- """
- Create easy to access dict from peprec dataframes
- """
- self.preds_dict = {}
- preds_list = self.all_preds[
- ["spec_id", "charge", "ion", "ionnumber", "mz", "prediction"]
- ].values.tolist()
-
- for row in preds_list:
- spec_id = row[0]
- if spec_id in self.preds_dict.keys():
- if row[2] in self.preds_dict[spec_id]["peaks"]:
- self.preds_dict[spec_id]["peaks"][row[2]].append(tuple(row[3:]))
- else:
- self.preds_dict[spec_id]["peaks"][row[2]] = [tuple(row[3:])]
- else:
- self.preds_dict[spec_id] = {
- "charge": row[1],
- "peaks": {row[2]: [tuple(row[3:])]},
- }
-
- def _normalize_spectra(self, method="basepeak_10000"):
- """
- Normalize spectra
- """
- if self.is_log_space:
- self.all_preds["prediction"] = (
- (2 ** self.all_preds["prediction"]) - 0.001
- ).clip(lower=0)
- self.is_log_space = False
-
- if method == "basepeak_10000":
- if self.normalization == "basepeak_10000":
- pass
- elif self.normalization == "basepeak_1":
- self.all_preds["prediction"] *= 10000
- else:
- self.all_preds["prediction"] = self.all_preds.groupby(
- ["spec_id"], group_keys=False
- )["prediction"].apply(lambda x: (x / x.max()) * 10000)
- self.normalization = "basepeak_10000"
-
- elif method == "basepeak_1":
- if self.normalization == "basepeak_1":
- pass
- elif self.normalization == "basepeak_10000":
- self.all_preds["prediction"] /= 10000
- else:
- self.all_preds["prediction"] = self.all_preds.groupby(
- ["spec_id"], group_keys=False
- )["prediction"].apply(lambda x: (x / x.max()))
- self.normalization = "basepeak_1"
-
- elif method == "tic":
- if self.normalization != "tic":
- self.all_preds["prediction"] = self.all_preds.groupby(
- ["spec_id"], group_keys=False
- )["prediction"].apply(lambda x: x / x.sum())
- self.normalization = "tic"
-
- else:
- raise NotImplementedError
-
- def _get_peak_string(
- self,
- peak_dict,
- sep="\t",
- include_zero=False,
- include_annotations=True,
- intensity_type=float,
- ):
- """
- Get MGF/MSP-like peaklist string
- """
- all_peaks = []
- for ion_type, peaks in peak_dict.items():
- for peak in peaks:
- if not include_zero and peak[2] == 0:
- continue
- if include_annotations:
- all_peaks.append(
- (
- peak[1],
- f'{peak[1]:.6f}{sep}{intensity_type(peak[2])}{sep}"{ion_type.lower()}{peak[0]}/0.0"',
- )
- )
- else:
- all_peaks.append((peak[1], f"{peak[1]:.6f}{sep}{peak[2]}"))
-
- all_peaks = sorted(all_peaks, key=itemgetter(0))
- peak_string = "\n".join([peak[1] for peak in all_peaks])
-
- return peak_string
-
- def _get_msp_modifications(self, sequence, modifications):
- """
- Format modifications in MSP-style, e.g. "1/0,E,Glu->pyro-Glu"
- """
-
- if isinstance(modifications, str):
- if not modifications or modifications == "-":
- msp_modifications = "0"
- else:
- mods = modifications.split("|")
- mods = [(int(mods[i]), mods[i + 1]) for i in range(0, len(mods), 2)]
- mods = [(x, y) if x == 0 else (x - 1, y) for (x, y) in mods]
- mods = sorted(mods)
- mods = [(str(x), sequence[x], y) for (x, y) in mods]
- msp_modifications = "/".join([",".join(list(x)) for x in mods])
- msp_modifications = f"{len(mods)}/{msp_modifications}"
- else:
- msp_modifications = "0"
-
- return msp_modifications
-
- def _parse_protein_string(self, protein_list):
- """
- Parse protein string from list, list string literal, or string.
- """
- if isinstance(protein_list, list):
- protein_string = "/".join(protein_list)
- elif isinstance(protein_list, str):
- try:
- protein_string = "/".join(literal_eval(protein_list))
- except ValueError:
- protein_string = protein_list
- else:
- protein_string = ""
- return protein_string
-
- def _get_last_ssl_scannr(self):
- """
- Return scan number of last line in a Bibliospec SSL file.
- """
- ssl_filename = "{}_predictions.ssl".format(self.output_filename)
- with open(ssl_filename, "rt") as ssl:
- for line in ssl:
- last_line = line
- last_scannr = int(last_line.split("\t")[1])
- return last_scannr
-
- def _generate_diff_modification_mapping(self, precision):
- """
- Make modification name -> ssl modification name mapping.
- """
- self.diff_modification_mapping[precision] = {
- ptm.split(",")[0]: "{0:+.{1}f}".format(float(ptm.split(",")[1]), precision)
- for ptm in self.params["ptm"]
- }
-
- def _get_diff_modified_sequence(self, sequence, modifications, precision=1):
- """
- Build BiblioSpec SSL modified sequence string.
- """
- pep = list(sequence)
- mapping = self.diff_modification_mapping[precision]
-
- for loc, name in zip(
- modifications.split("|")[::2], modifications.split("|")[1::2]
- ):
- # C-term mod
- if loc == "-1":
- pep[-1] = pep[-1] + "[{}]".format(mapping[name])
- # N-term mod
- elif loc == "0":
- pep[0] = pep[0] + "[{}]".format(mapping[name])
- # Normal mod
- else:
- pep[int(loc) - 1] = pep[int(loc) - 1] + "[{}]".format(mapping[name])
- return "".join(pep)
-
- @output_format("msp")
- @writer(
- file_suffix="_predictions.msp",
- normalization_method="basepeak_10000",
- requires_dicts=True,
- requires_diff_modifications=False,
- )
- def write_msp(self, file_object):
- """
- Construct MSP string and write to file_object.
- """
-
- for spec_id in sorted(self.peprec_dict.keys()):
- seq = self.peprec_dict[spec_id]["peptide"]
- mods = self.peprec_dict[spec_id]["modifications"]
- charge = self.peprec_dict[spec_id]["charge"]
- prec_mass, prec_mz = self.mods.calc_precursor_mz(seq, mods, charge)
- msp_modifications = self._get_msp_modifications(seq, mods)
- num_peaks = sum(
- [
- len(peaklist)
- for _, peaklist in self.preds_dict[spec_id]["peaks"].items()
- ]
- )
-
- comment_line = f" Mods={msp_modifications} Parent={prec_mz}"
-
- if self.has_protein_list:
- protein_list = self.peprec_dict[spec_id]["protein_list"]
- protein_string = self._parse_protein_string(protein_list)
- comment_line += f' Protein="{protein_string}"'
-
- if self.has_rt:
- rt = self.peprec_dict[spec_id]["rt"]
- comment_line += f" RetentionTime={rt}"
-
- comment_line += f' MS2PIP_ID="{spec_id}"'
-
- out = [
- f"Name: {seq}/{charge}",
- f"MW: {prec_mass}",
- f"Comment:{comment_line}",
- f"Num peaks: {num_peaks}",
- self._get_peak_string(
- self.preds_dict[spec_id]["peaks"],
- sep="\t",
- include_annotations=True,
- intensity_type=int,
- ),
- ]
-
- file_object.writelines([line + "\n" for line in out] + ["\n"])
-
- @output_format("mgf")
- @writer(
- file_suffix="_predictions.mgf",
- normalization_method="basepeak_10000",
- requires_dicts=True,
- requires_diff_modifications=False,
- )
- def write_mgf(self, file_object):
- """
- Construct MGF string and write to file_object
- """
- for spec_id in sorted(self.peprec_dict.keys()):
- seq = self.peprec_dict[spec_id]["peptide"]
- mods = self.peprec_dict[spec_id]["modifications"]
- charge = self.peprec_dict[spec_id]["charge"]
- _, prec_mz = self.mods.calc_precursor_mz(seq, mods, charge)
- msp_modifications = self._get_msp_modifications(seq, mods)
-
- if self.has_protein_list:
- protein_list = self.peprec_dict[spec_id]["protein_list"]
- protein_string = self._parse_protein_string(protein_list)
- else:
- protein_string = ""
-
- out = [
- "BEGIN IONS",
- f"TITLE={spec_id} {seq}/{charge} {msp_modifications} {protein_string}",
- f"PEPMASS={prec_mz}",
- f"CHARGE={charge}+",
- ]
-
- if self.has_rt:
- rt = self.peprec_dict[spec_id]["rt"]
- out.append(f"RTINSECONDS={rt}")
-
- out.append(
- self._get_peak_string(
- self.preds_dict[spec_id]["peaks"],
- sep=" ",
- include_annotations=False,
- )
- )
- out.append("END IONS\n")
- file_object.writelines([line + "\n" for line in out])
-
- @output_format("spectronaut")
- @writer(
- file_suffix="_predictions_spectronaut.csv",
- normalization_method="tic",
- requires_dicts=False,
- requires_diff_modifications=True,
- )
- def write_spectronaut(self, file_obj):
- """
- Construct spectronaut DataFrame and write to file_object.
- """
- if "w" in self.write_mode:
- header = True
- elif "a" in self.write_mode:
- header = False
- else:
- raise InvalidWriteModeError(self.write_mode)
-
- spectronaut_peprec = self.peprec.copy()
-
- # ModifiedPeptide and PrecursorMz columns
- spectronaut_peprec["ModifiedPeptide"] = spectronaut_peprec.apply(
- lambda row: self._get_diff_modified_sequence(
- row["peptide"], row["modifications"]
- ),
- axis=1,
- )
- spectronaut_peprec["PrecursorMz"] = spectronaut_peprec.apply(
- lambda row: self.mods.calc_precursor_mz(
- row["peptide"], row["modifications"], row["charge"]
- )[1],
- axis=1,
- )
- spectronaut_peprec["ModifiedPeptide"] = (
- "_" + spectronaut_peprec["ModifiedPeptide"] + "_"
- )
-
- # Additional columns
- spectronaut_peprec["FragmentLossType"] = "noloss"
-
- # Retention time
- if "rt" in spectronaut_peprec.columns:
- rt_cols = ["iRT"]
- spectronaut_peprec["iRT"] = spectronaut_peprec["rt"]
- else:
- rt_cols = []
-
- # ProteinId
- if self.has_protein_list:
- spectronaut_peprec["ProteinId"] = spectronaut_peprec["protein_list"].apply(
- self._parse_protein_string
- )
- else:
- spectronaut_peprec["ProteinId"] = spectronaut_peprec["spec_id"]
-
- # Rename columns and merge with predictions
- spectronaut_peprec = spectronaut_peprec.rename(
- columns={"charge": "PrecursorCharge", "peptide": "StrippedPeptide"}
- )
- peptide_cols = (
- [
- "ModifiedPeptide",
- "StrippedPeptide",
- "PrecursorCharge",
- "PrecursorMz",
- "ProteinId",
- ]
- + rt_cols
- + ["FragmentLossType"]
- )
- spectronaut_df = spectronaut_peprec[peptide_cols + ["spec_id"]]
- spectronaut_df = self.all_preds.merge(spectronaut_df, on="spec_id")
-
- # Fragment columns
- spectronaut_df["FragmentCharge"] = (
- spectronaut_df["ion"].str.contains("2").map({True: 2, False: 1})
- )
- spectronaut_df["FragmentType"] = spectronaut_df["ion"].str[0].str.lower()
-
- # Rename and sort columns
- spectronaut_df = spectronaut_df.rename(
- columns={
- "mz": "FragmentMz",
- "prediction": "RelativeIntensity",
- "ionnumber": "FragmentNumber",
- }
- )
- fragment_cols = [
- "FragmentCharge",
- "FragmentMz",
- "RelativeIntensity",
- "FragmentType",
- "FragmentNumber",
- ]
- spectronaut_df = spectronaut_df[peptide_cols + fragment_cols]
- try:
- spectronaut_df.to_csv(file_obj, index=False, header=header, sep=";", lineterminator="\n")
- except TypeError: # Pandas < 1.5 (Required for Python 3.7 support)
- spectronaut_df.to_csv(file_obj, index=False, header=header, sep=";", line_terminator="\n")
-
- return file_obj
-
- def _write_bibliospec_core(self, file_obj_ssl, file_obj_ms2, start_scannr=0):
- """
- Construct Bibliospec SSL/MS2 strings and write to file_objects.
- """
-
- for i, spec_id in enumerate(sorted(self.preds_dict.keys())):
- scannr = i + start_scannr
- seq = self.peprec_dict[spec_id]["peptide"]
- mods = self.peprec_dict[spec_id]["modifications"]
- charge = self.peprec_dict[spec_id]["charge"]
- prec_mass, prec_mz = self.mods.calc_precursor_mz(seq, mods, charge)
- ms2_filename = os.path.basename(self.output_filename) + "_predictions.ms2"
-
- peaks = self._get_peak_string(
- self.preds_dict[spec_id]["peaks"],
- sep="\t",
- include_annotations=False,
- )
-
- if isinstance(mods, str) and mods != "-" and mods != "":
- mod_seq = self._get_diff_modified_sequence(seq, mods)
- else:
- mod_seq = seq
-
- rt = self.peprec_dict[spec_id]["rt"] if self.has_rt else ""
-
- # TODO: implement csv instead of manual writing
- file_obj_ssl.write(
- "\t".join(
- [ms2_filename, str(scannr), str(charge), mod_seq, "", "", str(rt)]
- )
- + "\n"
- )
- file_obj_ms2.write(
- "\n".join(
- [
- f"S\t{scannr}\t{prec_mz}",
- f"Z\t{charge}\t{prec_mass}",
- f"D\tseq\t{seq}",
- f"D\tmodified seq\t{mod_seq}",
- peaks,
- ]
- )
- + "\n"
- )
-
- def _write_general(
- self,
- write_function,
- file_suffix,
- normalization_method,
- requires_dicts,
- requires_diff_modifications,
- diff_modification_precision=1,
- ):
- """
- General write function to call core write functions.
-
- Note: Does not work for write_bibliospec and write_dlib functions.
- """
-
- # Normalize if necessary and make dicts
- if not self.normalization == normalization_method:
- self._normalize_spectra(method=normalization_method)
- if requires_dicts:
- self._generate_preds_dict()
- elif requires_dicts and not self.preds_dict:
- self._generate_preds_dict()
- if requires_dicts and not self.peprec_dict:
- self._generate_peprec_dict()
-
- if (
- requires_diff_modifications
- and diff_modification_precision not in self.diff_modification_mapping
- ):
- self._generate_diff_modification_mapping(diff_modification_precision)
-
- # Write to file or stringbuffer
- if self.return_stringbuffer:
- file_object = StringIO()
- logger.info("Writing results to StringIO using %s", write_function.__name__)
- else:
- f_name = self.output_filename + file_suffix
- file_object = open(f_name, self.write_mode)
- logger.info("Writing results to %s", f_name)
-
- write_function(self, file_object)
-
- return file_object
-
- @output_format("bibliospec")
- def write_bibliospec(self):
- """
- Write MS2PIP predictions to BiblioSpec SSL and MS2 spectral library files
- (For example for use in Skyline).
- """
-
- precision = 1
- if precision not in self.diff_modification_mapping:
- self._generate_diff_modification_mapping(precision)
-
- # Normalize if necessary and make dicts
- if not self.normalization == "basepeak_10000":
- self._normalize_spectra(method="basepeak_10000")
- self._generate_preds_dict()
- elif not self.preds_dict:
- self._generate_preds_dict()
- if not self.peprec_dict:
- self._generate_peprec_dict()
-
- if self.return_stringbuffer:
- file_obj_ssl = StringIO()
- file_obj_ms2 = StringIO()
- else:
- file_obj_ssl = open(
- "{}_predictions.ssl".format(self.output_filename), self.write_mode
- )
- file_obj_ms2 = open(
- "{}_predictions.ms2".format(self.output_filename), self.write_mode
- )
-
- # If a new file is written, write headers
- if "w" in self.write_mode:
- start_scannr = 0
- ssl_header = [
- "file",
- "scan",
- "charge",
- "sequence",
- "score-type",
- "score",
- "retention-time",
- "\n",
- ]
- file_obj_ssl.write("\t".join(ssl_header))
- file_obj_ms2.write(
- "H\tCreationDate\t{}\n".format(
- strftime("%Y-%m-%d %H:%M:%S", localtime())
- )
- )
- file_obj_ms2.write("H\tExtractor\tMS2PIP predictions\n")
- else:
- # Get last scan number of ssl file, to continue indexing from there
- # because Bibliospec speclib scan numbers can only be integers
- start_scannr = self._get_last_ssl_scannr() + 1
-
- self._write_bibliospec_core(
- file_obj_ssl, file_obj_ms2, start_scannr=start_scannr
- )
-
- return file_obj_ssl, file_obj_ms2
-
- def _write_dlib_metadata(self, connection):
- from ms2pip.ms2pip_tools.dlib import DLIB_VERSION, Metadata
- from sqlalchemy import select
-
- with connection.begin():
- version = connection.execute(
- select([Metadata.c.Value]).where(Metadata.c.Key == "version")
- ).scalar()
- if version is None:
- connection.execute(
- Metadata.insert().values(
- Key="version",
- Value=DLIB_VERSION,
- )
- )
-
- def _write_dlib_entries(self, connection, precision):
- from ms2pip.ms2pip_tools.dlib import Entry
-
- peptide_to_proteins = set()
-
- with connection.begin():
- for spec_id, peprec in self.peprec_dict.items():
- seq = peprec["peptide"]
- mods = peprec["modifications"]
- charge = peprec["charge"]
-
- prec_mass, prec_mz = self.mods.calc_precursor_mz(seq, mods, charge)
- mod_seq = self._get_diff_modified_sequence(
- seq, mods, precision=precision
- )
-
- all_peaks = sorted(
- itertools.chain.from_iterable(
- self.preds_dict[spec_id]["peaks"].values()
- ),
- key=itemgetter(1),
- )
- mzs = [peak[1] for peak in all_peaks]
- intensities = [peak[2] for peak in all_peaks]
-
- connection.execute(
- Entry.insert().values(
- PrecursorMz=prec_mz,
- PrecursorCharge=charge,
- PeptideModSeq=mod_seq,
- PeptideSeq=seq,
- Copies=1,
- RTInSeconds=peprec["rt"],
- Score=0,
- MassEncodedLength=len(mzs),
- MassArray=mzs,
- IntensityEncodedLength=len(intensities),
- IntensityArray=intensities,
- SourceFile=self.output_filename,
- )
- )
-
- if self.has_protein_list:
- protein_list = peprec["protein_list"]
- if isinstance(protein_list, str):
- protein_list = literal_eval(protein_list)
-
- for protein in protein_list:
- peptide_to_proteins.add((seq, protein))
-
- return peptide_to_proteins
-
- def _write_dlib_peptide_to_protein(self, connection, peptide_to_proteins):
- from ms2pip.ms2pip_tools.dlib import PeptideToProtein
-
- if not self.has_protein_list:
- return
-
- with connection.begin():
- sql_peptide_to_proteins = set()
- proteins = {protein for _, protein in peptide_to_proteins}
- for peptide_to_protein in connection.execute(
- PeptideToProtein.select().where(
- PeptideToProtein.c.ProteinAccession.in_(proteins)
- )
- ):
- sql_peptide_to_proteins.add(
- (
- peptide_to_protein.PeptideSeq,
- peptide_to_protein.ProteinAccession,
- )
- )
-
- peptide_to_proteins.difference_update(sql_peptide_to_proteins)
- for seq, protein in peptide_to_proteins:
- connection.execute(
- PeptideToProtein.insert().values(
- PeptideSeq=seq, isDecoy=False, ProteinAccession=protein
- )
- )
-
- @output_format("dlib")
- def write_dlib(self):
- """
- Write MS2PIP predictions to a DLIB SQLite file.
- """
- from ms2pip.ms2pip_tools.dlib import metadata, open_sqlite
-
- normalization = "basepeak_10000"
- precision = 5
- if not self.normalization == normalization:
- self._normalize_spectra(method=normalization)
- self._generate_preds_dict()
- if not self.peprec_dict:
- self._generate_peprec_dict()
- if precision not in self.diff_modification_mapping:
- self._generate_diff_modification_mapping(precision)
-
- filename = "{}.dlib".format(self.output_filename)
- logger.info("Writing results to %s", filename)
-
- logger.debug(
- "write mode is ignored for DLIB at the file mode, although append or not is respected"
- )
- if "a" not in self.write_mode and os.path.exists(filename):
- os.remove(filename)
-
- if self.return_stringbuffer:
- raise NotImplementedError(
- "`return_stringbuffer` not implemented for DLIB output."
- )
-
- if not self.has_rt:
- raise NotImplementedError("Retention times required to write DLIB file.")
-
- with open_sqlite(filename) as connection:
- metadata.create_all()
- self._write_dlib_metadata(connection)
- peptide_to_proteins = self._write_dlib_entries(connection, precision)
- self._write_dlib_peptide_to_protein(connection, peptide_to_proteins)
-
- def get_normalized_predictions(self, normalization_method="tic"):
- """
- Return normalized copy of predictions.
- """
- self._normalize_spectra(method=normalization_method)
- return self.all_preds.copy()
-
- @output_format("csv")
- def write_csv(self):
- """
- Write MS2PIP predictions to CSV.
- """
-
- self._normalize_spectra(method="tic")
-
- # Write to file or stringbuffer
- if self.return_stringbuffer:
- file_object = StringIO()
- logger.info("Writing results to StringIO using %s", "write_csv")
- else:
- f_name = "{}_predictions.csv".format(self.output_filename)
- file_object = open(f_name, self.write_mode)
- logger.info("Writing results to %s", f_name)
-
- try:
- self.all_preds.to_csv(
- file_object, float_format="%.6g", index=False, lineterminator="\n"
- )
- except TypeError: # Pandas < 1.5 (Required for Python 3.7 support)
- self.all_preds.to_csv(
- file_object, float_format="%.6g", index=False, line_terminator="\n"
- )
- return file_object
-
- def write_results(self, output_formats: List[str]) -> Dict[str, Any]:
- """
- Write MS2PIP predictions in output formats defined by output_formats.
- """
- results = {}
- for output_format in output_formats:
- output_format = output_format.lower()
- writer = self.OUTPUT_FORMATS[output_format]
- results[output_format] = writer(self)
- return results
diff --git a/ms2pip/peptides.py b/ms2pip/peptides.py
deleted file mode 100644
index b4630655..00000000
--- a/ms2pip/peptides.py
+++ /dev/null
@@ -1,200 +0,0 @@
-"""
-MS2PIP / PEPREC modification handling
-"""
-
-import itertools
-import tempfile
-
-from pyteomics import mass
-
-from ms2pip.exceptions import InvalidAminoAcidError
-
-AMINO_ACIDS = [
- "A",
- "C",
- "D",
- "E",
- "F",
- "G",
- "H",
- "I",
- "K",
- "M",
- "N",
- "P",
- "Q",
- "R",
- "S",
- "T",
- "V",
- "W",
- "Y",
-]
-
-AMINO_ACID_MASSES = [
- 71.037114,
- 103.00919,
- 115.026943,
- 129.042593,
- 147.068414,
- 57.021464,
- 137.058912,
- 113.084064,
- 128.094963,
- 131.040485,
- 114.042927,
- 97.052764,
- 128.058578,
- 156.101111,
- 87.032028,
- 101.047679,
- 99.068414,
- 186.079313,
- 163.063329,
- # 147.0354 # iTRAQ fixed N-term modification (gets written to amino acid masses file)
-]
-
-AMINO_ACID_IDS = {a: i for i, a in enumerate(AMINO_ACIDS)}
-
-PROTON_MASS = 1.007825032070059
-
-
-class Modifications:
- def __init__(self):
- """
- MS2PIP / PEPREC modification handling
- """
- self.modifications = {}
- self._mass_shifts = None
- self._ptm_ids = None
- self._next_mod_id = 38 # Omega compatibility (mutations)
-
- def add_from_ms2pip_modstrings(self, modstrings, mod_type="ptm"):
- """
- Add modifications from MS2PIP modstring list
-
- Parameters
- ----------
- modstrings: list(str)
- List of MS2PIP modstrings
-
- Example
- -------
- >>> ms2pip_ptms = [
- ... "Oxidation,15.994915,opt,M",
- ... "Acetyl,42.010565,opt,N-term",
- ... ]
- ... mods = Modifications()
- ... mods.add_from_ms2pip_modstrings(ms2pip_ptms)
- """
-
- if mod_type not in self.modifications:
- self.modifications[mod_type] = {}
-
- # NOTE: opt is ignored
- for mod in modstrings:
- mod_name, mass_shift, opt, amino_acid = mod.split(",")
-
- if amino_acid == "N-term":
- amino_acid_id = -1
- elif amino_acid == "C-term":
- amino_acid_id = -2
- elif amino_acid == "L":
- amino_acid_id = AMINO_ACID_IDS["I"]
- elif amino_acid in AMINO_ACID_IDS:
- amino_acid_id = AMINO_ACID_IDS[amino_acid]
- else:
- raise InvalidAminoAcidError(amino_acid)
-
- self.modifications[mod_type][mod_name] = {
- "mass_shift": float(mass_shift),
- "amino_acid": amino_acid,
- "amino_acid_id": amino_acid_id,
- "mod_id": self._next_mod_id,
- }
- self._next_mod_id += 1
-
- self._mass_shifts = None
-
- @property
- def _all_modifications(self):
- return itertools.chain.from_iterable(
- (mods.items() for mods in self.modifications.values())
- )
-
- @property
- def mass_shifts(self):
- """
- Return modification name -> mass shift mapping.
- """
- if not self._mass_shifts:
- self._mass_shifts = {
- name: mod["mass_shift"] for name, mod in self._all_modifications
- }
- return self._mass_shifts
-
- @property
- def ptm_ids(self):
- """
- Return modification name -> modification id mapping.
- """
- if not self._ptm_ids:
- self._ptm_ids = {
- name: mod["mod_id"] for name, mod in self._all_modifications
- }
- return self._ptm_ids
-
- def write_modifications_file(self, mod_type="ptm"):
- mod_file = tempfile.NamedTemporaryFile(delete=False, mode="w", newline="\n")
- mod_file.write("{}\n".format(len(self.modifications[mod_type])))
- for name, mod in self.modifications[mod_type].items():
- mod_file.write(
- "{},1,{},{}\n".format(
- mod["mass_shift"], mod["amino_acid_id"], mod["mod_id"]
- )
- )
- mod_file.close()
- return mod_file.name
-
- def calc_precursor_mz(self, peptide, modifications, charge):
- """
- Calculate precursor mass and mz for given peptide and modification list,
- taking the modifications into account.
-
- Note: This method does not use the build-in Pyteomics modification handling, as
- that would require a known atomic composition of the modification.
-
- Parameters
- ----------
- peptide: str
- stripped peptide sequence
-
- modifications: str
- MS2PIP-style formatted modifications list (e.g. `0|Acetyl|2|Oxidation`)
-
- charge: int
- precursor charge
-
- Returns
- -------
- prec_mass, prec_mz: tuple(float, float)
- """
-
- charge = int(charge)
- unmodified_mass = mass.fast_mass(peptide)
- mods_massses = sum(
- [self.mass_shifts[mod] for mod in modifications.split("|")[1::2]]
- )
- prec_mass = unmodified_mass + mods_massses
- prec_mz = (prec_mass + charge * PROTON_MASS) / charge
- return prec_mass, prec_mz
-
-
-def write_amino_acid_masses():
- # Includes fixed/variable information for Omega compatibility
- amino_file = tempfile.NamedTemporaryFile(delete=False, mode="w", newline="\n")
- for m in AMINO_ACID_MASSES:
- amino_file.write("{}\n".format(m))
- amino_file.write("0\n")
- amino_file.close()
- return amino_file.name
diff --git a/ms2pip/plot.py b/ms2pip/plot.py
new file mode 100644
index 00000000..56f7bc63
--- /dev/null
+++ b/ms2pip/plot.py
@@ -0,0 +1,23 @@
+from pathlib import Path
+from typing import Union
+
+from ms2pip.spectrum import Spectrum
+
+try:
+ import matplotlib.pyplot as plt
+ import spectrum_utils.plot as sup
+
+ _can_plot = True
+except ImportError:
+ _can_plot = False
+
+
+def spectrum_to_png(spectrum: Spectrum, filepath: Union[str, Path]):
+ """Plot a single spectrum and write to a PNG file."""
+ if not _can_plot:
+ raise ImportError("Matplotlib and spectrum_utils are required to plot spectra.")
+ ax = plt.gca()
+ ax.set_title("MS²PIP prediction for " + str(spectrum.peptidoform))
+ sup.spectrum(spectrum.to_spectrum_utils(), ax=ax)
+ plt.savefig(Path(filepath).with_suffix(".png"))
+ plt.close()
diff --git a/ms2pip/result.py b/ms2pip/result.py
new file mode 100644
index 00000000..3cf7eb73
--- /dev/null
+++ b/ms2pip/result.py
@@ -0,0 +1,128 @@
+"""Definition and handling of MS²PIP results."""
+
+from __future__ import annotations
+
+import csv
+from typing import Any, Dict, List, Optional, Tuple
+from logging import getLogger
+
+import numpy as np
+from psm_utils import PSM
+from pydantic import BaseModel, ConfigDict
+
+try:
+ import spectrum_utils.plot as sup
+ import spectrum_utils.spectrum as sus
+except ImportError:
+ sus = None
+ sup = None
+
+from ms2pip.spectrum import ObservedSpectrum, PredictedSpectrum
+
+logger = getLogger(__name__)
+
+class ProcessingResult(BaseModel):
+ """Result of processing a single PSM."""
+
+ psm_index: int
+ psm: Optional[PSM] = None
+ theoretical_mz: Optional[Dict[str, np.ndarray]] = None
+ predicted_intensity: Optional[Dict[str, np.ndarray]] = None
+ observed_intensity: Optional[Dict[str, np.ndarray]] = None
+ correlation: Optional[float] = None
+ feature_vectors: Optional[np.ndarray] = None
+ model_config = ConfigDict(arbitrary_types_allowed=True)
+
+ def __init__(__pydantic_self__, **data: Any) -> None:
+ """Result of processing a single PSM."""
+ super().__init__(**data)
+
+ def as_spectra(self) -> Tuple[Optional[PredictedSpectrum], Optional[ObservedSpectrum]]:
+ """Convert result to predicted and observed spectra."""
+ if not self.theoretical_mz:
+ raise ValueError("Theoretical m/z values required to convert to spectra.")
+ mz = np.concatenate([i for i in self.theoretical_mz.values()])
+ annotations = np.concatenate(
+ [
+ [ion_type + str(i + 1) for i in range(len(peaks))]
+ for ion_type, peaks in self.theoretical_mz.items()
+ ]
+ )
+ peak_order = np.argsort(mz)
+
+ if self.predicted_intensity:
+ pred_int = np.concatenate([i for i in self.predicted_intensity.values()])
+ pred_int = (2**pred_int) - 0.001 # Unlog intensities
+ predicted = PredictedSpectrum(
+ mz=mz[peak_order],
+ intensity=pred_int[peak_order],
+ annotations=annotations[peak_order],
+ peptidoform=self.psm.peptidoform if self.psm else None,
+ precursor_charge=self.psm.peptidoform.precursor_charge if self.psm else None,
+ )
+ else:
+ predicted = None
+
+ if self.observed_intensity:
+ obs_int = np.concatenate([i for i in self.observed_intensity.values()])
+ obs_int = (2**obs_int) - 0.001 # Unlog intensities
+ observed = ObservedSpectrum(
+ mz=mz[peak_order],
+ intensity=obs_int[peak_order],
+ annotations=annotations[peak_order],
+ peptidoform=self.psm.peptidoform if self.psm else None,
+ precursor_charge=self.psm.peptidoform.precursor_charge if self.psm else None,
+ )
+ else:
+ observed = None
+
+ return predicted, observed
+
+ def plot_spectra(self):
+ """
+ Plot predicted and observed spectra.
+
+ Returns
+ -------
+ matplotlib.axes.Axes
+
+ Notes
+ -----
+ Requires optional dependency ``spectrum_utils`` to be installed.
+
+ """
+ predicted, observed = (
+ spec.to_spectrum_utils() if spec else None for spec in self.as_spectra()
+ )
+ if predicted and observed:
+ ax = sup.mirror(observed, predicted)
+ ax.set_title(
+ f"Observed (top) and predicted (bottom) spectra for {self.psm.peptidoform}"
+ )
+ elif predicted:
+ ax = sup.spectrum(predicted)
+ ax.set_title(f"Predicted spectrum for {self.psm.peptidoform}")
+ elif observed:
+ ax = sup.spectrum(observed)
+ ax.set_title(f"Observed spectrum for {self.psm.peptidoform}")
+ else:
+ raise ValueError("No spectra to plot.")
+ return ax
+
+
+def calculate_correlations(results: List[ProcessingResult]) -> None:
+ """Calculate and add Pearson correlations to list of results."""
+ for result in results:
+ pred_int = np.concatenate([i for i in result.predicted_intensity.values()])
+ obs_int = np.concatenate([i for i in result.observed_intensity.values()])
+ result.correlation = np.corrcoef(pred_int, obs_int)[0][1]
+
+
+def write_correlations(results: List["ProcessingResult"], output_file: str) -> None:
+ """Write correlations to CSV file."""
+ with open(output_file, "wt") as f:
+ fieldnames = ["psm_index", "correlation"]
+ writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t", lineterminator="\n")
+ writer.writeheader()
+ for result in results:
+ writer.writerow({"psm_index": result.psm_index, "correlation": result.correlation})
diff --git a/ms2pip/search_space.py b/ms2pip/search_space.py
new file mode 100644
index 00000000..be31109e
--- /dev/null
+++ b/ms2pip/search_space.py
@@ -0,0 +1,669 @@
+"""
+Define and build the search space for in silico spectral library generation.
+
+This module defines the search space for in silico spectral library generation as a
+:py:class:`~ProteomeSearchSpace` object. Variable and fixed modifications can be configured
+as :py:class:`~ModificationConfig` objects.
+
+The peptide search space can be built from a protein FASTA file and a set of parameters, which can
+then be converted to a :py:class:`psm_utils.PSMList` object for use in :py:mod:`ms2pip`. All
+parameters are listed below at :py:class:`~ProteomeSearchSpace` and can be passed as a
+dictionary, a JSON file, or as a :py:class:`~ProteomeSearchSpace` object. For example:
+
+.. code-block:: json
+
+ {
+ "fasta_file": "test.fasta",
+ "min_length": 8,
+ "max_length": 3,
+ "cleavage_rule": "trypsin",
+ "missed_cleavages": 2,
+ "semi_specific": false,
+ "add_decoys": true,
+ "modifications": [
+ {
+ "label": "UNIMOD:Oxidation",
+ "amino_acid": "M"
+ },
+ {
+ "label": "UNIMOD:Carbamidomethyl",
+ "amino_acid": "C",
+ "fixed": true
+ }
+ ],
+ "max_variable_modifications": 3,
+ "charges": [2, 3]
+ }
+
+
+For an unspecific protein digestion, the cleavage rule can be set to ``unspecific``. This will
+result in a cleavage rule that allows cleavage after any amino acid with an unlimited number of
+allowed missed cleavages.
+
+To disable protein digestion when the FASTA file contains peptides, set the cleavage rule to
+``-``. This will treat each line in the FASTA file as a separate peptide sequence, but still
+allow for modifications and charges to be added.
+
+
+Examples
+--------
+>>> from ms2pip.search_space import ProteomeSearchSpace, ModificationConfig
+>>> search_space = ProteomeSearchSpace(
+... fasta_file="tests/data/test_proteins.fasta",
+... min_length=8,
+... max_length=30,
+... cleavage_rule="trypsin",
+... missed_cleavages=2,
+... semi_specific=False,
+... modifications=[
+... ModificationConfig(label="UNIMOD:Oxidation", amino_acid="M"),
+... ModificationConfig(label="UNIMOD:Carbamidomethyl", amino_acid="C", fixed=True),
+... ],
+... charges=[2, 3],
+... )
+>>> psm_list = search_space.into_psm_list()
+
+>>> from ms2pip.search_space import ProteomeSearchSpace
+>>> search_space = ProteomeSearchSpace.from_any("tests/data/test_search_space.json")
+>>> psm_list = search_space.into_psm_list()
+
+"""
+
+from __future__ import annotations
+
+import multiprocessing
+import multiprocessing.dummy
+from collections import defaultdict
+from functools import partial
+from itertools import chain, combinations, product
+from logging import getLogger
+from pathlib import Path
+from typing import Any, Dict, Generator, List, Optional, Union
+
+import numpy as np
+import pyteomics.fasta
+from psm_utils import PSM, PSMList
+from pydantic import BaseModel, field_validator, model_validator
+from pyteomics.parser import icleave
+from rich.progress import track
+
+logger = getLogger(__name__)
+
+
+class ModificationConfig(BaseModel):
+ """Configuration for a single modification in the search space."""
+
+ label: str
+ amino_acid: Optional[str] = None
+ peptide_n_term: Optional[bool] = False
+ protein_n_term: Optional[bool] = False
+ peptide_c_term: Optional[bool] = False
+ protein_c_term: Optional[bool] = False
+ fixed: Optional[bool] = False
+
+ def __init__(self, **data: Any):
+ """
+ Configuration for a single modification in the search space.
+
+ Parameters
+ ----------
+ label
+ Label of the modification. This can be any valid ProForma 2.0 label.
+ amino_acid
+ Amino acid target of the modification. :py:obj:`None` if the modification is not
+ specific to an amino acid. Default is None.
+ peptide_n_term
+ Whether the modification occurs only on the peptide N-terminus. Default is False.
+ protein_n_term
+ Whether the modification occurs only on the protein N-terminus. Default is False.
+ peptide_c_term
+ Whether the modification occurs only on the peptide C-terminus. Default is False.
+ protein_c_term
+ Whether the modification occurs only on the protein C-terminus. Default is False.
+ fixed
+ Whether the modification is fixed. Default is False.
+
+ """
+ super().__init__(**data)
+
+ @model_validator(mode="after")
+ def _modification_must_have_target(self):
+ target_fields = [
+ "amino_acid",
+ "peptide_n_term",
+ "protein_n_term",
+ "peptide_c_term",
+ "protein_c_term",
+ ]
+ if not any(getattr(self, t) for t in target_fields):
+ raise ValueError("Modifications must have a target (amino acid or N/C-term).")
+ return self
+
+
+DEFAULT_MODIFICATIONS = [
+ ModificationConfig(
+ label="UNIMOD:Oxidation",
+ amino_acid="M",
+ ),
+ ModificationConfig(
+ label="UNIMOD:Carbamidomethyl",
+ amino_acid="C",
+ fixed=True,
+ ),
+]
+
+
+class ProteomeSearchSpace(BaseModel):
+ """Search space for in silico spectral library generation."""
+
+ fasta_file: Path
+ min_length: int = 8
+ max_length: int = 30
+ min_precursor_mz: Optional[float] = 0
+ max_precursor_mz: Optional[float] = np.Inf
+ cleavage_rule: str = "trypsin"
+ missed_cleavages: int = 2
+ semi_specific: bool = False
+ add_decoys: bool = False
+ modifications: List[ModificationConfig] = DEFAULT_MODIFICATIONS
+ max_variable_modifications: int = 3
+ charges: List[int] = [2, 3]
+
+ def __init__(self, **data: Any):
+ """
+ Search space for in silico spectral library generation.
+
+ Parameters
+ ----------
+ fasta_file
+ Path to FASTA file with protein sequences.
+ min_length
+ Minimum peptide length. Default is 8.
+ max_length
+ Maximum peptide length. Default is 30.
+ min_precursor_mz
+ Minimum precursor m/z for peptides. Default is 0.
+ max_precursor_mz
+ Maximum precursor m/z for peptides. Default is np.Inf.
+ cleavage_rule
+ Cleavage rule for peptide digestion. Default is "trypsin".
+ missed_cleavages
+ Maximum number of missed cleavages. Default is 2.
+ semi_specific
+ Allow semi-specific cleavage. Default is False.
+ add_decoys
+ Add decoy sequences to search space. Default is False.
+ modifications
+ List of modifications to consider. Default is oxidation of M and carbamidomethylation
+ of C.
+ max_variable_modifications
+ Maximum number of variable modifications per peptide. Default is 3.
+ charges
+ List of charges to consider. Default is [2, 3].
+
+ """
+
+ super().__init__(**data)
+ self._peptidoform_spaces: List[_PeptidoformSearchSpace] = []
+
+ @field_validator("modifications")
+ @classmethod
+ def _validate_modifications(cls, v):
+ if all(isinstance(m, ModificationConfig) for m in v):
+ return v
+ elif all(isinstance(m, dict) for m in v):
+ return [ModificationConfig(**modification) for modification in v]
+ else:
+ raise ValueError(
+ "Modifications should be a list of dicts or ModificationConfig objects."
+ )
+
+ @model_validator(mode="after")
+ def _validate_unspecific_cleavage(self):
+ """Validate and configure unspecific cleavage settings."""
+ # `unspecific` is not an option in pyteomics.parser.icleave, so we configure
+ # the settings for unspecific cleavage manually.
+ if self.cleavage_rule == "unspecific":
+ self.missed_cleavages = self.max_length
+ self.cleavage_rule = r"(?<=[A-Z])"
+ return self
+
+ def __len__(self):
+ if not self._peptidoform_spaces:
+ raise ValueError("Search space must be built before length can be determined.")
+ return sum(len(pep_space) for pep_space in self._peptidoform_spaces)
+
+ @classmethod
+ def from_any(cls, _input: Union[dict, str, Path, ProteomeSearchSpace]) -> ProteomeSearchSpace:
+ """
+ Create ProteomeSearchSpace from various input types.
+
+ Parameters
+ ----------
+ _input
+ Search space parameters as a dictionary, a path to a JSON file, an existing
+ :py:class:`ProteomeSearchSpace` object.
+
+ """
+ if isinstance(_input, ProteomeSearchSpace):
+ return _input
+ elif isinstance(_input, (str, Path)):
+ with open(_input, "rt") as f:
+ return cls.model_validate_json(f.read())
+ elif isinstance(_input, dict):
+ return cls.model_validate(_input)
+ else:
+ raise ValueError("Search space must be a dict, str, Path, or ProteomeSearchSpace.")
+
+ def build(self, processes: int = 1):
+ """
+ Build peptide search space from FASTA file.
+
+ Parameters
+ ----------
+ processes : int
+ Number of processes to use for parallelization.
+
+ """
+ processes = processes if processes else multiprocessing.cpu_count()
+ self._digest_fasta(processes)
+ self._remove_redundancy()
+ self._add_modifications(processes)
+ self._add_charges()
+
+ def __iter__(self) -> Generator[PSM, None, None]:
+ """
+ Generate PSMs from search space.
+
+ If :py:meth:`build` has not been called, the search space will first be built with the
+ given parameters.
+
+ Parameters
+ ----------
+ processes : int
+ Number of processes to use for parallelization.
+
+ """
+ # Build search space if not already built
+ if not self._peptidoform_spaces:
+ raise ValueError("Search space must be built before PSMs can be generated.")
+
+ spectrum_id = 0
+ for pep_space in self._peptidoform_spaces:
+ for pep in pep_space:
+ yield PSM(
+ peptidoform=pep,
+ spectrum_id=spectrum_id,
+ protein_list=pep_space.proteins,
+ )
+ spectrum_id += 1
+
+ def filter_psms_by_mz(self, psms: PSMList) -> PSMList:
+ """Filter PSMs by precursor m/z range."""
+ return PSMList(
+ psm_list=[
+ psm
+ for psm in psms
+ if self.min_precursor_mz <= psm.peptidoform.theoretical_mz <= self.max_precursor_mz
+ ]
+ )
+
+ def _digest_fasta(self, processes: int = 1):
+ """Digest FASTA file to peptides and populate search space."""
+ # Convert to string to avoid issues with Path objects
+ self.fasta_file = str(self.fasta_file)
+ n_proteins = _count_fasta_entries(self.fasta_file)
+ if self.add_decoys:
+ fasta_db = pyteomics.fasta.decoy_db(
+ self.fasta_file,
+ mode="reverse",
+ decoy_only=False,
+ keep_nterm=True,
+ )
+ n_proteins *= 2
+ else:
+ fasta_db = pyteomics.fasta.FASTA(self.fasta_file)
+
+ # Read proteins and digest to peptides
+ with _get_pool(processes) as pool:
+ partial_digest_protein = partial(
+ _digest_single_protein,
+ min_length=self.min_length,
+ max_length=self.max_length,
+ cleavage_rule=self.cleavage_rule,
+ missed_cleavages=self.missed_cleavages,
+ semi_specific=self.semi_specific,
+ )
+ results = track(
+ pool.imap(partial_digest_protein, fasta_db),
+ total=n_proteins,
+ description="Digesting proteins...",
+ transient=True,
+ )
+ self._peptidoform_spaces = list(chain.from_iterable(results))
+
+ def _remove_redundancy(self):
+ """Remove redundancy in peptides and combine protein lists."""
+ peptide_dict = dict()
+ for peptide in track(
+ self._peptidoform_spaces,
+ description="Removing peptide redundancy...",
+ transient=True,
+ ):
+ if peptide.sequence in peptide_dict:
+ peptide_dict[peptide.sequence].proteins.extend(peptide.proteins)
+ else:
+ peptide_dict[peptide.sequence] = peptide
+
+ # Overwrite with non-redundant peptides
+ self._peptidoform_spaces = list(peptide_dict.values())
+
+ def _add_modifications(self, processes: int = 1):
+ """Add modifications to peptides in search space."""
+ modifications_by_target = _restructure_modifications_by_target(self.modifications)
+ modification_options = []
+ with _get_pool(processes) as pool:
+ partial_get_modification_versions = partial(
+ _get_peptidoform_modification_versions,
+ modifications=self.modifications,
+ modifications_by_target=modifications_by_target,
+ max_variable_modifications=self.max_variable_modifications,
+ )
+ modification_options = pool.imap(
+ partial_get_modification_versions, self._peptidoform_spaces
+ )
+ for pep, mod_opt in track(
+ zip(self._peptidoform_spaces, modification_options),
+ description="Adding modifications...",
+ total=len(self._peptidoform_spaces),
+ transient=True,
+ ):
+ pep.modification_options = mod_opt
+
+ def _add_charges(self):
+ """Add charge permutations to peptides in search space."""
+ for peptide in track(
+ self._peptidoform_spaces,
+ description="Adding charge permutations...",
+ transient=True,
+ ):
+ peptide.charge_options = self.charges
+
+
+class _PeptidoformSearchSpace(BaseModel):
+ """Search space for a given amino acid sequence."""
+
+ sequence: str
+ proteins: List[str]
+ is_n_term: Optional[bool] = None
+ is_c_term: Optional[bool] = None
+ modification_options: List[Dict[int, ModificationConfig]] = []
+ charge_options: List[int] = []
+
+ def __init__(self, **data: Any):
+ """
+ Search space for a given amino acid sequence.
+
+ Parameters
+ ----------
+ sequence
+ Amino acid sequence of the peptidoform.
+ proteins
+ List of protein IDs containing the peptidoform.
+ is_n_term
+ Whether the peptidoform is an N-terminal peptide. Default is None.
+ is_c_term
+ Whether the peptidoform is a C-terminal peptide. Default is None.
+ modification_options
+ List of dictionaries with modification positions and configurations. Default is [].
+ charge_options
+ List of charge states to consider. Default is [].
+
+ """
+ super().__init__(**data)
+
+ def __len__(self):
+ return len(self.modification_options) * len(self.charge_options)
+
+ def __iter__(self) -> Generator[str, None, None]:
+ """Yield peptidoform strings with given charges and modifications."""
+ if not self.charge_options:
+ raise ValueError("Peptide charge options not defined.")
+ if not self.modification_options:
+ raise ValueError("Peptide modification options not defined.")
+
+ for modifications, charge in product(self.modification_options, self.charge_options):
+ yield self._construct_peptidoform_string(self.sequence, modifications, charge)
+
+ @staticmethod
+ def _construct_peptidoform_string(
+ sequence: str, modifications: Dict[int, ModificationConfig], charge: int
+ ) -> str:
+ if not modifications:
+ return f"{sequence}/{charge}"
+
+ modded_sequence = list(sequence)
+ for position, mod in modifications.items():
+ if isinstance(position, int):
+ aa = modded_sequence[position]
+ if aa != mod.amino_acid:
+ raise ValueError(
+ f"Modification {mod.label} at position {position} does not match amino "
+ f"acid {aa}."
+ )
+ modded_sequence[position] = f"{aa}[{mod.label}]"
+ elif position == "N":
+ modded_sequence.insert(0, f"[{mod.label}]-")
+ elif position == "C":
+ modded_sequence.append(f"-[{mod.label}]")
+ else:
+ raise ValueError(f"Invalid position {position} for modification {mod.label}.")
+
+ return f"{''.join(modded_sequence)}/{charge}"
+
+
+def _digest_single_protein(
+ protein: pyteomics.fasta.Protein,
+ min_length: int = 8,
+ max_length: int = 30,
+ cleavage_rule: str = "trypsin",
+ missed_cleavages: int = 2,
+ semi_specific: bool = False,
+) -> List[_PeptidoformSearchSpace]:
+ """Digest protein sequence and return a list of validated peptides."""
+
+ def valid_residues(sequence: str) -> bool:
+ return not any(aa in sequence for aa in ["B", "J", "O", "U", "X", "Z"])
+
+ def parse_peptide(
+ start_position: int,
+ sequence: str,
+ protein: pyteomics.fasta.Protein,
+ ) -> _PeptidoformSearchSpace:
+ """Parse result from parser.icleave into Peptide."""
+ return _PeptidoformSearchSpace(
+ sequence=sequence,
+ # Assumes protein ID is description until first space
+ proteins=[protein.description.split(" ")[0]],
+ is_n_term=start_position == 0,
+ is_c_term=start_position + len(sequence) == len(protein.sequence),
+ )
+
+ peptides = [
+ parse_peptide(start, seq, protein)
+ for start, seq in icleave(
+ protein.sequence,
+ cleavage_rule,
+ missed_cleavages=missed_cleavages,
+ min_length=min_length,
+ max_length=max_length,
+ semi=semi_specific,
+ )
+ if valid_residues(seq)
+ ]
+
+ return peptides
+
+
+def _count_fasta_entries(filename: Path) -> int:
+ """Count the number of entries in a FASTA file."""
+ with open(filename, "rt") as f:
+ count = 0
+ for line in f:
+ if line[0] == ">":
+ count += 1
+ return count
+
+
+def _restructure_modifications_by_target(
+ modifications: List[ModificationConfig],
+) -> Dict[str, Dict[str, List[ModificationConfig]]]:
+ """Restructure variable modifications to options per side chain or terminus."""
+ modifications_by_target = {
+ "sidechain": defaultdict(lambda: []),
+ "peptide_n_term": defaultdict(lambda: []),
+ "peptide_c_term": defaultdict(lambda: []),
+ "protein_n_term": defaultdict(lambda: []),
+ "protein_c_term": defaultdict(lambda: []),
+ }
+
+ def add_mod(mod, target, amino_acid):
+ if amino_acid:
+ modifications_by_target[target][amino_acid].append(mod)
+ else:
+ modifications_by_target[target]["any"].append(mod)
+
+ for mod in modifications:
+ if mod.fixed:
+ continue
+ if mod.peptide_n_term:
+ add_mod(mod, "peptide_n_term", mod.amino_acid)
+ elif mod.peptide_c_term:
+ add_mod(mod, "peptide_c_term", mod.amino_acid)
+ elif mod.protein_n_term:
+ add_mod(mod, "protein_n_term", mod.amino_acid)
+ elif mod.protein_c_term:
+ add_mod(mod, "protein_c_term", mod.amino_acid)
+ else:
+ add_mod(mod, "sidechain", mod.amino_acid)
+
+ return {k: dict(v) for k, v in modifications_by_target.items()}
+
+
+def _get_modification_possibilities_by_site(
+ peptide: _PeptidoformSearchSpace,
+ modifications_by_target: Dict[str, Dict[str, List[ModificationConfig]]],
+ modifications: List[ModificationConfig],
+) -> Dict[Union[str, int], List[ModificationConfig]]:
+ """Get all possible modifications for each site in a peptide sequence."""
+ possibilities_by_site = defaultdict(list)
+
+ # Generate dictionary of positions per amino acid
+ position_dict = defaultdict(list)
+ for pos, aa in enumerate(peptide.sequence):
+ position_dict[aa].append(pos)
+ # Map modifications to positions
+ for aa in set(position_dict).intersection(set(modifications_by_target["sidechain"])):
+ possibilities_by_site.update(
+ {pos: modifications_by_target["sidechain"][aa] for pos in position_dict[aa]}
+ )
+
+ # Assign possible modifications per terminus
+ for terminus, position, site_name, specificity in [
+ ("peptide_n_term", 0, "N", None),
+ ("peptide_c_term", -1, "C", None),
+ ("protein_n_term", 0, "N", "is_n_term"),
+ ("protein_c_term", -1, "C", "is_c_term"),
+ ]:
+ if specificity is None or getattr(peptide, specificity):
+ for site, mods in modifications_by_target[terminus].items():
+ if site == "any" or peptide.sequence[position] == site:
+ possibilities_by_site[site_name].extend(mods)
+
+ # Override with fixed modifications
+ for mod in modifications:
+ aa = mod.amino_acid
+ # Skip variable modifications
+ if not mod.fixed:
+ continue
+ # Assign if specific aa matches or if no aa is specified for each terminus
+ for terminus, position, site_name, specificity in [
+ ("peptide_n_term", 0, "N", None),
+ ("peptide_c_term", -1, "C", None),
+ ("protein_n_term", 0, "N", "is_n_term"),
+ ("protein_c_term", -1, "C", "is_c_term"),
+ ]:
+ if getattr(mod, terminus): # Mod has this terminus
+ if specificity is None or getattr(peptide, specificity): # Specificity matches
+ if not aa or (aa and peptide.sequence[position] == aa): # AA matches
+ possibilities_by_site[site_name] = [mod] # Override with fixed mod
+ break # Allow `else: if amino_acid` if no terminus matches
+ # Assign if fixed modification is not terminal and specific aa matches
+ else:
+ if aa:
+ for pos in position_dict[aa]:
+ possibilities_by_site[pos] = [mod]
+
+ return possibilities_by_site
+
+
+def _get_peptidoform_modification_versions(
+ peptide: _PeptidoformSearchSpace,
+ modifications: List[ModificationConfig],
+ modifications_by_target: Dict[str, Dict[str, List[ModificationConfig]]],
+ max_variable_modifications: int = 3,
+) -> List[Dict[Union[str, int], List[ModificationConfig]]]:
+ """
+ Get all potential combinations of modifications for a peptide sequence.
+
+ Examples
+ --------
+ >>> peptide = PeptidoformSpace(sequence="PEPTIDE", proteins=["PROTEIN"])
+ >>> phospho = ModificationConfig(label="Phospho", amino_acid="T", fixed=False)
+ >>> acetyl = ModificationConfig(label="Acetyl", peptide_n_term=True, fixed=False)
+ >>> modifications = [phospho, acetyl]
+ >>> modifications_by_target = {
+ ... "sidechain": {"S": [modifications[0]]},
+ ... "peptide_n_term": {"any": [modifications[1]]},
+ ... "peptide_c_term": {"any": []},
+ ... "protein_n_term": {"any": []},
+ ... "protein_c_term": {"any": []},
+ ... }
+ >>> _get_modification_versions(peptide, modifications, modifications_by_target)
+ [{}, {3: phospho}, {0: acetyl}, {0: acetyl, 3: phospho}]
+
+ """
+ # Get all possible modifications for each site in the peptide sequence
+ possibilities_by_site = _get_modification_possibilities_by_site(
+ peptide, modifications_by_target, modifications
+ )
+
+ # Separate fixed and variable modification sites
+ fixed_modifications = {}
+ variable_sites = []
+ for site, mods in possibilities_by_site.items():
+ for mod in mods:
+ if mod.fixed:
+ fixed_modifications[site] = mod
+ else:
+ variable_sites.append((site, mod))
+
+ # Generate all combinations of variable modifications up to the maximum allowed
+ modification_versions = []
+ for i in range(max_variable_modifications + 1):
+ for comb in combinations(variable_sites, i):
+ combination_dict = fixed_modifications.copy()
+ for site, mod in comb:
+ combination_dict[site] = mod
+ modification_versions.append(combination_dict)
+
+ return modification_versions
+
+
+def _get_pool(processes: int) -> Union[multiprocessing.Pool, multiprocessing.dummy.Pool]:
+ """Get a multiprocessing pool with the given number of processes."""
+ # TODO: fix None default value for processes
+ if processes > 1:
+ return multiprocessing.Pool(processes)
+ else:
+ return multiprocessing.dummy.Pool(processes)
diff --git a/ms2pip/single_prediction.py b/ms2pip/single_prediction.py
deleted file mode 100644
index d50f8559..00000000
--- a/ms2pip/single_prediction.py
+++ /dev/null
@@ -1,335 +0,0 @@
-""""Run MS²PIP prediction for single peptide."""
-
-import logging
-import os
-import re
-
-import click
-import matplotlib.pyplot as plt
-import numpy as np
-import spectrum_utils.plot as sup
-import spectrum_utils.spectrum as sus
-import xgboost as xgb
-
-from ms2pip.config_parser import ConfigParser
-from ms2pip.cython_modules import ms2pip_pyx
-from ms2pip.exceptions import InvalidModificationFormattingError, InvalidPeptideError
-from ms2pip.ms2pipC import MODELS, apply_mods
-from ms2pip.peptides import AMINO_ACID_IDS, Modifications, write_amino_acid_masses
-from ms2pip.predict_xgboost import initialize_xgb_models, validate_requested_xgb_model
-
-logger = logging.getLogger("ms2pip")
-
-
-class SinglePrediction:
- """Run MS²PIP prediction for single peptide."""
-
- def __init__(self, modification_strings=None, model_dir=None) -> None:
- """
- Run MS²PIP prediction for single peptide.
-
- Parameters
- ----------
- modification_strings: list-like
- List of MS²PIP configuration-style modification strings, e.g.
- `Carbamidomethyl,57.02146,opt,C` or `Oxidation,15.994915,opt,M`. See MS²PIP
- README.md for more info.
- model_dir : str, optional
- Custom directory for downloaded XGBoost model files. By default,
- `~/.ms2pip` is used.
-
- Examples
- --------
- >>> from ms2pip.single_prediction import SinglePrediction
- >>> ms2pip_sp = SinglePrediction(
- >>> modification_strings=[
- >>> "Carbamidomethyl,57.021464,opt,C"
- >>> ]
- >>> )
- >>> mz, intensity, annotation = ms2pip_sp.predict(
- >>> "GSIGECIAEEEEFELDSESNR", "6|Carbamidomethyl", 3
- >>> )
-
- """
- if not modification_strings:
- modification_strings = []
- self._init_ms2pip(modification_strings)
-
- if model_dir:
- self.model_dir = model_dir
- else:
- self.model_dir = os.path.join(os.path.expanduser("~"), ".ms2pip")
-
- def _init_ms2pip(self, modification_strings):
- self.mod_info = Modifications()
- self.mod_info.modifications = {"ptm": {}, "sptm": {}}
- self.mod_info.add_from_ms2pip_modstrings(modification_strings)
- afile = write_amino_acid_masses()
- modfile = self.mod_info.write_modifications_file(mod_type="ptm")
- modfile2 = self.mod_info.write_modifications_file(mod_type="sptm")
- ms2pip_pyx.ms2pip_init(afile, modfile, modfile2)
-
- def predict(
- self,
- peptide,
- modifications,
- charge,
- model="HCD",
- validate_input=True,
- ):
- """
- Predict single peptide spectrum with MS²PIP.
-
- Parameters
- ----------
- peptide: string
- Unmodified peptide sequence. Only canonical amino acids are allowed, and
- peptide sequence should be of length [3, 100].
- modifications: string
- MS²PIP style-formatted modification string (e.g. `0|Acetyl|5|Oxidation`).
- See MS²PIP README.md for more info.
- charge: int
- Peptide precursor charge
- model: string (default: "HCD")
- MS²PIP model to use, identical to the option in the MS²PIP configuration
- file.
- validate_input: boolean (default: True)
- Whether to validate input peptide and modifications. Disable for speed-up.
-
- Returns
- -------
- mz: list[float]
- List with fragment ion m/z values in Da.
- intensity: list[float]
- List with TIC-normalized predicted intensities, order matches `mz`
- annotation: list[str]
- List with fragment ion types and series, order matches `mz`
-
- """
- peptide = peptide.upper().replace("L", "I")
-
- if validate_input:
- self._validate_sequence(peptide)
- self._validate_mod_string(modifications)
-
- peptide = np.array(
- [0] + [AMINO_ACID_IDS[x] for x in peptide] + [0], dtype=np.uint16
- )
- modpeptide = apply_mods(peptide, modifications, self.mod_info.ptm_ids)
- model_id = MODELS[model]["id"]
- peaks_version = MODELS[model]["peaks_version"]
- ce = 30
-
- mz = np.array(ms2pip_pyx.get_mzs(modpeptide, peaks_version))
- if "xgboost_model_files" in MODELS[model].keys():
- validate_requested_xgb_model(
- MODELS[model]["xgboost_model_files"],
- MODELS[model]["model_hash"],
- self.model_dir,
- )
- xgboost_models = initialize_xgb_models(
- MODELS[model]["xgboost_model_files"],
- self.model_dir,
- 1,
- )
- xgb_vector = np.array(
- ms2pip_pyx.get_vector(peptide, modpeptide, charge), dtype=np.uint16
- )
- xgb_vector = xgb.DMatrix(xgb_vector)
- intensity = []
- for ion_type, model_file in xgboost_models.items():
- preds = model_file.predict(xgb_vector)
- if ion_type in ["x", "y", "y2", "z"]:
- preds = list(np.array(preds[::-1], dtype=np.float32))
- elif ion_type in ["a", "b", "b2", "c"]:
- preds = list(np.array(preds, dtype=np.float32))
- intensity.append(preds)
- intensity = np.array(intensity)
- else:
- intensity = np.array(
- ms2pip_pyx.get_predictions(
- peptide, modpeptide, charge, model_id, peaks_version, ce
- )
- )
- annotation = []
- for ion_type in MODELS[model]["ion_types"]:
- annotation.append(
- [
- ion_type.lower() + str(i + 1)
- for i in range(len(mz[MODELS[model]["ion_types"].index(ion_type)]))
- ]
- )
- annotation = np.array(annotation)
- mz = mz.flatten()
- intensity = self._tic_normalize(self._transform(intensity.flatten()))
- annotation = annotation.flatten()
- return mz, intensity, annotation
-
- def plot_prediction(
- self,
- peptide,
- modifications,
- charge,
- prediction=None,
- ax=None,
- filename=None,
- ):
- """
- Plot MS²PIP-predicted spectrum with spectrum_utils.
-
- Parameters
- ----------
- peptide: string
- Unmodified peptide sequence. Only canonical amino acids are allowed, and
- peptide sequence should be of length [3, 100].
- modifications: string
- MS²PIP style-formatted modification string (e.g. `0|Acetyl|5|Oxidation`).
- See MS²PIP README.md for more info.
- charge: int
- Peptide precursor charge.
- prediction: tuple or None (default: None)
- Tuple with `ms2pip.single_prediction.SinglePrediction.predict()` output.
- ax: matplotlib.axes.Axes or None (default: None)
- Figure ax to plot onto.
- filename: str or None (default: None)
- Filename to save plot to. File extension defines the format. Figure will
- not be saved if None.
-
- """
- if not prediction:
- prediction = self.predict(peptide, modifications, charge)
- mz, intensity, annotation = prediction
-
- identifier = f"{peptide}/{charge}/{modifications}"
- precursor_mz = self.mod_info.calc_precursor_mz(peptide, modifications, charge)
- mod_dict = self._modifications_to_dict(modifications)
- sus_annotation = self._get_sus_annotation(mz, annotation)
-
- spectrum = sus.MsmsSpectrum(
- identifier,
- precursor_mz,
- charge,
- mz,
- intensity,
- annotation=sus_annotation,
- retention_time=None,
- peptide=peptide,
- modifications=mod_dict,
- )
-
- if not ax:
- ax = plt.gca()
- sup.spectrum(spectrum, ax=ax)
- ax.set_title("MS²PIP prediction for " + identifier)
- if filename:
- plt.savefig(filename)
-
- @staticmethod
- def _validate_sequence(sequence):
- """Validate peptide sequence for MS²PIP."""
- pattern = r"[ACDEFGHIKLMNPQRSTVWY]{3,99}"
- if not re.fullmatch(pattern, sequence):
- raise InvalidPeptideError
-
- @staticmethod
- def _validate_mod_string(mod_string):
- """Validate modification string for MS²PIP."""
- pattern = r"^(?:(?:-1|[0-9]{1,2})\|(?:[^\|\s]+)\|?)*$|^-$"
- if not re.fullmatch(pattern, mod_string):
- raise InvalidModificationFormattingError(mod_string)
-
- @staticmethod
- def _transform(intensity):
- """Undo MS²PIP peak intensity log transformation and pseudo-count."""
- return (2 ** np.array(intensity)) - 0.001
-
- @staticmethod
- def _tic_normalize(intensity):
- """TIC-normalize peak intensities."""
- intensity = np.array(intensity)
- return intensity / np.sum(intensity)
-
- @staticmethod
- def _modifications_to_dict(modifications):
- """Convert ms2pip modification notation to spectrum_utils dict."""
-
- def parse_loc(loc):
- if loc == "0":
- return "N-term"
- elif loc == "-1":
- return "C-term"
- else:
- return int(loc) - 1
-
- m_split = [modifications.split("|")[i::2] for i in [0, 1]]
- mods_dict = {parse_loc(loc): name for loc, name in zip(m_split[0], m_split[1])}
- return mods_dict
-
- @staticmethod
- def _get_sus_annotation(mz, annotation):
- """Get spectrum_utils.PeptideFragmentAnnotation objects."""
- return [
- sus.PeptideFragmentAnnotation(1, mz, annotation[0], annotation[1:])
- for mz, annotation in zip(mz, annotation)
- ]
-
-
-@click.command()
-@click.argument("peptide", type=str)
-@click.argument("modifications", type=str)
-@click.argument("charge", type=int)
-@click.option("-m", "--model", type=str, default="HCD", help="")
-@click.option("-c", "--configfile", type=click.Path(exists=True), default=None, help="")
-@click.option("--model-dir", type=click.Path(), default=None, help="")
-@click.option(
- "-o",
- "--output",
- type=click.Path(exists=False),
- default="ms2pip_prediction.png",
- help="",
-)
-def _main(
- peptide,
- modifications,
- charge,
- model="HCD",
- configfile=None,
- model_dir=None,
- output="ms2pip_prediction.png",
-):
- """
- Generate MS²PIP-predicted spectrum and plot.
-
- \b
- Examples:
- - ms2pip-single-prediction PGAQANPYSR "-" 3
- - ms2pip-single-prediction -o prediction.png PGAQANPYSR "-" 3
- - ms2pip-single-prediction -c config.toml NSVPCSR "5|Carbamidomethyl" 3
-
- """
- root_logger = logging.getLogger()
- handler = logging.StreamHandler()
- root_logger.addHandler(handler)
- root_logger.setLevel(logging.INFO)
-
- if configfile:
- config_parser = ConfigParser(configfile)
- mod_strings = config_parser.config["ms2pip"]["ptm"]
- else:
- mod_strings = None
- ms2pip_sp = SinglePrediction(mod_strings, model_dir)
-
- _, ax = plt.subplots(figsize=(10, 5))
- prediction = ms2pip_sp.predict(peptide, modifications, charge, model=model)
- ms2pip_sp.plot_prediction(
- peptide,
- modifications,
- charge,
- prediction=prediction,
- ax=ax,
- filename=output,
- )
-
-
-if __name__ == "__main__":
- _main()
diff --git a/ms2pip/spectrum.py b/ms2pip/spectrum.py
index e90b6909..8e49a389 100644
--- a/ms2pip/spectrum.py
+++ b/ms2pip/spectrum.py
@@ -1,166 +1,190 @@
-"""Read MS2 spectra."""
+"""MS2 spectrum handling."""
-from pathlib import Path
-from typing import Generator
+from __future__ import annotations
+
+import warnings
+from typing import Any, Optional, Union
import numpy as np
-from pyteomics import mgf, mzml
-
-from ms2pip.exceptions import (
- EmptySpectrumError,
- InvalidSpectrumError,
- UnsupportedSpectrumFiletypeError,
-)
-
-
-class Spectrum:
- def __init__(
- self, title, msms, peaks, precursor_charge=None, precursor_mz=None
- ) -> None:
- """Minimal information on observed MS2 spectrum."""
- self.title = str(title)
- self.msms = np.array(msms, dtype=np.float32)
- self.peaks = np.array(peaks, dtype=np.float32)
- self.precursor_charge = int(precursor_charge) if precursor_charge else None
- self.precursor_mz = float(precursor_mz) if precursor_mz else None
-
- self.tic = np.sum(self.peaks)
-
- if len(self.msms) != len(self.peaks):
- raise InvalidSpectrumError(
- "Inconsistent number of m/z and intensity values."
- )
+from psm_utils import Peptidoform
+from pydantic import model_validator, field_validator, ConfigDict, BaseModel
+try:
+ import spectrum_utils.spectrum as sus
+ import spectrum_utils.plot as sup
+except ImportError:
+ sus = None
+ sup = None
+
+
+class Spectrum(BaseModel):
+ """MS2 spectrum."""
+
+ mz: np.ndarray
+ intensity: np.ndarray
+ annotations: Optional[np.ndarray] = None
+ identifier: Optional[str] = None
+ peptidoform: Optional[Union[Peptidoform, str]] = None
+ precursor_mz: Optional[float] = None
+ precursor_charge: Optional[int] = None
+ retention_time: Optional[float] = None
+ mass_tolerance: Optional[float] = None
+ mass_tolerance_unit: Optional[str] = None
+
+ model_config = ConfigDict(arbitrary_types_allowed=True)
+
+ def __init__(__pydantic_self__, **data: Any) -> None:
+ """
+ MS2 spectrum.
+
+ Parameters
+ ----------
+ mz
+ Array of m/z values.
+ intensity
+ Array of intensity values.
+ annotations
+ Array of peak annotations.
+ identifier
+ Spectrum identifier.
+ peptidoform
+ Peptidoform.
+ precursor_mz
+ Precursor m/z.
+ precursor_charge
+ Precursor charge.
+ retention_time
+ Retention time.
+ mass_tolerance
+ Mass tolerance for spectrum annotation.
+ mass_tolerance_unit
+ Unit of mass tolerance for spectrum annotation.
+
+ """
+ super().__init__(**data)
def __repr__(self) -> str:
return "{}.{}({})".format(
self.__class__.__module__,
self.__class__.__qualname__,
- f"title='{self.title}'",
+ f"identifier='{self.identifier}'",
)
- def validate_spectrum_content(self) -> None:
- """Raise EmptySpectrumError if no peaks are present."""
- if (len(self.peaks) == 0) or (len(self.msms) == 0):
- raise EmptySpectrumError()
+ @model_validator(mode="after")
+ @classmethod
+ def check_array_lengths(cls, data: dict):
+ if len(data.mz) != len(data.intensity):
+ raise ValueError("Array lengths do not match.")
+ if data.annotations is not None:
+ if len(data.annotations) != len(data.intensity):
+ raise ValueError("Array lengths do not match.")
+ return data
+
+ @field_validator("peptidoform")
+ @classmethod
+ def check_peptidoform(cls, value):
+ if not value or isinstance(value, Peptidoform):
+ return value
+ elif isinstance(value, str):
+ return Peptidoform(value)
+ else:
+ raise ValueError("Peptidoform must be a string, a Peptidoform object, or None.")
+
+ @property
+ def tic(self):
+ """Total ion current."""
+ return np.sum(self.intensity)
def remove_reporter_ions(self, label_type=None) -> None:
- """Remove reporter ions."""
+ """Set the intensity of reporter ions to 0."""
+ # TODO: Consider using the exact m/z values instead of a range.
if label_type == "iTRAQ":
- for mi, mp in enumerate(self.msms):
- if (mp >= 113) & (mp <= 118):
- self.peaks[mi] = 0
+ for i, mz in enumerate(self.mz):
+ if (mz >= 113) & (mz <= 118):
+ self.intensity[i] = 0
# TMT6plex: 126.1277, 127.1311, 128.1344, 129.1378, 130.1411, 131.1382
elif label_type == "TMT":
- for mi, mp in enumerate(self.msms):
- if (mp >= 125) & (mp <= 132):
- self.peaks[mi] = 0
+ for i, mz in enumerate(self.mz):
+ if (mz >= 125) & (mz <= 132):
+ self.intensity[i] = 0
def remove_precursor(self, tolerance=0.02) -> None:
- """Remove precursor peak."""
- for mi, mp in enumerate(self.msms):
- if (mp >= self.precursor_mz - tolerance) & (
- mp <= self.precursor_mz + tolerance
- ):
- self.peaks[mi] = 0
+ """Set the intensity of the precursor peak to 0."""
+ if not self.precursor_mz:
+ raise ValueError("Precursor m/z must be set.")
+ for i, mz in enumerate(self.mz):
+ if (mz >= self.precursor_mz - tolerance) & (mz <= self.precursor_mz + tolerance):
+ self.intensity[i] = 0
def tic_norm(self) -> None:
"""Normalize spectrum to total ion current."""
- self.peaks = self.peaks / self.tic
+ self.intensity = self.intensity / self.tic
def log2_transform(self) -> None:
"""Log2-tranform spectrum."""
- self.peaks = np.log2(self.peaks + 0.001)
-
-
-def read_mgf(spec_file) -> Generator[Spectrum, None, None]:
- """
- Read MGF file.
-
- Parameters
- ----------
- spec_file: str
- Path to MGF file.
-
- """
- with mgf.read(
- spec_file,
- convert_arrays=1,
- read_charges=False,
- read_ions=False,
- dtype=np.float32,
- use_index=False,
- ) as mgf_file:
- for spectrum in mgf_file:
- spec_id = spectrum["params"]["title"]
- peaks = spectrum["intensity array"]
- msms = spectrum["m/z array"]
- try:
- precursor_charge = spectrum["params"]["charge"][0]
- except KeyError:
- precursor_charge = None
- try:
- precursor_mz = spectrum["params"]["pepmass"][0]
- except KeyError:
- precursor_mz = None
- parsed_spectrum = Spectrum(
- spec_id, msms, peaks, precursor_charge, precursor_mz
+ self.intensity = np.log2(self.intensity + 0.001)
+
+ def clip_intensity(self, min_intensity=0.0) -> None:
+ """Clip intensity values."""
+ self.intensity = np.clip(self.intensity, min_intensity, None)
+
+ def to_spectrum_utils(self):
+ """
+ Convert to spectrum_utils.spectrum.MsmsSpectrum.
+
+ Notes
+ -----
+ - Requires spectrum_utils to be installed.
+ - If the ``precursor_mz`` or ``precursor_charge`` attributes are not set, the theoretical
+ m/z and precursor charge of the ``peptidoform`` attribute are used, if present.
+ Otherwise, ``ValueError`` is raised.
+
+ """
+ if not sus:
+ raise ImportError("Optional dependency spectrum_utils not installed.")
+
+ if self.precursor_charge:
+ precursor_charge = self.precursor_charge
+ else:
+ if not self.peptidoform:
+ raise ValueError("`precursor_charge` or `peptidoform` must be set.")
+ else:
+ precursor_charge = self.peptidoform.precursor_charge
+
+ if self.precursor_mz:
+ precursor_mz = self.precursor_mz
+ else:
+ if not self.peptidoform:
+ raise ValueError("`precursor_mz` or `peptidoform` must be set.")
+ else:
+ warnings.warn("precursor_mz not set, using theoretical precursor m/z.")
+ precursor_mz = self.peptidoform.theoretical_mz
+
+ spectrum = sus.MsmsSpectrum(
+ identifier=self.identifier if self.identifier else "spectrum",
+ precursor_mz=precursor_mz,
+ precursor_charge=precursor_charge,
+ mz=self.mz,
+ intensity=self.intensity,
+ retention_time=self.retention_time,
+ )
+ if self.peptidoform:
+ spectrum.annotate_proforma(
+ str(self.peptidoform), self.mass_tolerance, self.mass_tolerance_unit
)
- yield parsed_spectrum
-
-
-def read_mzml(spec_file) -> Generator[Spectrum, None, None]:
- """
- Read mzML file.
-
- Parameters
- ----------
- spec_file: str
- Path to mzML file.
-
- """
- with mzml.read(
- spec_file,
- read_schema=False,
- iterative=True,
- use_index=False,
- dtype=np.float32,
- ) as mzml_file:
- for spectrum in mzml_file:
- if spectrum["ms level"] == 2:
- spec_id = spectrum["id"]
- peaks = spectrum["intensity array"]
- msms = spectrum["m/z array"]
- precursor = spectrum["precursorList"]["precursor"][0][
- "selectedIonList"
- ]["selectedIon"][0]
- precursor_mz = precursor["selected ion m/z"]
- precursor_charge = precursor["charge state"]
- parsed_spectrum = Spectrum(
- spec_id, msms, peaks, precursor_charge, precursor_mz
- )
- yield parsed_spectrum
-
-
-def read_spectrum_file(spec_file) -> Generator[Spectrum, None, None]:
- """
- Read MGF or mzML file; infer type from filename extension.
-
- Parameters
- ----------
- spec_file: str
- Path to mzML file.
- peptide_titles: list[str], optional
- List with peptide `spec_id` values which correspond to mzML spectrum id
- values.
-
- """
- filetype = Path(spec_file).suffix.lower()
- if filetype == ".mzml":
- for spectrum in read_mzml(spec_file):
- yield spectrum
- elif filetype == ".mgf":
- for spectrum in read_mgf(spec_file):
- yield spectrum
- else:
- raise UnsupportedSpectrumFiletypeError(filetype)
+ return spectrum
+
+
+class ObservedSpectrum(Spectrum):
+ """Observed MS2 spectrum."""
+
+ pass
+
+
+class PredictedSpectrum(Spectrum):
+ """Predicted MS2 spectrum."""
+
+ mass_tolerance: Optional[float] = 0.001
+ mass_tolerance_unit: Optional[str] = "Da"
+
+ pass
diff --git a/ms2pip/spectrum_input.py b/ms2pip/spectrum_input.py
new file mode 100644
index 00000000..beca7c2a
--- /dev/null
+++ b/ms2pip/spectrum_input.py
@@ -0,0 +1,64 @@
+"""Read MS2 spectra."""
+
+from pathlib import Path
+from typing import Generator
+
+import numpy as np
+from ms2rescore_rs import get_ms2_spectra
+
+from ms2pip.exceptions import UnsupportedSpectrumFiletypeError
+from ms2pip.spectrum import ObservedSpectrum
+
+
+def read_spectrum_file(spectrum_file: str) -> Generator[ObservedSpectrum, None, None]:
+ """
+ Read MS2 spectra from a supported file format; inferring the type from the filename extension.
+
+ Parameters
+ ----------
+ spectrum_file
+ Path to MGF or mzML file.
+
+ Yields
+ ------
+ ObservedSpectrum
+
+ Raises
+ ------
+ UnsupportedSpectrumFiletypeError
+ If the file extension is not supported.
+
+ """
+ file_extension = Path(spectrum_file).suffix.lower()
+ if file_extension not in [".mgf", ".mzml", ".d"] and not _is_minitdf(spectrum_file):
+ raise UnsupportedSpectrumFiletypeError(file_extension)
+
+ for spectrum in get_ms2_spectra(str(spectrum_file)):
+ obs_spectrum = ObservedSpectrum(
+ mz=np.array(spectrum.mz, dtype=np.float32),
+ intensity=np.array(spectrum.intensity, dtype=np.float32),
+ identifier=str(spectrum.identifier),
+ precursor_mz=float(spectrum.precursor.mz),
+ precursor_charge=float(spectrum.precursor.charge),
+ retention_time=float(spectrum.precursor.rt),
+ )
+ # Workaround for mobiusklein/mzdata#3
+ if (
+ obs_spectrum.identifier == ""
+ or obs_spectrum.mz.shape[0] == 0
+ or obs_spectrum.intensity.shape[0] == 0
+ ):
+ continue
+ yield obs_spectrum
+
+
+def _is_minitdf(spectrum_file: str) -> bool:
+ """
+ Check if the spectrum file is a Bruker miniTDF folder.
+
+ A Bruker miniTDF folder has no fixed name, but contains files matching the patterns
+ ``*ms2spectrum.bin`` and ``*ms2spectrum.parquet``.
+ """
+ files = set(Path(spectrum_file).glob("*ms2spectrum.bin"))
+ files.update(Path(spectrum_file).glob("*ms2spectrum.parquet"))
+ return len(files) >= 2
diff --git a/ms2pip/spectrum_output.py b/ms2pip/spectrum_output.py
new file mode 100644
index 00000000..fc174612
--- /dev/null
+++ b/ms2pip/spectrum_output.py
@@ -0,0 +1,790 @@
+"""
+Write spectrum files from MS²PIP prediction results.
+
+
+Examples
+--------
+
+The simplest way to write MS²PIP predictions to a file is to use the :py:func:`write_spectra`
+function:
+
+>>> from ms2pip import predict_single, write_spectra
+>>> results = [predict_single("ACDE/2")]
+>>> write_spectra("/path/to/output/filename", results, "mgf")
+
+Specific writer classes can also be used directly. Writer classes should be used in a context
+manager to ensure the file is properly closed after writing. The following example writes MS²PIP
+predictions to a TSV file:
+
+>>> from ms2pip import predict_single
+>>> results = [predict_single("ACDE/2")]
+>>> with TSV("output.tsv") as writer:
+... writer.write(results)
+
+Results can be written to the same file sequentially:
+
+>>> results_2 = [predict_single("PEPTIDEK/2")]
+>>> with TSV("output.tsv", write_mode="a") as writer:
+... writer.write(results)
+... writer.write(results_2)
+
+Results can be written to an existing file using the append mode:
+
+>>> with TSV("output.tsv", write_mode="a") as writer:
+... writer.write(results_2)
+
+
+"""
+
+from __future__ import annotations
+
+import csv
+import itertools
+import logging
+import re
+import warnings
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from io import StringIO
+from pathlib import Path
+from time import localtime, strftime
+from typing import Any, Dict, Generator, List, Optional, Union
+
+import numpy as np
+from psm_utils import PSM, Peptidoform
+from pyteomics import proforma
+from sqlalchemy import engine, select
+
+from ms2pip._utils import dlib
+from ms2pip.result import ProcessingResult
+
+LOGGER = logging.getLogger(__name__)
+
+
+def write_spectra(
+ filename: Union[str, Path],
+ processing_results: List[ProcessingResult],
+ file_format: str = "tsv",
+ write_mode: str = "w",
+):
+ """
+ Write MS2PIP processing results to a supported spectrum file format.
+
+ Parameters
+ ----------
+ filename
+ Output filename without file extension.
+ processing_results
+ List of :py:class:`ms2pip.result.ProcessingResult` objects.
+ file_format
+ File format to write. See :py:attr:`FILE_FORMATS` for available formats.
+ write_mode
+ Write mode for file. Default is ``w`` (write). Use ``a`` (append) to add to existing file.
+
+ """
+ with SUPPORTED_FORMATS[file_format](filename, write_mode) as writer:
+ LOGGER.info(f"Writing to {writer.filename}")
+ writer.write(processing_results)
+
+
+class _Writer(ABC):
+ """Abstract base class for writing spectrum files."""
+
+ suffix = ""
+
+ def __init__(self, filename: Union[str, Path], write_mode: str = "w"):
+ self.filename = Path(filename).with_suffix(self.suffix)
+ self.write_mode = write_mode
+
+ self._open_file = None
+
+ def __enter__(self):
+ """Open file in context manager."""
+ self.open()
+ return self
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ """Close file in context manager."""
+ self.close()
+
+ def __repr__(self):
+ return f"{self.__class__.__name__}({self.filename, self.write_mode})"
+
+ def open(self):
+ """Open file."""
+ if self._open_file:
+ self.close()
+ self._open_file = open(self.filename, self.write_mode)
+
+ def close(self):
+ """Close file."""
+ if self._open_file:
+ self._open_file.close()
+ self._open_file = None
+
+ @property
+ def _file_object(self):
+ """Get open file object."""
+ if self._open_file:
+ return self._open_file
+ else:
+ warnings.warn(
+ "Opening file outside of context manager. Manually close file after use."
+ )
+ self.open()
+ return self._open_file
+
+ def write(self, processing_results: List[ProcessingResult]):
+ """Write multiple processing results to file."""
+ for result in processing_results:
+ self._write_result(result)
+
+ @abstractmethod
+ def _write_result(self, result: ProcessingResult):
+ """Write single processing result to file."""
+ ...
+
+
+class TSV(_Writer):
+ """Write TSV files from MS2PIP processing results."""
+
+ suffix = ".tsv"
+ field_names = [
+ "psm_index",
+ "ion_type",
+ "ion_number",
+ "mz",
+ "predicted",
+ "observed",
+ "rt",
+ "im",
+ ]
+
+ def write(self, processing_results: List[ProcessingResult]):
+ """Write multiple processing results to file."""
+ writer = csv.DictWriter(
+ self._file_object, fieldnames=self.field_names, delimiter="\t", lineterminator="\n"
+ )
+ if self.write_mode == "w":
+ writer.writeheader()
+ for result in processing_results:
+ self._write_result(result, writer)
+
+ def _write_result(self, result: ProcessingResult, writer: csv.DictWriter):
+ """Write single processing result to file."""
+ # Only write results with predictions or observations
+ if not result.theoretical_mz:
+ return
+
+ for ion_type in result.theoretical_mz:
+ for i in range(len(result.theoretical_mz[ion_type])):
+ writer.writerow(self._write_row(result, ion_type, i))
+
+ @staticmethod
+ def _write_row(result: ProcessingResult, ion_type: str, ion_index: int):
+ """Write single row for TSV file."""
+ return {
+ "psm_index": result.psm_index,
+ "ion_type": ion_type,
+ "ion_number": ion_index + 1,
+ "mz": "{:.8f}".format(result.theoretical_mz[ion_type][ion_index]),
+ "predicted": "{:.8f}".format(result.predicted_intensity[ion_type][ion_index])
+ if result.predicted_intensity
+ else None,
+ "observed": "{:.8f}".format(result.observed_intensity[ion_type][ion_index])
+ if result.observed_intensity
+ else None,
+ "rt": result.psm.retention_time if result.psm.retention_time else None,
+ "im": result.psm.ion_mobility if result.psm.ion_mobility else None,
+ }
+
+
+class MSP(_Writer):
+ """Write MSP files from MS2PIP processing results."""
+
+ suffix = ".msp"
+
+ def write(self, results: List[ProcessingResult]):
+ """Write multiple processing results to file."""
+ for result in results:
+ self._write_result(result)
+
+ def _write_result(self, result: ProcessingResult):
+ """Write single processing result to file."""
+ predicted_spectrum = result.as_spectra()[0]
+ intensity_normalized = _basepeak_normalize(predicted_spectrum.intensity) * 1e4
+ peaks = zip(predicted_spectrum.mz, intensity_normalized, predicted_spectrum.annotations)
+
+ # Header
+ lines = [
+ f"Name: {result.psm.peptidoform.sequence}/{result.psm.get_precursor_charge()}",
+ f"MW: {result.psm.peptidoform.theoretical_mass}",
+ self._format_comment_line(result.psm),
+ f"Num peaks: {len(predicted_spectrum.mz)}",
+ ]
+
+ # Peaks
+ lines.extend(
+ f"{mz:.8f}\t{intensity:.8f}\t{annotation}/0.0" for mz, intensity, annotation in peaks
+ )
+
+ # Write to file
+ self._file_object.writelines(line + "\n" for line in lines)
+ self._file_object.write("\n")
+
+ @staticmethod
+ def _format_modifications(peptidoform: Peptidoform):
+ """Format modifications in MSP-style string, e.g. ``Mods=1/0,E,Glu->pyro-Glu``."""
+
+ def _format_single_modification(
+ amino_acid: str,
+ position: int,
+ modifications: Optional[List[proforma.ModificationBase]],
+ ) -> Union[str, None]:
+ """Get modification label from :py:class:`proforma.ModificationBase` list."""
+ if not modifications:
+ return None
+ if len(modifications) > 1:
+ raise ValueError("Multiple modifications per amino acid not supported in MSP.")
+ modification = modifications[0]
+ try:
+ return f"{position},{amino_acid},{modification.name}"
+ except AttributeError: # MassModification has no attribute `name`
+ return f"{position},{amino_acid},{modification.value}"
+
+ sequence_mods = [
+ _format_single_modification(aa, pos + 1, mods)
+ for pos, (aa, mods) in enumerate(peptidoform.parsed_sequence)
+ ]
+ n_term = _format_single_modification(
+ peptidoform.sequence[0], 0, peptidoform.properties["n_term"]
+ )
+ c_term = _format_single_modification(
+ peptidoform.sequence[-1], -1, peptidoform.properties["c_term"]
+ )
+
+ mods = [mod for mod in [n_term] + sequence_mods + [c_term] if mod is not None]
+
+ if not mods:
+ return "Mods=0"
+ else:
+ return f"Mods={len(mods)}/{'/'.join(mods)}"
+
+ @staticmethod
+ def _format_parent_mass(peptidoform: Peptidoform) -> str:
+ """Format parent mass as string."""
+ return f"Parent={peptidoform.theoretical_mz}"
+
+ @staticmethod
+ def _format_protein_string(psm: PSM) -> Union[str, None]:
+ """Format protein list as string."""
+ if psm.protein_list:
+ return f"Protein={','.join(psm.protein_list)}"
+ else:
+ return None
+
+ @staticmethod
+ def _format_retention_time(psm: PSM) -> Union[str, None]:
+ """Format retention time as string."""
+ if psm.retention_time:
+ return f"RetentionTime={psm.retention_time}"
+ else:
+ return None
+
+ @staticmethod
+ def _format_ion_mobility(psm: PSM) -> Union[str, None]:
+ """Format ion mobility as string."""
+ if psm.ion_mobility:
+ return f"IonMobility={psm.ion_mobility}"
+ else:
+ return None
+
+ @staticmethod
+ def _format_identifier(psm: PSM) -> str:
+ """Format MS2PIP ID as string."""
+ return f"SpectrumIdentifier={psm.spectrum_id}"
+
+ @staticmethod
+ def _format_comment_line(psm: PSM) -> str:
+ """Format comment line for MSP file."""
+ comments = " ".join(
+ filter(
+ None,
+ [
+ MSP._format_modifications(psm.peptidoform),
+ MSP._format_parent_mass(psm.peptidoform),
+ MSP._format_protein_string(psm),
+ MSP._format_retention_time(psm),
+ MSP._format_ion_mobility(psm),
+ MSP._format_identifier(psm),
+ ],
+ )
+ )
+ return f"Comment: {comments}"
+
+
+class MGF(_Writer):
+ """
+ Write MGF files from MS2PIP processing results.
+
+ See http://www.matrixscience.com/help/data_file_help.html for documentation on the MGF format.
+ """
+
+ suffix = ".mgf"
+
+ def write(self, results: List[ProcessingResult]):
+ """Write multiple processing results to file."""
+ for result in results:
+ self._write_result(result)
+
+ def _write_result(self, result: ProcessingResult):
+ """Write single processing result to file."""
+ predicted_spectrum = result.as_spectra()[0]
+ intensity_normalized = _basepeak_normalize(predicted_spectrum.intensity) * 1e4
+ peaks = zip(predicted_spectrum.mz, intensity_normalized)
+
+ # Header
+ lines = [
+ "BEGIN IONS",
+ f"TITLE={result.psm.peptidoform}",
+ f"PEPMASS={result.psm.peptidoform.theoretical_mz}",
+ f"CHARGE={result.psm.get_precursor_charge()}+",
+ f"SCANS={result.psm.spectrum_id}",
+ f"RTINSECONDS={result.psm.retention_time}" if result.psm.retention_time else None,
+ f"ION_MOBILITY={result.psm.ion_mobility}" if result.psm.ion_mobility else None,
+ ]
+
+ # Peaks
+ lines.extend(f"{mz:.8f} {intensity:.8f}" for mz, intensity in peaks)
+
+ # Write to file
+ self._file_object.writelines(line + "\n" for line in lines if line)
+ self._file_object.write("END IONS\n\n")
+
+
+class Spectronaut(_Writer):
+ """Write Spectronaut files from MS2PIP processing results."""
+
+ suffix = ".spectronaut.tsv"
+ field_names = [
+ "ModifiedPeptide",
+ "StrippedPeptide",
+ "PrecursorCharge",
+ "PrecursorMz",
+ "IonMobility",
+ "iRT",
+ "ProteinId",
+ "RelativeFragmentIntensity",
+ "FragmentMz",
+ "FragmentType",
+ "FragmentNumber",
+ "FragmentCharge",
+ "FragmentLossType",
+ ]
+
+ def write(self, processing_results: List[ProcessingResult]):
+ """Write multiple processing results to file."""
+ writer = csv.DictWriter(
+ self._file_object, fieldnames=self.field_names, delimiter="\t", lineterminator="\n"
+ )
+ if self.write_mode == "w":
+ writer.writeheader()
+ for result in processing_results:
+ self._write_result(result, writer)
+
+ def _write_result(self, result: ProcessingResult, writer: csv.DictWriter):
+ """Write single processing result to file."""
+ # Only write results with predictions
+ if result.predicted_intensity is None:
+ return
+ psm_info = self._process_psm(result.psm)
+ for fragment_info in self._yield_fragment_info(result):
+ writer.writerow({**psm_info, **fragment_info})
+
+ @staticmethod
+ def _process_psm(psm: PSM) -> Dict[str, Any]:
+ """Process PSM to Spectronaut format."""
+ return {
+ "ModifiedPeptide": _peptidoform_str_without_charge(psm.peptidoform),
+ "StrippedPeptide": psm.peptidoform.sequence,
+ "PrecursorCharge": psm.get_precursor_charge(),
+ "PrecursorMz": f"{psm.peptidoform.theoretical_mz:.8f}",
+ "IonMobility": f"{psm.ion_mobility:.8f}" if psm.ion_mobility else None,
+ "iRT": f"{psm.retention_time:.8f}" if psm.retention_time else None,
+ "ProteinId": "".join(psm.protein_list) if psm.protein_list else None,
+ }
+
+ @staticmethod
+ def _yield_fragment_info(result: ProcessingResult) -> Generator[Dict[str, Any], None, None]:
+ """Yield fragment information for a processing result."""
+ # Normalize intensities
+ intensities = {
+ ion_type: _unlogarithmize(intensities)
+ for ion_type, intensities in result.predicted_intensity.items()
+ }
+ max_intensity = max(itertools.chain(*intensities.values()))
+ intensities = {
+ ion_type: _basepeak_normalize(intensities[ion_type], basepeak=max_intensity)
+ for ion_type in intensities
+ }
+ for ion_type in result.predicted_intensity:
+ fragment_type = ion_type[0].lower()
+ fragment_charge = ion_type[1:] if len(ion_type) > 1 else "1"
+ for ion_index, (intensity, mz) in enumerate(
+ zip(intensities[ion_type], result.theoretical_mz[ion_type])
+ ):
+ yield {
+ "RelativeFragmentIntensity": f"{intensity:.8f}",
+ "FragmentMz": f"{mz:.8f}",
+ "FragmentType": fragment_type,
+ "FragmentNumber": ion_index + 1,
+ "FragmentCharge": fragment_charge,
+ "FragmentLossType": "noloss",
+ }
+
+
+class Bibliospec(_Writer):
+ """
+ Write Bibliospec SSL and MS2 files from MS2PIP processing results.
+
+ Bibliospec SSL and MS2 files are also compatible with Skyline. See
+ https://skyline.ms/wiki/home/software/BiblioSpec/page.view?name=BiblioSpec%20input%20and%20output%20file%20formats
+ for documentation on the Bibliospec file formats.
+
+ """
+
+ ssl_suffix = ".ssl"
+ ms2_suffix = ".ms2"
+ ssl_field_names = [
+ "file",
+ "scan",
+ "charge",
+ "sequence",
+ "score-type",
+ "score",
+ "retention-time",
+ "ion-mobility",
+ ]
+
+ def __init__(self, filename: Union[str, Path], write_mode: str = "w"):
+ super().__init__(filename, write_mode)
+ self.ssl_file = self.filename.with_suffix(self.ssl_suffix)
+ self.ms2_file = self.filename.with_suffix(self.ms2_suffix)
+
+ self._open_ssl_file = None
+ self._open_ms2_file = None
+
+ def open(self):
+ """Open files."""
+ self._open_ssl_file = open(self.ssl_file, self.write_mode)
+ self._open_ms2_file = open(self.ms2_file, self.write_mode)
+
+ def close(self):
+ """Close files."""
+ if self._open_ssl_file:
+ self._open_ssl_file.close()
+ self._open_ssl_file = None
+ if self._open_ms2_file:
+ self._open_ms2_file.close()
+ self._open_ms2_file = None
+
+ @property
+ def _ssl_file_object(self):
+ """Get open SSL file object."""
+ if self._open_ssl_file:
+ return self._open_ssl_file
+ else:
+ warnings.warn(
+ "Opening file outside of context manager. Manually close file after use."
+ )
+ self.open()
+ return self._open_ssl_file
+
+ @property
+ def _ms2_file_object(self):
+ """Get open MS2 file object."""
+ if self._open_ms2_file:
+ return self._open_ms2_file
+ else:
+ warnings.warn(
+ "Opening file outside of context manager. Manually close file after use."
+ )
+ self.open()
+ return self._open_ms2_file
+
+ def write(self, processing_results: List[ProcessingResult]):
+ """Write multiple processing results to file."""
+ # Create CSV writer
+ ssl_dict_writer = csv.DictWriter(
+ self._ssl_file_object,
+ fieldnames=self.ssl_field_names,
+ delimiter="\t",
+ lineterminator="\n",
+ )
+
+ # Write headers
+ if self.write_mode == "w":
+ ssl_dict_writer.writeheader()
+ self._write_ms2_header()
+ start_scan_number = 0
+ elif self.write_mode == "a":
+ start_scan_number = self._get_last_ssl_scan_number(self.ssl_file) + 1
+ else:
+ raise ValueError(f"Unsupported write mode: {self.write_mode}")
+
+ # Write results
+ for i, result in enumerate(processing_results):
+ scan_number = start_scan_number + i
+ modified_sequence = self._format_modified_sequence(result.psm.peptidoform)
+ self._write_result(result, modified_sequence, scan_number, ssl_dict_writer)
+
+ def _write_ms2_header(self):
+ """Write header to MS2 file."""
+ self._ms2_file_object.write(
+ f"H\tCreationDate\t{strftime('%Y-%m-%d %H:%M:%S', localtime())}\n"
+ )
+ self._ms2_file_object.write("H\tExtractor\tMS2PIP predictions\n")
+
+ def _write_result(
+ self,
+ result: ProcessingResult,
+ modified_sequence: str,
+ scan_number: int,
+ writer: csv.DictWriter,
+ ):
+ """Write single processing result to files."""
+ self._write_result_to_ssl(result, modified_sequence, scan_number, writer)
+ self._write_result_to_ms2(result, modified_sequence, scan_number)
+
+ def _write_result_to_ssl(
+ self,
+ result: ProcessingResult,
+ modified_sequence: str,
+ scan_number: int,
+ writer: csv.DictWriter,
+ ):
+ """Write single processing result to the SSL file."""
+ writer.writerow(
+ {
+ "file": self.ms2_file.name if isinstance(self.ms2_file, Path) else "file.ms2",
+ "scan": scan_number,
+ "charge": result.psm.get_precursor_charge(),
+ "sequence": modified_sequence,
+ "score-type": None,
+ "score": None,
+ "retention-time": result.psm.retention_time if result.psm.retention_time else None,
+ "ion-mobility": result.psm.ion_mobility if result.psm.ion_mobility else None,
+ }
+ )
+
+ def _write_result_to_ms2(
+ self, result: ProcessingResult, modified_sequence: str, scan_number: int
+ ):
+ """Write single processing result to the MS2 file."""
+ predicted_spectrum = result.as_spectra()[0]
+ intensity_normalized = _basepeak_normalize(predicted_spectrum.intensity) * 1e4
+ peaks = zip(predicted_spectrum.mz, intensity_normalized)
+
+ # Header
+ lines = [
+ f"S\t{scan_number}\t{result.psm.peptidoform.theoretical_mz}",
+ f"Z\t{result.psm.get_precursor_charge()}\t{result.psm.peptidoform.theoretical_mass}",
+ f"D\tseq\t{result.psm.peptidoform.sequence}",
+ f"D\tmodified seq\t{modified_sequence}",
+ ]
+
+ # Peaks
+ lines.extend(f"{mz:.8f}\t{intensity:.8f}" for mz, intensity in peaks)
+
+ # Write to file
+ self._ms2_file_object.writelines(line + "\n" for line in lines)
+ self._ms2_file_object.write("\n")
+
+ @staticmethod
+ def _format_modified_sequence(peptidoform: Peptidoform) -> str:
+ """Format modified sequence as string for Spectronaut."""
+ modification_dict = defaultdict(list)
+ for term, position in [("n_term", 0), ("c_term", len(peptidoform) - 1)]:
+ if peptidoform.properties[term]:
+ modification_dict[position].extend(peptidoform.properties[term])
+ for position, (_, mods) in enumerate(peptidoform.parsed_sequence):
+ if mods:
+ modification_dict[position].extend(mods)
+ return "".join(
+ [
+ f"{aa}{''.join([f'[{mod.mass:+.1f}]' for mod in modification_dict[position]])}"
+ for position, aa in enumerate(peptidoform.sequence)
+ ]
+ )
+
+ @staticmethod
+ def _get_last_ssl_scan_number(ssl_file: Union[str, Path, StringIO]):
+ """Read scan number of last line in a Bibliospec SSL file."""
+ if isinstance(ssl_file, StringIO):
+ ssl_file.seek(0)
+ for line in ssl_file:
+ last_line = line
+ elif isinstance(ssl_file, (str, Path)):
+ with open(ssl_file, "rt") as ssl:
+ for line in ssl:
+ last_line = line
+ else:
+ raise TypeError("Unsupported type for `ssl_file`.")
+ return int(last_line.split("\t")[1])
+
+
+class DLIB(_Writer):
+ """
+ Write DLIB files from MS2PIP processing results.
+
+ See `EncyclopeDIA File Formats `_
+ for documentation on the DLIB format.
+
+ """
+
+ suffix = ".dlib"
+
+ def open(self):
+ """Open file."""
+ if self.write_mode == "w":
+ self._open_file = self.filename.unlink(missing_ok=True)
+ self._open_file = dlib.open_sqlite(self.filename)
+
+ def write(self, processing_results: List[ProcessingResult]):
+ """Write MS2PIP predictions to a DLIB SQLite file."""
+ connection = self._file_object
+ dlib.metadata.create_all()
+ self._write_metadata(connection)
+ self._write_entries(processing_results, connection, self.filename)
+ self._write_peptide_to_protein(processing_results, connection)
+
+ def _write_result(self, result: ProcessingResult): ...
+
+ @staticmethod
+ def _format_modified_sequence(peptidoform: Peptidoform) -> str:
+ """Format modified sequence as string for DLIB."""
+ # Sum all sequential mass shifts for each position
+ masses = [
+ sum(mod.mass for mod in mods) if mods else 0 for _, mods in peptidoform.parsed_sequence
+ ]
+
+ # Add N- and C-terminal modifications
+ for term, position in [("n_term", 0), ("c_term", len(peptidoform) - 1)]:
+ if peptidoform.properties[term]:
+ masses[position] += sum(mod.mass for mod in peptidoform.properties[term])
+
+ # Format modified sequence
+ return "".join(
+ [
+ f"{aa}[{mass:+.6f}]" if mass else aa
+ for aa, mass in zip(peptidoform.sequence, masses)
+ ]
+ )
+
+ @staticmethod
+ def _write_metadata(connection: engine.Connection):
+ """Write metadata to DLIB SQLite file."""
+ with connection.begin():
+ version = connection.execute(
+ select([dlib.Metadata.c.Value]).where(dlib.Metadata.c.Key == "version")
+ ).scalar()
+ if version is None:
+ connection.execute(
+ dlib.Metadata.insert().values(
+ Key="version",
+ Value=dlib.DLIB_VERSION,
+ )
+ )
+
+ @staticmethod
+ def _write_entries(
+ processing_results: List[ProcessingResult],
+ connection: engine.Connection,
+ output_filename: str,
+ ):
+ """Write spectra to DLIB SQLite file."""
+ with connection.begin():
+ for result in processing_results:
+ if not result.psm.retention_time:
+ raise ValueError("Retention time required to write DLIB file.")
+
+ spectrum = result.as_spectra()[0]
+ intensity_normalized = _basepeak_normalize(spectrum.intensity) * 1e4
+ n_peaks = len(spectrum.mz)
+
+ connection.execute(
+ dlib.Entry.insert().values(
+ PrecursorMz=result.psm.peptidoform.theoretical_mz,
+ PrecursorCharge=result.psm.get_precursor_charge(),
+ PeptideModSeq=DLIB._format_modified_sequence(result.psm.peptidoform),
+ PeptideSeq=result.psm.peptidoform.sequence,
+ Copies=1,
+ RTInSeconds=result.psm.retention_time,
+ Score=0,
+ MassEncodedLength=n_peaks,
+ MassArray=spectrum.mz.tolist(),
+ IntensityEncodedLength=n_peaks,
+ IntensityArray=intensity_normalized.tolist(),
+ SourceFile=str(output_filename),
+ )
+ )
+
+ @staticmethod
+ def _write_peptide_to_protein(results: List[ProcessingResult], connection: engine.Connection):
+ """Write peptide-to-protein mappings to DLIB SQLite file."""
+ peptide_to_proteins = {
+ (result.psm.peptidoform.sequence, protein)
+ for result in results
+ if result.psm.protein_list
+ for protein in result.psm.protein_list
+ }
+
+ with connection.begin():
+ sql_peptide_to_proteins = set()
+ proteins = {protein for _, protein in peptide_to_proteins}
+ for peptide_to_protein in connection.execute(
+ dlib.PeptideToProtein.select().where(
+ dlib.PeptideToProtein.c.ProteinAccession.in_(proteins)
+ )
+ ):
+ sql_peptide_to_proteins.add(
+ (
+ peptide_to_protein.PeptideSeq,
+ peptide_to_protein.ProteinAccession,
+ )
+ )
+
+ peptide_to_proteins.difference_update(sql_peptide_to_proteins)
+ for seq, protein in peptide_to_proteins:
+ connection.execute(
+ dlib.PeptideToProtein.insert().values(
+ PeptideSeq=seq, isDecoy=False, ProteinAccession=protein
+ )
+ )
+
+
+SUPPORTED_FORMATS = {
+ "tsv": TSV,
+ "msp": MSP,
+ "mgf": MGF,
+ "spectronaut": Spectronaut,
+ "bibliospec": Bibliospec,
+ "dlib": DLIB,
+}
+
+
+def _peptidoform_str_without_charge(peptidoform: Peptidoform) -> str:
+ """Get peptidoform string without charge."""
+ return re.sub(r"\/\d+$", "", str(peptidoform))
+
+
+def _unlogarithmize(intensities: np.array) -> np.array:
+ """Undo logarithmic transformation of intensities."""
+ return (2**intensities) - 0.001
+
+
+def _basepeak_normalize(intensities: np.array, basepeak: Optional[float] = None) -> np.array:
+ """Normalize intensities to most intense peak."""
+ if not basepeak:
+ basepeak = intensities.max()
+ return intensities / basepeak
diff --git a/ms2pip/sqldb/tables.py b/ms2pip/sqldb/tables.py
deleted file mode 100644
index 0cae8328..00000000
--- a/ms2pip/sqldb/tables.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import sqlalchemy
-from sqlalchemy import (MetaData, Table, Column, Integer, String, Float,
- ForeignKey)
-from sqlalchemy.types import ARRAY
-
-
-def create_engine(engine_uri):
- engine = sqlalchemy.create_engine(engine_uri)
- metadata.bind = engine
- return engine
-
-
-metadata = MetaData()
-
-# TODO: filename + path unique
-specfile = Table('specfile', metadata,
- Column('id', Integer, primary_key=True),
- Column('filename', String, nullable=False),
- Column('path', String, nullable=False))
-
-spec = Table('spec', metadata,
- Column('specfile_id', Integer, ForeignKey('specfile.id'), primary_key=True),
- Column('spec_id', String, primary_key=True),
- Column('pepmass', Float, index=True, nullable=False),
- Column('mzs', ARRAY(Float), nullable=False))
diff --git a/pyproject.toml b/pyproject.toml
index 2c6a515c..ce3540ea 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,37 +1,100 @@
-[tool.poetry]
+[project]
name = "ms2pip"
-version = "3.11.0"
-description = "MS²PIP: MS² Peak Intensity Prediction"
-authors = ["Sven Degroeve", "Ralf Gabriels", "Kevin Velghe", "Ana Sílvia C. Silva", "Arthur Declercq"]
+description = "MS2PIP: Accurate and versatile peptide fragmentation spectrum prediction."
+readme = "README.rst"
+license = { file = "LICENSE" }
+dynamic = ["version"]
+keywords = [
+ "MS2PIP",
+ "proteomics",
+ "peptide fragmentation",
+ "peak intensity prediction",
+ "spectrum prediction",
+ "machine learning",
+ "spectral library",
+ "fasta2speclib",
+]
+authors = [
+ { name = "Ralf Gabriels", email = "ralf@gabriels.dev" },
+ { name = "Sven Degroeve", email = "sven.degroeve@ugent.be" },
+ { name = "Arthur Declercq", email = "arthur.declercq@ugent.be" },
+ { name = "Kevin Velghe", email = "kevin.velghe@ugent.be" },
+ { name = "Ana Sílvia C. Silva" },
+]
+classifiers = [
+ "Intended Audience :: Science/Research",
+ "License :: OSI Approved :: Apache Software License",
+ "Operating System :: POSIX :: Linux",
+ "Operating System :: MacOS :: MacOS X",
+ "Operating System :: Microsoft :: Windows",
+ "Programming Language :: Python :: 3 :: Only",
+ "Topic :: Scientific/Engineering :: Bio-Informatics",
+ "Development Status :: 5 - Production/Stable",
+]
+requires-python = ">=3.8"
+dependencies = [
+ "numpy>=1.16,<2",
+ "pandas>=1,<2",
+ "pyarrow",
+ "pyteomics>=3.5,<5",
+ "tomlkit>=0.5,<1",
+ "sqlalchemy>=1.3,<2",
+ "click>=7,<9",
+ "xgboost>=1.3,<2",
+ "lxml>=4",
+ "rich>=13",
+ "pydantic>=2",
+ "werkzeug>=2",
+ "psm_utils>=0.9",
+ "ms2rescore-rs",
+]
+
+[project.optional-dependencies]
+plotting = ["matplotlib>=3.0", "spectrum-utils>=0.4"]
+dev = ["black", "isort>5", "pytest"]
+docs = [
+ "sphinx",
+ "numpydoc>=1,<2",
+ "recommonmark",
+ "toml",
+ "semver>=2",
+ "sphinx_rtd_theme",
+ "sphinx-autobuild",
+ "sphinx-automodapi",
+ "sphinx-click",
+]
+
+[project.urls]
+documentation = "http://compomics.github.io/projects/ms2pip"
+source = "https://github.com/compomics/ms2pip"
+tracker = "https://github.com/compomics/ms2pip/issues"
+webserver = "https://iomics.ugent.be/ms2pip/"
+publication = "https://doi.org/10.1093/nar/gkad335/"
+
+[project.scripts]
+ms2pip = "ms2pip.__main__:main"
[build-system]
requires = ["setuptools", "cython", "oldest-supported-numpy"]
build-backend = "setuptools.build_meta"
-[tool.poetry.dependencies]
-python = "^3.7"
-biopython = ">=1.74,<2"
-numpy = ">=1.16,<2"
-pandas = ">=1,<2"
-pyteomics = ">=3.5,<5"
-tqdm = ">=4,<5"
-tables = ">=3.4"
-tomlkit = ">=0.5.11,<1"
-sqlalchemy = "^1.3.13"
-psycopg2 = "^2.8.4"
-deeplc = "^0.1.14"
-xgboost = "^1.3"
-matplotlib = "^3.0"
-spectrum_utils = "^0.3.5"
-click = ">=7,<9"
-lxml = "^4"
-rich = ">=13"
-pydantic = ">=1.10,<2"
-
-[tool.poetry.dev-dependencies]
-cython = "*"
-pytest = "*"
+[tool.setuptools.packages.find]
+include = ["ms2pip*"]
[tool.black]
-line-length = 88
-target-version = ['py37']
+line-length = 99
+target-version = ['py38']
+
+[tool.ruff]
+line-length = 99
+target-version = 'py38'
+
+[tool.cibuildwheel]
+build = "cp3*-manylinux_x86_64 cp3*-win_amd64 cp3*-macosx_x86_64 cp3*-macosx_arm64"
+skip = "cp36-* cp37-* cp312-*" # EOL / no Numpy wheels available yet for Python 3.12
+manylinux-x86_64-image = "manylinux2014"
+# test-command = "pytest {package}/tests"
+test-command = "ms2pip --help"
+
+[tool.cibuildwheel.macos]
+before-all = "brew install libomp"
diff --git a/scripts/add_mgf_to_database.py b/scripts/add_mgf_to_database.py
deleted file mode 100755
index 22c939fa..00000000
--- a/scripts/add_mgf_to_database.py
+++ /dev/null
@@ -1,113 +0,0 @@
-#!/usr/bin/env python3
-import argparse
-import os
-import pyteomics.mgf
-from ms2pip.sqldb import tables
-from argparse import ArgumentTypeError as err
-
-
-class PathType(object):
- def __init__(self, exists=True, type='file', dash_ok=True):
- '''exists:
- True: a path that does exist
- False: a path that does not exist, in a valid parent directory
- None: don't care
- type: file, dir, symlink, None, or a function returning True for valid paths
- None: don't care
- dash_ok: whether to allow "-" as stdin/stdout'''
-
- assert exists in (True, False, None)
- assert type in ('file', 'dir', 'symlink', None) or hasattr(type, '__call__')
-
- self._exists = exists
- self._type = type
- self._dash_ok = dash_ok
-
- def __call__(self, string):
- if string == '-':
- # the special argument "-" means sys.std{in,out}
- if self._type == 'dir':
- raise err('standard input/output (-) not allowed as directory path')
- elif self._type == 'symlink':
- raise err('standard input/output (-) not allowed as symlink path')
- elif not self._dash_ok:
- raise err('standard input/output (-) not allowed')
- else:
- e = os.path.exists(string)
- if self._exists==True:
- if not e:
- raise err("path does not exist: '%s'" % string)
-
- if self._type is None:
- pass
- elif self._type=='file':
- if not os.path.isfile(string):
- raise err("path is not a file: '%s'" % string)
- elif self._type=='symlink':
- if not os.path.symlink(string):
- raise err("path is not a symlink: '%s'" % string)
- elif self._type=='dir':
- if not os.path.isdir(string):
- raise err("path is not a directory: '%s'" % string)
- elif not self._type(string):
- raise err("path not valid: '%s'" % string)
- else:
- if self._exists==False and e:
- raise err("path exists: '%s'" % string)
-
- p = os.path.dirname(os.path.normpath(string)) or '.'
- if not os.path.isdir(p):
- raise err("parent path is not a directory: '%s'" % p)
- elif not os.path.exists(p):
- raise err("parent directory does not exist: '%s'" % p)
-
- return string
-
-
-def parse_arguments():
- parser = argparse.ArgumentParser()
- parser.add_argument("mgf_file", type=PathType(exists=True, type='file'))
- parser.add_argument("--data-dir",
- type=PathType(exists=True, type='dir'),
- default="./data")
- parser.add_argument('--db-uri',
- default='postgresql:///ms2pip')
- return parser.parse_args()
-
-
-def add_mgf_to_database(connection, data_dir, mgf_file):
- spec_file = os.path.join(data_dir, mgf_file)
- with connection.begin() as trans:
- specfile = connection.execute(
- tables.specfile.insert().values(
- filename=mgf_file,
- path=data_dir))
- specfile_id = specfile.inserted_primary_key[0]
-
- with pyteomics.mgf.read(spec_file,
- use_header=False,
- convert_arrays=0,
- read_charges=False) as reader:
- for spectrum in reader:
- if 'pepmass' not in spectrum['params']:
- continue
-
- connection.execute(
- tables.spec.insert().values(
- specfile_id=specfile_id,
- spec_id=spectrum['params']['title'],
- pepmass=spectrum['params']['pepmass'][0],
- mzs=sorted(spectrum['m/z array'])
- ))
- trans.commit()
-
-
-def main():
- args = parse_arguments()
- engine = tables.create_engine(args.db_uri)
- with engine.connect() as connection:
- add_mgf_to_database(connection, os.path.dirname(args.mgf_file), os.path.basename(args.mgf_file))
-
-
-if __name__ == "__main__":
- main()
diff --git a/scripts/create_database.py b/scripts/create_database.py
deleted file mode 100755
index 53221d24..00000000
--- a/scripts/create_database.py
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/usr/bin/env python3
-from ms2pip.sqldb import tables
-
-engine = tables.create_engine("postgresql:///ms2pip")
-tables.metadata.create_all()
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 29b21fba..00000000
--- a/setup.cfg
+++ /dev/null
@@ -1,2 +0,0 @@
-[metadata]
-license_file = LICENSE
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 5b59c730..b389ab08 100644
--- a/setup.py
+++ b/setup.py
@@ -1,3 +1,4 @@
+import os
import platform
from glob import glob
@@ -6,68 +7,19 @@
from setuptools import setup
from setuptools.extension import Extension
-VERSION = "3.13.0"
-NAME = "ms2pip"
-LICENSE = "apache-2.0"
-DESCRIPTION = "MS²PIP: MS² Peak Intensity Prediction"
-AUTHOR = (
- "Sven Degroeve, Ralf Gabriels, Kevin Velghe, Ana Sílvia C. Silva, Arthur Declercq"
-)
-AUTHOR_EMAIL = "sven.degroeve@vib-ugent.be"
-URL = "https://www.github.com/compomics/ms2pip_c"
-PROJECT_URLS = {
- "Documentation": "http://compomics.github.io/projects/ms2pip_c",
- "Source": "https://github.com/compomics/ms2pip_c",
- "Tracker": "https://github.com/compomics/ms2pip_c/issues",
- "Web server": "https://iomics.ugent.be/ms2pip/",
- "Publication": "https://doi.org/10.1093/nar/gkz299/",
-}
-KEYWORDS = [
- "MS2PIP",
- "Proteomics",
- "peptides",
- "peak intensity prediction",
- "spectrum",
- "machine learning",
- "spectral library",
- "fasta2speclib",
-]
-CLASSIFIERS = [
- "Intended Audience :: Science/Research",
- "License :: OSI Approved :: Apache Software License",
- "Operating System :: POSIX :: Linux",
- "Programming Language :: Python :: 3 :: Only",
- "Topic :: Scientific/Engineering :: Bio-Informatics",
- "Development Status :: 5 - Production/Stable",
-]
-INSTALL_REQUIRES = [
- "biopython>=1.74,<2",
- "numpy>=1.16,<2",
- "pandas>=1,<2",
- "pyteomics>=3.5,<5",
- "tqdm>=4,<5",
- "tables>=3.4",
- "tomlkit>=0.5,<1",
- "sqlalchemy>=1.3,<2",
- "click>=7,<9",
- "xgboost>=1.3,<2",
- "matplotlib>=3.0",
- "spectrum-utils==0.3.5",
- "lxml>=4",
- "rich>=13",
- "pydantic>=1.10,<2",
-]
-PYTHON_REQUIRES = ">=3.7,<4"
+def _get_version():
+ with open("ms2pip/__init__.py") as f:
+ for line in f:
+ if line.startswith("__version__"):
+ return line.split("=")[1].strip().strip('"').strip("'")
-with open("README.md", "r") as fh:
- LONG_DESCRIPTION = fh.read()
to_remove = [
- "ms2pip/cython_modules/ms2pip_pyx.c*",
- "ms2pip/cython_modules/ms2pip_pyx.so",
+ "ms2pip/_cython_modules/ms2pip_pyx.c*",
+ "ms2pip/_cython_modules/ms2pip_pyx.so",
]
-# _ = [[os.remove(f) for f in glob(pat)] for pat in to_remove]
+_ = [[os.remove(f) for f in glob(pat)] for pat in to_remove]
# Large machine-written C model files require optimization to be disabled
compile_args = {
@@ -92,36 +44,14 @@
extensions = [
Extension(
- "ms2pip.cython_modules.ms2pip_pyx",
- sources=["ms2pip/cython_modules/ms2pip_pyx.pyx"] + glob("ms2pip/models/*/*.c"),
+ "ms2pip._cython_modules.ms2pip_pyx",
+ sources=["ms2pip/_cython_modules/ms2pip_pyx.pyx"] + glob("ms2pip/_models_c/*/*.c"),
extra_compile_args=compile_args[platform.system()],
)
]
setup(
- name=NAME,
- version=VERSION,
- license=LICENSE,
- description=DESCRIPTION,
- long_description=LONG_DESCRIPTION,
- long_description_content_type="text/markdown",
- author=AUTHOR,
- author_email=AUTHOR_EMAIL,
- url=URL,
- project_urls=PROJECT_URLS,
- keywords=KEYWORDS,
- classifiers=CLASSIFIERS,
- packages=["ms2pip", "ms2pip.ms2pip_tools", "fasta2speclib"],
- include_package_data=True,
- entry_points={
- "console_scripts": [
- "ms2pip=ms2pip.__main__:main",
- "fasta2speclib=fasta2speclib.fasta2speclib:main",
- "ms2pip-single-prediction=ms2pip.single_prediction:_main",
- ],
- },
- install_requires=INSTALL_REQUIRES,
- python_requires=PYTHON_REQUIRES,
+ version=_get_version(),
ext_modules=extensions,
include_dirs=[numpy.get_include()],
cmdclass={"build_ext": build_ext},
diff --git a/tests/test_config_parser.py b/tests/test_config_parser.py
deleted file mode 100644
index 19f307f1..00000000
--- a/tests/test_config_parser.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import os
-
-from ms2pip.config_parser import ConfigParser
-
-
-TEST_DIR = os.path.dirname(__file__)
-
-
-class TestConfigParser:
- def test_load_ms2pip_txt(self):
- config_file = os.path.join(TEST_DIR, "test_data/config.txt")
- config_parser = ConfigParser(config_file)
- config_parser.load()
-
- assert config_parser.config == {
- "ms2pip": {
- "ptm": [
- "Oxidation,15.994915,opt,M",
- "Carbamidomethyl,57.021464,opt,C",
- "Acetyl,42.010565,opt,N-term",
- ],
- "sptm": [],
- "gptm": [],
- "frag_method": "HCD",
- "frag_error": 0.02,
- "out": "csv",
- }
- }
-
- def test_load_toml(self):
- config_file = os.path.join(TEST_DIR, "test_data/config.toml")
- config_parser = ConfigParser(config_file)
- config_parser.load()
-
- assert config_parser.config == {
- "ms2pip": {
- "ptm": [
- "Oxidation,15.994915,opt,M",
- "Carbamidomethyl,57.021464,opt,C",
- "Acetyl,42.010565,opt,N-term",
- ],
- "sptm": [],
- "gptm": [],
- "frag_method": "HCD",
- "frag_error": 0.02,
- "out": "csv",
- }
- }
-
- def test_write_toml(self):
- test_config_file = os.path.join(TEST_DIR, "test_data/config_test.toml")
- config_parser = ConfigParser()
- target_config = {
- "ms2pip": {
- "ptm": [
- "Oxidation,15.994915,opt,M",
- "Carbamidomethyl,57.021464,opt,C",
- "Acetyl,42.010565,opt,N-term",
- ],
- "sptm": [],
- "gptm": [],
- "frag_method": "HCD",
- "frag_error": 0.02,
- "out": "csv",
- }
- }
- config_parser.config = target_config
- config_parser.write(test_config_file)
- config_parser.load()
- os.remove(test_config_file)
- assert config_parser.config == target_config
diff --git a/tests/test_core.py b/tests/test_core.py
new file mode 100644
index 00000000..d2759476
--- /dev/null
+++ b/tests/test_core.py
@@ -0,0 +1,15 @@
+import pandas as pd
+
+from ms2pip.core import get_training_data
+
+
+def _test_get_training_data():
+ expected_df = pd.read_feather("tests/test_data/massivekb_selected_500.feather")
+ output_df = get_training_data(
+ "tests/test_data/massivekb_selected_500.peprec",
+ "tests/test_data/massivekb_selected_500.mgf",
+ model="HCD",
+ ms2_tolerance=0.02,
+ processes=1
+ )
+ pd.testing.assert_frame_equal(expected_df, output_df)
diff --git a/tests/test_data/massivekb_selected_500_targetvectors.h5 b/tests/test_data/massivekb_selected_500_targetvectors.h5
deleted file mode 100644
index 1cda4fd0..00000000
Binary files a/tests/test_data/massivekb_selected_500_targetvectors.h5 and /dev/null differ
diff --git a/tests/test_data/massivekb_selected_500_training_data.feather b/tests/test_data/massivekb_selected_500_training_data.feather
new file mode 100644
index 00000000..b1109e1f
Binary files /dev/null and b/tests/test_data/massivekb_selected_500_training_data.feather differ
diff --git a/tests/test_data/search_space_config.json b/tests/test_data/search_space_config.json
new file mode 100644
index 00000000..65970077
--- /dev/null
+++ b/tests/test_data/search_space_config.json
@@ -0,0 +1,14 @@
+{
+ "fasta_file": "tests/test_data/test.fasta",
+ "min_length": 7,
+ "max_length": 30,
+ "min_precursor_mz": 400,
+ "max_precursor_mz": 2000,
+ "cleavage_rule": "trypsin",
+ "missed_cleavages": 2,
+ "semi_specific": false,
+ "add_decoys": false,
+ "modifications": [],
+ "max_variable_modifications": 3,
+ "charges": [2, 3]
+}
diff --git a/tests/test_data/test.fasta b/tests/test_data/test.fasta
new file mode 100644
index 00000000..8683d577
--- /dev/null
+++ b/tests/test_data/test.fasta
@@ -0,0 +1,2 @@
+>P12345
+MYSSCSLLQRLVWFPFLALVATQLLFIRNVSSLNLTNEYLHHKCLVSEGKYKPGSKYEYI
diff --git a/tests/test_data/test_HCD2019_predictions.csv b/tests/test_data/test_HCD2019_predictions.csv
deleted file mode 100644
index 5f66aacc..00000000
--- a/tests/test_data/test_HCD2019_predictions.csv
+++ /dev/null
@@ -1,45 +0,0 @@
-spec_id,charge,ion,ionnumber,mz,prediction
-peptide1,2,B,1,72.0443,0.0114047
-peptide1,2,B,2,175.054,0.0764582
-peptide1,2,B,3,290.08,0.748557
-peptide1,2,Y,1,148.06,0.153323
-peptide1,2,Y,2,263.087,0.0102568
-peptide1,2,Y,3,366.096,0
-peptide2,2,B,1,72.0443,0
-peptide2,2,B,2,175.054,0.0771302
-peptide2,2,B,3,290.08,0.0179581
-peptide2,2,B,4,419.123,0.00167235
-peptide2,2,B,5,566.192,0.000991638
-peptide2,2,B,6,623.213,0
-peptide2,2,B,7,760.272,0
-peptide2,2,Y,1,132.102,0.00592365
-peptide2,2,Y,2,269.161,0.177075
-peptide2,2,Y,3,326.182,0.236763
-peptide2,2,Y,4,473.251,0.0238432
-peptide2,2,Y,5,602.293,0.129095
-peptide2,2,Y,6,717.32,0.274491
-peptide2,2,Y,7,820.329,0.0550567
-peptide3,2,B,1,72.0443,0
-peptide3,2,B,2,175.054,0.0640166
-peptide3,2,B,3,290.08,0.0442152
-peptide3,2,B,4,419.123,0.0352122
-peptide3,2,B,5,566.192,0.00300568
-peptide3,2,B,6,623.213,0.00172832
-peptide3,2,B,7,760.272,0.01605
-peptide3,2,B,8,873.356,0.00741987
-peptide3,2,B,9,1001.45,0.00984674
-peptide3,2,B,10,1132.49,0.00759491
-peptide3,2,B,11,1246.53,0.0381688
-peptide3,2,B,12,1343.59,0.000883813
-peptide3,2,Y,1,147.076,0.0406565
-peptide3,2,Y,2,244.129,0.488677
-peptide3,2,Y,3,358.172,0.0152312
-peptide3,2,Y,4,489.213,0.0253922
-peptide3,2,Y,5,617.308,0.0511958
-peptide3,2,Y,6,730.392,0.0225316
-peptide3,2,Y,7,867.451,0.00816545
-peptide3,2,Y,8,924.472,0.0791076
-peptide3,2,Y,9,1071.54,0.0240666
-peptide3,2,Y,10,1200.58,0.00349956
-peptide3,2,Y,11,1315.61,0.0133346
-peptide3,2,Y,12,1418.62,0
diff --git a/tests/test_encoder.py b/tests/test_encoder.py
new file mode 100644
index 00000000..5feb5f96
--- /dev/null
+++ b/tests/test_encoder.py
@@ -0,0 +1,91 @@
+import pytest
+from psm_utils import Peptidoform, PSM, PSMList
+
+from ms2pip._utils.encoder import Encoder
+
+
+class TestEncoder:
+ def test_from_peptidoform(self):
+ test_cases = [
+ # Peptidoform, {(target, label): (amino_acid, amino_acid_id, mass_shift)}
+ ("ACDEK", {}),
+ ("AC[+57.021464]DEK", {("C", "+57.021464"): ("C", 1, 57.021464)}),
+ ("AC[U:4]", {("C", "UNIMOD:4"): ("C", 1, 57.021464)}),
+ ("AC[formula:H3C2NO]", {("C", "Formula:H3C2NO"): ("C", 1, 57.021464)}),
+ ("[Acetyl]-ACDE", {("n_term", "Acetyl"): ("n_term", -1, 42.010565)}),
+ ("ACDE-[Amidated]", {("c_term", "Amidated"): ("c_term", -2, -0.984016)}),
+ (
+ "AC[+57.021464]DE-[Amidated]",
+ {
+ ("C", "+57.021464"): ("C", 1, 57.021464),
+ ("c_term", "Amidated"): ("c_term", -2, -0.984016),
+ },
+ ),
+ (
+ "[Acetyl]-AC[+57.021464]DE",
+ {
+ ("n_term", "Acetyl"): ("n_term", -1, 42.010565),
+ ("C", "+57.021464"): ("C", 1, 57.021464),
+ },
+ ),
+ ]
+
+ for peptidoform, expected_mods in test_cases:
+ encoder = Encoder.from_peptidoform(Peptidoform(peptidoform))
+ for key, modification in encoder.modifications.items():
+ for item_key, expected_item in zip(
+ ["amino_acid", "amino_acid_id", "mass_shift"], expected_mods[key]
+ ):
+ if isinstance(expected_item, float):
+ assert modification[item_key] == pytest.approx(expected_item)
+ else:
+ assert modification[item_key] == expected_item
+
+ def test_from_psm_list(self):
+ psm_list = PSMList(psm_list=[
+ PSM(peptidoform="AC[+57.021464]DEK", spectrum_id=0),
+ PSM(peptidoform="AC[U:4]", spectrum_id=1),
+ PSM(peptidoform="AC[formula:H3C2NO]", spectrum_id=2),
+ PSM(peptidoform="[Acetyl]-ACDE", spectrum_id=3),
+ PSM(peptidoform="ACDE-[Amidated]",spectrum_id= 4)
+ ])
+ expected = {
+ ("C", "+57.021464"): {
+ "mod_id": 38,
+ "mass_shift": 57.021464,
+ "amino_acid": "C",
+ "amino_acid_id": 1,
+ },
+ ("C", "UNIMOD:4"): {
+ "mod_id": 39,
+ "mass_shift": 57.021464,
+ "amino_acid": "C",
+ "amino_acid_id": 1,
+ },
+ ("C", "Formula:H3C2NO"): {
+ "mod_id": 40,
+ "mass_shift": 57.02146372057,
+ "amino_acid": "C",
+ "amino_acid_id": 1,
+ },
+ ("n_term", "Acetyl"): {
+ "mod_id": 41,
+ "mass_shift": 42.010565,
+ "amino_acid": "n_term",
+ "amino_acid_id": -1,
+ },
+ ("c_term", "Amidated"): {
+ "mod_id": 42,
+ "mass_shift": -0.984016,
+ "amino_acid": "c_term",
+ "amino_acid_id": -2,
+ },
+ }
+
+ encoder = Encoder.from_psm_list(psm_list)
+ for modification_key, modification_dict in encoder.modifications.items():
+ for item_key, expected_item in expected[modification_key].items():
+ if isinstance(expected_item, float):
+ assert modification_dict[item_key] == pytest.approx(expected_item)
+ else:
+ assert modification_dict[item_key] == expected_item
diff --git a/tests/test_fasta2speclib.py b/tests/test_fasta2speclib.py
deleted file mode 100644
index 60930c1c..00000000
--- a/tests/test_fasta2speclib.py
+++ /dev/null
@@ -1,185 +0,0 @@
-"""Tests for fasta2speclib."""
-
-from pyteomics.fasta import Protein
-
-from fasta2speclib.fasta2speclib import Fasta2SpecLib, ModificationConfig, Peptide
-
-MODIFICATION_CONFIG = [
- {
- "name": "Oxidation",
- "mass_shift": 15.9994,
- "amino_acid": "M",
- },
- {
- "name": "Carbamidomethyl",
- "mass_shift": 57.0513,
- "amino_acid": "C",
- "fixed": True,
- },
- {
- "name": "Glu->pyro-Glu",
- "mass_shift": -18.010565,
- "amino_acid": "E",
- "peptide_n_term": True,
- },
- {
- "name": "Acetyl",
- "mass_shift": 42.010565,
- "amino_acid": None,
- "protein_n_term": True,
- },
-]
-MODIFICATION_CONFIG = [ModificationConfig(**mod) for mod in MODIFICATION_CONFIG]
-
-
-def test_get_modifications_by_target():
- modifications_by_target = Fasta2SpecLib._get_modifications_by_target(MODIFICATION_CONFIG)
- assert modifications_by_target["sidechain"] == {"M": [None] + MODIFICATION_CONFIG[0:1]}
- assert modifications_by_target["peptide_n_term"] == {"E": [None] + MODIFICATION_CONFIG[2:3]}
- assert modifications_by_target["peptide_c_term"] == {}
- assert modifications_by_target["protein_n_term"] == {"any": [None] + MODIFICATION_CONFIG[3:4]}
- assert modifications_by_target["protein_c_term"] == {}
-
-
-def test_get_modification_versions():
- modification_config = [
- ModificationConfig(
- **{
- "name": "Oxidation",
- "mass_shift": 15.9994,
- "amino_acid": "M",
- }
- ),
- ModificationConfig(
- **{
- "name": "Carbamidomethyl",
- "mass_shift": 57.0513,
- "amino_acid": "C",
- "fixed": True,
- }
- ),
- ModificationConfig(
- **{
- "name": "Glu->pyro-Glu",
- "mass_shift": -18.010565,
- "amino_acid": "E",
- "protein_n_term": True,
- }
- ),
- ]
- modifications_by_target = Fasta2SpecLib._get_modifications_by_target(modification_config)
-
- test_cases = [
- ("ADEF", {""}), # None
- ("ACDE", {"2|Carbamidomethyl"}), # Single fixed
- ("ACCDE", {"2|Carbamidomethyl|3|Carbamidomethyl"}), # Double fixed
- ("ADME", {"", "3|Oxidation"}), # Single variable
- (
- "ADMME",
- {"", "3|Oxidation", "4|Oxidation", "3|Oxidation|4|Oxidation"},
- ), # Double variable
- (
- "ADMMME",
- {
- "",
- "3|Oxidation",
- "4|Oxidation",
- "5|Oxidation",
- "3|Oxidation|4|Oxidation",
- "4|Oxidation|5|Oxidation",
- "3|Oxidation|5|Oxidation",
- },
- ), # More than maximum simultaneous mods should be ignored
- ("EDEF", {"", "0|Glu->pyro-Glu"}), # N-term and AA-specific
- ]
-
- for peptide, expected_output in test_cases:
- output = Fasta2SpecLib._get_modification_versions(
- Peptide(sequence=peptide, is_n_term=True, proteins=[]),
- modification_config,
- modifications_by_target,
- max_variable_modifications=2,
- )
- assert set(output) == expected_output
-
-
-def test_digest_protein():
- test_input = {
- "protein": Protein(
- description="P12345",
- sequence="MYSSCSLLQRLVWFPFLALVATQLLFIRNVSSLNLTNEYLHHKCLVSEGKYKPGSKYEYI",
- ),
- "min_length": 8,
- "max_length": 30,
- "cleavage_rule": "trypsin",
- "missed_cleavages": 2,
- "semi_specific": False,
- }
-
- test_output = [
- Peptide(
- sequence="MYSSCSLLQR",
- proteins=["P12345"],
- modification_options=None,
- is_n_term=True,
- is_c_term=False,
- ),
- Peptide(
- sequence="MYSSCSLLQRLVWFPFLALVATQLLFIR",
- proteins=["P12345"],
- modification_options=None,
- is_n_term=True,
- is_c_term=False,
- ),
- Peptide(
- sequence="LVWFPFLALVATQLLFIR",
- proteins=["P12345"],
- modification_options=None,
- is_n_term=False,
- is_c_term=False,
- ),
- Peptide(
- sequence="NVSSLNLTNEYLHHK",
- proteins=["P12345"],
- modification_options=None,
- is_n_term=False,
- is_c_term=False,
- ),
- Peptide(
- sequence="NVSSLNLTNEYLHHKCLVSEGK",
- proteins=["P12345"],
- modification_options=None,
- is_n_term=False,
- is_c_term=False,
- ),
- Peptide(
- sequence="NVSSLNLTNEYLHHKCLVSEGKYKPGSK",
- proteins=["P12345"],
- modification_options=None,
- is_n_term=False,
- is_c_term=False,
- ),
- Peptide(
- sequence="CLVSEGKYKPGSK",
- proteins=["P12345"],
- modification_options=None,
- is_n_term=False,
- is_c_term=False,
- ),
- Peptide(
- sequence="CLVSEGKYKPGSKYEYI",
- proteins=["P12345"],
- modification_options=None,
- is_n_term=False,
- is_c_term=True,
- ),
- Peptide(
- sequence="YKPGSKYEYI",
- proteins=["P12345"],
- modification_options=None,
- is_n_term=False,
- is_c_term=True,
- ),
- ]
-
- assert test_output == Fasta2SpecLib._digest_protein(**test_input)
diff --git a/tests/test_features.py b/tests/test_features.py
deleted file mode 100644
index 87bdca01..00000000
--- a/tests/test_features.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import os
-
-import pandas as pd
-import numpy as np
-
-from ms2pip.ms2pipC import MS2PIP
-
-
-TEST_DIR = os.path.dirname(__file__)
-
-
-class TestFeatureExtraction:
- def _assert_get_feature_vectors(self, test_data, target_data):
- assert test_data[test_data.columns[:-3]].equals(
- target_data[target_data.columns[:-3]]
- )
-
- def _assert_get_targetsB(self, test_data, target_data):
- for i in range(3):
- assert np.isclose(test_data["targets_B"][i], target_data["targets_B"][i])
-
- def _assert_get_targetsY(self, test_data, target_data):
- for i in range(3):
- assert np.isclose(test_data["targets_Y"][i], target_data["targets_Y"][i])
-
- def _assert_get_psmid(self, test_data, target_data):
- assert test_data["psmid"].equals(target_data["psmid"])
-
- def test_dummy_spectrum(self):
- # Run ms2pipC to extract features and targets from an .mgf and .PEPREC files
- params = {
- "ms2pip": {
- "ptm": [],
- "sptm": [],
- "gptm": [],
- "frag_method": "HCD2019",
- "frag_error": 0.02,
- "out": "csv",
- }
- }
- ms2pip = MS2PIP(
- os.path.join(TEST_DIR, "test_data/test.peprec"),
- spec_file=os.path.join(TEST_DIR, "test_data/hard_test.mgf"),
- vector_file=os.path.join(TEST_DIR, "test_data/test.h5"),
- params=params,
- )
- ms2pip.run()
-
- # Load target values
- test_data = pd.read_hdf(os.path.join(TEST_DIR, "test_data/test.h5"), "table")
- target_data = pd.read_hdf(
- os.path.join(TEST_DIR, "test_data/hard_test_targetvectors.h5"), "table"
- )
-
- # Test
- self._assert_get_feature_vectors(test_data, target_data)
- self._assert_get_targetsB(test_data, target_data)
- self._assert_get_targetsY(test_data, target_data)
- self._assert_get_psmid(test_data, target_data)
-
- os.remove(os.path.join(TEST_DIR, "test_data/test.h5"))
-
- def test_real_spectra(self):
- # Run ms2pipC to extract features and targets from an .mgf and .PEPREC files
- params = {
- "ms2pip": {
- "ptm": [
- "Oxidation,15.994915,opt,M",
- "Carbamidomethyl,57.021464,opt,C",
- "Pyro_glu,-18.010565,opt,E",
- "Deamidation,0.984016,opt,N",
- "Acetyl,42.010565,opt,N-term",
- "Carbamyl,43.005814,opt,N-term",
- ],
- "sptm": [],
- "gptm": [],
- "frag_method": "HCD2019",
- "frag_error": 0.02,
- "out": "csv",
- }
- }
- ms2pip = MS2PIP(
- os.path.join(TEST_DIR, "test_data/massivekb_selected_500.peprec"),
- spec_file=os.path.join(TEST_DIR, "test_data/massivekb_selected_500.mgf"),
- vector_file=os.path.join(
- TEST_DIR, "test_data/massivekb_selected_500_test.h5"
- ),
- params=params,
- num_cpu=1,
- )
- ms2pip.run()
-
- # Load target values
- test_data = pd.read_hdf(
- os.path.join(TEST_DIR, "test_data/massivekb_selected_500_test.h5"), "table"
- )
- target_data = pd.read_hdf(
- os.path.join(TEST_DIR, "test_data/massivekb_selected_500_targetvectors.h5"),
- "table",
- )
-
- # Test
- self._assert_get_feature_vectors(test_data, target_data)
- self._assert_get_targetsB(test_data, target_data)
- self._assert_get_targetsY(test_data, target_data)
- self._assert_get_psmid(test_data, target_data)
-
- os.remove(os.path.join(TEST_DIR, "test_data/massivekb_selected_500_test.h5"))
diff --git a/tests/test_match_spectra.py b/tests/test_match_spectra.py
deleted file mode 100644
index 79da262f..00000000
--- a/tests/test_match_spectra.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import unittest
-import numpy as np
-import ms2pip.match_spectra
-from operator import itemgetter
-
-
-class TestMatchSpectra(unittest.TestCase):
- def test_get_intense_mzs(self):
- mzs = np.array([72.04435, 143.08147, 214.11859, 285.1557, 414.19827, 527.28235, 598.31946, 697.3879, 147.11276, 246.18117, 317.21826, 430.30234, 559.345, 630.3821, 701.4192, 772.4563], dtype=np.float32)
- intensities = np.array([0.000340063, 0.186675, 0.0165939, 0.0138825, 0, 0, 0, 0, 0.0670459, 0.147733, 0.046132, 0.00865729, 0.181739, 0.161184, 0.15575, 0.0142658], dtype=np.float32)
- top5 = [np.float32(x) for x in (143.08147, 559.345, 630.3821, 701.4192, 246.18117)]
-
- self.assertEqual(top5[:3], ms2pip.match_spectra.get_intense_mzs(mzs, intensities))
- self.assertEqual([], ms2pip.match_spectra.get_intense_mzs(mzs, intensities, n=0))
- self.assertEqual(top5, ms2pip.match_spectra.get_intense_mzs(mzs, intensities, n=5))
- self.assertEqual([x[0] for x in sorted(zip(mzs, intensities), key=itemgetter(1), reverse=True)], ms2pip.match_spectra.get_intense_mzs(mzs, intensities, n=len(mzs)))
-
- def test_match_mzs(self):
- mzs = np.array([72.04435, 143.08147, 214.11859, 285.1557, 414.19827, 527.28235, 598.31946, 697.3879, 147.11276, 246.18117, 317.21826, 430.30234, 559.345, 630.3821, 701.4192, 772.4563], dtype=np.float32)
- top3 = [np.float32(x) for x in (143.08147, 559.345, 630.3821)]
-
- self.assertTrue(ms2pip.match_spectra.match_mzs(mzs, []))
- self.assertFalse(ms2pip.match_spectra.match_mzs([], []))
- self.assertFalse(ms2pip.match_spectra.match_mzs([], [3]))
- self.assertTrue(ms2pip.match_spectra.match_mzs(mzs, top3))
- self.assertFalse(ms2pip.match_spectra.match_mzs(mzs, [x + 0.02 for x in top3]))
- self.assertFalse(ms2pip.match_spectra.match_mzs(mzs, [x - 0.02 for x in top3]))
- self.assertTrue(ms2pip.match_spectra.match_mzs(mzs, [x + 0.02 for x in top3], max_error=0.05))
- self.assertTrue(ms2pip.match_spectra.match_mzs(mzs, [x - 0.02 for x in top3], max_error=0.05))
diff --git a/tests/test_modifications.py b/tests/test_modifications.py
deleted file mode 100644
index 1a37766a..00000000
--- a/tests/test_modifications.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import ms2pip.peptides
-
-
-class TestModifications:
- def test_add_from_ms2pip_modstrings(self):
- mods = ms2pip.peptides.Modifications()
- mods.add_from_ms2pip_modstrings([
- "Oxidation,15.994915,opt,M",
- "Acetyl,42.010565,opt,N-term",
- "Methyl,14.01565,opt,L",
- ])
-
- assert mods.modifications['ptm']["Oxidation"]["amino_acid"] == "M"
- assert mods.modifications['ptm']["Acetyl"]["mass_shift"] == 42.010565
- assert mods.modifications['ptm']["Methyl"]["mass_shift"] == 14.01565
-
- def test_get_mass_shifts(self):
- mods = ms2pip.peptides.Modifications()
-
- mods.add_from_ms2pip_modstrings([
- "Oxidation,15.994915,opt,M"
- ])
- assert mods.mass_shifts["Oxidation"] == 15.994915
-
- # Test cache clear after adding new modifications
- mods.add_from_ms2pip_modstrings([
- "Acetyl,42.010565,opt,N-term",
- ])
- assert mods.mass_shifts["Acetyl"] == 42.010565
diff --git a/tests/test_predictions.py b/tests/test_predictions.py
deleted file mode 100644
index e6eed081..00000000
--- a/tests/test_predictions.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import os
-
-import pandas as pd
-
-from ms2pip.ms2pipC import MS2PIP
-
-
-TEST_DIR = os.path.dirname(__file__)
-
-
-def run_ms2pip():
- """Run ms2pipC to predict peak intensities from a PEPREC file (HCD model). """
- params = {
- "ms2pip": {
- "ptm": [
- "Oxidation,15.994915,opt,M",
- "Carbamidomethyl,57.021464,opt,C",
- "Acetyl,42.010565,opt,N-term",
- ],
- "sptm": [],
- "gptm": [],
- "frag_method": "HCD2019",
- "frag_error": 0.02,
- "out": "csv",
- }
- }
- ms2pip = MS2PIP(os.path.join(TEST_DIR, "test_data/test.peprec"), params=params)
- ms2pip.run()
-
- test_data = pd.read_csv(
- os.path.join(TEST_DIR, "test_data/test_HCD2019_predictions.csv")
- )
- target_data = pd.read_csv(
- os.path.join(TEST_DIR, "test_data/target_HCD2019_predictions.csv")
- )
- pepfile = pd.read_csv(
- os.path.join(TEST_DIR, "test_data/test.peprec"),
- sep=" ",
- index_col=False,
- dtype={"spec_id": str, "modifications": str},
- )
- return test_data, target_data, pepfile
-
-
-TEST_DATA, TARGET_DATA, PEPFILE = run_ms2pip()
-
-
-class TestPredictions:
- def test_all_spec(self):
- assert set(TEST_DATA.spec_id.unique()) == set(PEPFILE.spec_id)
-
- def test_amount_peaks(self):
- for pep in ["peptide1", "peptide2", "peptide3"]:
- peplen = len(PEPFILE[PEPFILE.spec_id == pep].peptide.values[0])
- assert len(TEST_DATA[TEST_DATA.spec_id == pep]) == (2 * peplen) - 2
-
- def test_peak_ints_b(self):
- for pep in TARGET_DATA.spec_id.unique():
- tmp_test = TEST_DATA[TEST_DATA.spec_id == pep]
- tmp_test = tmp_test[tmp_test.ion == "b"]
- tmp_target = TARGET_DATA[TARGET_DATA.spec_id == pep]
- tmp_target = tmp_target[tmp_target.ion == "b"]
- for no in tmp_target.ionnumber:
- assert (
- tmp_test[tmp_test.ionnumber == no]["prediction"].values[0]
- == tmp_target[tmp_target.ionnumber == no]["prediction"].values[0]
- )
-
- def test_peak_ints_y(self):
- for pep in TARGET_DATA.spec_id.unique():
- tmp_test = TEST_DATA[TEST_DATA.spec_id == pep]
- tmp_test = tmp_test[tmp_test.ion == "y"]
- tmp_target = TARGET_DATA[TARGET_DATA.spec_id == pep]
- tmp_target = tmp_target[tmp_target.ion == "y"]
- for no in tmp_target.ionnumber:
- assert (
- tmp_test[tmp_test.ionnumber == no]["prediction"].values[0]
- == tmp_target[tmp_target.ionnumber == no]["prediction"].values[0]
- )
diff --git a/tests/test_retention_time.py b/tests/test_retention_time.py
deleted file mode 100644
index 8be7fbaa..00000000
--- a/tests/test_retention_time.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-
-import numpy as np
-import pandas as pd
-
-from ms2pip.retention_time import RetentionTime
-from ms2pip.config_parser import ConfigParser
-
-
-TEST_DIR = os.path.dirname(__file__)
-
-class TestRetentionTime:
- def test_prepare_deeplc_peptide_df(self):
- peprec = pd.read_csv(os.path.join(TEST_DIR, "test_data/test.peprec"), sep=" ")
- config = {
- "deeplc": {
- "calibration_file": False,
- "verbose": False,
- "path_model": False,
- "split_cal": 25,
- "batch_num": 350000,
- }
- }
-
- rt_predictor = RetentionTime(config=config)
- rt_predictor.peprec = peprec
- rt_predictor._prepare_deeplc_peptide_df()
- dlc_df = rt_predictor.deeplc_pep_df
-
- assert dlc_df.equals(
- pd.DataFrame({
- "seq": {0: "ACDE", 1: "ACDEFGHI", 2: "ACDEFGHIKMNPQ"},
- "modifications": {0: np.nan, 1: np.nan, 2: np.nan},
- })
- )
diff --git a/tests/test_search_space.py b/tests/test_search_space.py
new file mode 100644
index 00000000..d3479093
--- /dev/null
+++ b/tests/test_search_space.py
@@ -0,0 +1,234 @@
+from ms2pip import search_space
+
+OXIDATION = search_space.ModificationConfig(
+ label="Oxidation",
+ amino_acid="M",
+)
+CARBAMIDOMETHYL = search_space.ModificationConfig(
+ label="Carbamidomethyl",
+ amino_acid="C",
+ fixed=True,
+)
+PYROGLU = search_space.ModificationConfig(
+ label="Glu->pyro-Glu",
+ amino_acid="E",
+ peptide_n_term=True,
+)
+ACETYL = search_space.ModificationConfig(
+ label="Acetyl",
+ amino_acid=None,
+ protein_n_term=True,
+)
+PHOSPHO = search_space.ModificationConfig(
+ label="Phospho",
+ amino_acid="T",
+ fixed=False,
+)
+
+MODIFICATION_CONFIG = [OXIDATION, CARBAMIDOMETHYL, PYROGLU, ACETYL]
+
+
+def test_restructure_modifications_by_target():
+ test_cases = [
+ {
+ "modifications": [PHOSPHO, ACETYL],
+ "expected": {
+ "sidechain": {"T": [PHOSPHO]},
+ "peptide_n_term": {},
+ "peptide_c_term": {},
+ "protein_n_term": {"any": [ACETYL]},
+ "protein_c_term": {},
+ },
+ },
+ {
+ "modifications": [CARBAMIDOMETHYL, ACETYL],
+ "expected": {
+ "sidechain": {},
+ "peptide_n_term": {},
+ "peptide_c_term": {},
+ "protein_n_term": {"any": [ACETYL]},
+ "protein_c_term": {},
+ },
+ },
+ ]
+
+ for case in test_cases:
+ test_out = search_space._restructure_modifications_by_target(case["modifications"])
+ assert test_out == case["expected"]
+
+
+def test_get_peptidoform_modification_versions():
+ test_cases = [
+ # None
+ {
+ "sequence": "PEPTIDE",
+ "modifications": [],
+ "expected": [{}],
+ },
+ # Single fixed
+ {
+ "sequence": "ACDE",
+ "modifications": [CARBAMIDOMETHYL],
+ "expected": [{1: CARBAMIDOMETHYL}],
+ },
+ # Double fixed
+ {
+ "sequence": "ACCDE",
+ "modifications": [CARBAMIDOMETHYL],
+ "expected": [{1: CARBAMIDOMETHYL, 2: CARBAMIDOMETHYL}],
+ },
+ # Single variable
+ {
+ "sequence": "ADME",
+ "modifications": [OXIDATION],
+ "expected": [{}, {2: OXIDATION}],
+ },
+ # Double variable
+ {
+ "sequence": "ADMME",
+ "modifications": [OXIDATION],
+ "expected": [{}, {2: OXIDATION}, {3: OXIDATION}, {2: OXIDATION, 3: OXIDATION}],
+ },
+ # More than maximum simultaneous mods should be ignored
+ {
+ "sequence": "ADMMME",
+ "modifications": [OXIDATION],
+ "expected": [
+ {},
+ {2: OXIDATION},
+ {3: OXIDATION},
+ {4: OXIDATION},
+ {2: OXIDATION, 3: OXIDATION},
+ {2: OXIDATION, 4: OXIDATION},
+ {3: OXIDATION, 4: OXIDATION},
+ ],
+ },
+ # N-term and AA-specific
+ {
+ "sequence": "EDEF",
+ "modifications": [PYROGLU],
+ "expected": [{}, {"N": PYROGLU}],
+ },
+ {
+ "sequence": "PEPTIDE",
+ "modifications": [PHOSPHO, ACETYL],
+ "expected": [{}, {3: PHOSPHO}, {"N": ACETYL}, {"N": ACETYL, 3: PHOSPHO}],
+ },
+ {
+ "sequence": "ACDEK",
+ "modifications": [CARBAMIDOMETHYL, ACETYL],
+ "expected": [
+ {1: CARBAMIDOMETHYL},
+ {1: CARBAMIDOMETHYL, "N": ACETYL},
+ ],
+ },
+ ]
+
+ for case in test_cases:
+ peptide = search_space._PeptidoformSearchSpace(
+ sequence=case["sequence"], proteins=[], is_n_term=True
+ )
+ modifications_by_target = search_space._restructure_modifications_by_target(
+ case["modifications"]
+ )
+ test_out = search_space._get_peptidoform_modification_versions(
+ peptide,
+ case["modifications"],
+ modifications_by_target,
+ max_variable_modifications=2,
+ )
+
+ assert test_out == case["expected"]
+
+
+def test_get_modifications_by_target():
+ modifications_by_target = search_space._restructure_modifications_by_target(
+ MODIFICATION_CONFIG
+ )
+ assert modifications_by_target["sidechain"] == {"M": MODIFICATION_CONFIG[0:1]}
+ assert modifications_by_target["peptide_n_term"] == {"E": MODIFICATION_CONFIG[2:3]}
+ assert modifications_by_target["peptide_c_term"] == {}
+ assert modifications_by_target["protein_n_term"] == {"any": MODIFICATION_CONFIG[3:4]}
+ assert modifications_by_target["protein_c_term"] == {}
+
+
+class TestProteomeSearchSpace:
+ def test_digest_fasta(self):
+ test_input = {
+ "fasta_file": "tests/test_data/test.fasta",
+ "min_length": 8,
+ "max_length": 30,
+ "cleavage_rule": "trypsin",
+ "missed_cleavages": 2,
+ "semi_specific": False,
+ }
+
+ test_output = [
+ search_space._PeptidoformSearchSpace(
+ sequence="MYSSCSLLQR",
+ proteins=["P12345"],
+ modification_options=[],
+ is_n_term=True,
+ is_c_term=False,
+ ),
+ search_space._PeptidoformSearchSpace(
+ sequence="MYSSCSLLQRLVWFPFLALVATQLLFIR",
+ proteins=["P12345"],
+ modification_options=[],
+ is_n_term=True,
+ is_c_term=False,
+ ),
+ search_space._PeptidoformSearchSpace(
+ sequence="LVWFPFLALVATQLLFIR",
+ proteins=["P12345"],
+ modification_options=[],
+ is_n_term=False,
+ is_c_term=False,
+ ),
+ search_space._PeptidoformSearchSpace(
+ sequence="NVSSLNLTNEYLHHK",
+ proteins=["P12345"],
+ modification_options=[],
+ is_n_term=False,
+ is_c_term=False,
+ ),
+ search_space._PeptidoformSearchSpace(
+ sequence="NVSSLNLTNEYLHHKCLVSEGK",
+ proteins=["P12345"],
+ modification_options=[],
+ is_n_term=False,
+ is_c_term=False,
+ ),
+ search_space._PeptidoformSearchSpace(
+ sequence="NVSSLNLTNEYLHHKCLVSEGKYKPGSK",
+ proteins=["P12345"],
+ modification_options=[],
+ is_n_term=False,
+ is_c_term=False,
+ ),
+ search_space._PeptidoformSearchSpace(
+ sequence="CLVSEGKYKPGSK",
+ proteins=["P12345"],
+ modification_options=[],
+ is_n_term=False,
+ is_c_term=False,
+ ),
+ search_space._PeptidoformSearchSpace(
+ sequence="CLVSEGKYKPGSKYEYI",
+ proteins=["P12345"],
+ modification_options=[],
+ is_n_term=False,
+ is_c_term=True,
+ ),
+ search_space._PeptidoformSearchSpace(
+ sequence="YKPGSKYEYI",
+ proteins=["P12345"],
+ modification_options=[],
+ is_n_term=False,
+ is_c_term=True,
+ ),
+ ]
+
+ sp = search_space.ProteomeSearchSpace(**test_input)
+ sp._digest_fasta()
+ assert test_output == sp._peptidoform_spaces
diff --git a/tests/test_single_prediction.py b/tests/test_single_prediction.py
deleted file mode 100644
index e0f0b523..00000000
--- a/tests/test_single_prediction.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from ms2pip.single_prediction import SinglePrediction
-from ms2pip.exceptions import InvalidPeptideError, InvalidModificationFormattingError
-
-MOD_TEST_CASES = {
- "": True,
- "-": True,
- "17|Cmm": True,
- "3|Cmm|11|Cmm": True,
- "7|Cm-:?M": True,
- "10|Cmm|": True,
- "-1|Oxidation": True,
- "|10|Cmm": False,
- "1|Pyro_glu ": False,
- "6|Cmm |25|Cmm": False,
- "221|Cmm": False,
- "-4|Oxidation": False,
-}
-
-PEPTIDE_TEST_CASES = {
- "HKA": True,
- "SETAPLAPTIPAPAEK": True,
- "TICIETIKGTCWQTVIDGR": True,
- "LASYAVBYR": False,
- "SPLTICYPEYTGSNTYEEAAAYIQCQFEDLNRR": True,
- "-KAVASVAK-": False,
- "VM(ox)SAFVEIIFDNRLPIDKEEVSLR": False,
- "HK": False,
- "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA": False,
-}
-
-
-class TestSinglePrediction:
- def test_validate_mod_string(self):
- for mod_string, is_valid in MOD_TEST_CASES.items():
- try:
- SinglePrediction._validate_mod_string(mod_string)
- except InvalidModificationFormattingError:
- passed = False
- else:
- passed = True
- assert passed == is_valid, f"{mod_string} incorrectly marked as {passed}"
-
- def test_validate_sequence(self):
- for peptide, is_valid in PEPTIDE_TEST_CASES.items():
- try:
- SinglePrediction._validate_sequence(peptide)
- except InvalidPeptideError:
- passed = False
- else:
- passed = True
- assert passed == is_valid, f"{peptide} incorrectly marked as {passed}"
diff --git a/tests/test_spectrum_output.py b/tests/test_spectrum_output.py
index 0760af58..9ddf8d65 100644
--- a/tests/test_spectrum_output.py
+++ b/tests/test_spectrum_output.py
@@ -1,100 +1,47 @@
-import os
-import re
+from psm_utils import Peptidoform
-import numpy as np
-import pandas as pd
+from ms2pip.spectrum_output import MSP, Bibliospec, DLIB
-from ms2pip.ms2pip_tools.spectrum_output import SpectrumOutput
+class TestMSP:
+ def test__format_modification_string(self):
+ test_cases = [
+ ("ACDE/2", "Mods=0"),
+ ("AC[Carbamidomethyl]DE/2", "Mods=1/2,C,Carbamidomethyl"),
+ ("[Glu->pyro-Glu]-EPEPTIDEK/2", "Mods=1/0,E,Glu->pyro-Glu"),
+ ("PEPTIDEK-[Amidated]/2", "Mods=1/-1,K,Amidated"),
+ ("AM[Oxidation]C[Carbamidomethyl]DE/2", "Mods=2/2,M,Oxidation/3,C,Carbamidomethyl"),
+ ]
-TEST_DIR = os.path.dirname(__file__)
-
-
-class TestSpectrumOutput:
- def test_integration(self):
- def compare_line(test_line, target_line):
- """Assert if two lines in spectrum output are the same."""
-
- # Extract float values from line and use assert_allclose, to allow for
- # float imprecisions
- float_pattern = re.compile(r"[0-9]*[.][0-9]+")
- test_floats = float_pattern.findall(test_line)
- target_floats = float_pattern.findall(target_line)
- assert len(test_floats) == len(target_floats)
- [
- np.testing.assert_allclose(float(te), float(ta), rtol=1e-5)
- for te, ta in zip(test_floats, target_floats)
- ]
- assert float_pattern.sub(test_line, "") == float_pattern.sub(
- target_line, ""
- )
-
- peprec = pd.read_pickle(
- os.path.join(TEST_DIR, "test_data/spectrum_output/input_peprec.pkl")
- )
- all_preds = pd.read_pickle(
- os.path.join(TEST_DIR, "test_data/spectrum_output/input_preds.pkl")
- )
-
- params = {
- "ptm": [
- "Oxidation,15.994915,opt,M",
- "Carbamidomethyl,57.021464,opt,C",
- "Glu->pyro-Glu,-18.010565,opt,E",
- "Gln->pyro-Glu,-17.026549,opt,Q",
- "Acetyl,42.010565,opt,N-term",
- ],
- "sptm": [],
- "gptm": [],
- "model": "HCD",
- "frag_error": "0.02",
- "out": "csv",
- }
-
- peprec_tmp = peprec.sample(5, random_state=10).copy()
- all_preds_tmp = all_preds[
- all_preds["spec_id"].isin(peprec_tmp["spec_id"])
- ].copy()
-
- so = SpectrumOutput(
- all_preds_tmp,
- peprec_tmp,
- params,
- output_filename="test",
- return_stringbuffer=True,
- )
+ for peptidoform_str, expected_output in test_cases:
+ peptidoform = Peptidoform(peptidoform_str)
+ assert MSP._format_modifications(peptidoform) == expected_output
- target_filename_base = os.path.join(
- TEST_DIR, "test_data/spectrum_output/target"
- )
- # Test general output
+class TestBiblioSpec:
+ def test__format_modified_sequence(self):
test_cases = [
- (so.write_mgf, "_predictions.mgf"),
- (so.write_msp, "_predictions.msp"),
- (so.write_spectronaut, "_predictions_spectronaut.csv"),
+ ("ACDE/2", "ACDE"),
+ ("AC[Carbamidomethyl]DE/2", "AC[+57.0]DE"),
+ ("[Glu->pyro-Glu]-EPEPTIDEK/2", "E[-18.0]PEPTIDEK"),
+ ("PEPTIDEK-[Amidated]/2", "PEPTIDEK[-1.0]"),
+ ("AM[Oxidation]C[Carbamidomethyl]DE/2", "AM[+16.0]C[+57.0]DE"),
]
- for test_function, file_ext in test_cases:
- test = test_function()
- test.seek(0)
- with open(target_filename_base + file_ext) as target:
- for test_line, target_line in zip(test.readlines(), target.readlines()):
- compare_line(test_line, target_line)
+ for peptidoform_str, expected_output in test_cases:
+ peptidoform = Peptidoform(peptidoform_str)
+ assert Bibliospec._format_modified_sequence(peptidoform) == expected_output
+
- # Test bibliospec output
- bibliospec_ssl, bibliospec_ms2 = so.write_bibliospec()
+class TestDLIB:
+ def test__format_modified_sequence(self):
test_cases = [
- (bibliospec_ssl, "_predictions.ssl"),
- (bibliospec_ms2, "_predictions.ms2"),
+ ("ACDE/2", "ACDE"),
+ ("AC[Carbamidomethyl]DE/2", "AC[+57.021464]DE"),
+ ("[Glu->pyro-Glu]-EPEPTIDEK/2", "E[-18.010565]PEPTIDEK"),
+ ("PEPTIDEK-[Amidated]/2", "PEPTIDEK[-0.984016]"),
+ ("AM[Oxidation]C[Carbamidomethyl]DE/2", "AM[+15.994915]C[+57.021464]DE"),
]
- for test, file_ext in test_cases:
- test.seek(0)
- with open(target_filename_base + file_ext) as target:
- for test_line, target_line in zip(test.readlines(), target.readlines()):
- test_line = test_line.replace(
- "test_predictions.ms2", "target_predictions.ms2"
- )
- if not "CreationDate" in target_line:
- compare_line(test_line, target_line)
+ for test_in, expected_out in test_cases:
+ assert DLIB._format_modified_sequence(Peptidoform(test_in)) == expected_out
diff --git a/train_scripts/compute_pearson.py b/train_scripts/compute_pearson.py
deleted file mode 100644
index a2e9b1d2..00000000
--- a/train_scripts/compute_pearson.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import sys
-import math
-#from scipy import spatial
-#import numpy as np
-#from scipy.stats import pearsonr
-#from sklearn.metrics import mean_absolute_error
-
-"""
-def spectral_angle(X,Y):
- epsilon = 1e-07
- true = np.array(X)
- pred = np.array(Y)
- pred_masked = ((true + 1) * pred) / (true + 1 + epsilon)
- true_masked = ((true + 1) * true) / (true + 1 + epsilon)
- pred_norm = true_masked / math.sqrt(max(sum(true_masked**2), epsilon))
- true_norm = pred_masked / math.sqrt(max(sum(pred_masked**2), epsilon))
- return (2.*math.acos(spatial.distance.cosine(true_norm,pred_norm))/math.pi)
-"""
-
-def pearson(X, Y):
- Xmean = sum(X)/len(X)
- Ymean = sum(Y)/len(Y)
-
- x = [var - Xmean for var in X]
- y = [var - Ymean for var in Y]
-
- xy = [a * b for a, b in list(zip(x, y))]
- sum_xy = sum(xy)
-
- x_square = [a * a for a in x]
- y_square = [b * b for b in y]
-
- sum_x_square = sum(x_square)
- sum_y_square = sum(y_square)
-
- sum_x_square_sum_y_square = sum_x_square * sum_y_square
- sqrt_sum_x_square_sum_y_square = math.sqrt(sum_x_square_sum_y_square)
-
- tmp = sqrt_sum_x_square_sum_y_square
- if tmp == 0:
- return 0
- return sum_xy / tmp
-
-
-x = []
-y = []
-buf = []
-prev = ""
-print("Title ms2pip-pearsonr")
-with open(sys.argv[1]) as f:
- f.readline()
- for row in f:
- l = row.rstrip().split(",")
- if (l[2] == "B") & (l[3] == "1"):
- if len(x) != 0:
- #print("%s %f %f"%(prev,spectral_angle(x,y),pearson(x,y)))
- #print(">%s %f"%(prev,pearson(x,y)))
- buf.append(pearson(x, y))
- prev = l[0]
- x = []
- y = []
- if int(l[1]) < 2: continue
- if int(l[1]) > 4: continue
- if l[2] == "B2": continue
- if l[2] == "B2": continue
- #if l[2] != "2": continue
- #if l[2] == "Y": continue
- x.append(float(l[5]))
- y.append(float(l[6]))
- #x.append(max(min(float(l[5]),1.),0))
- #y.append(max(min(float(l[6]),1.),0))
- #x.append(2**(float(l[5])+0.001))
- #y.append(2**(float(l[6])+0.001))
-
-print(sorted(buf)[int(len(buf) / 2)])
diff --git a/train_scripts/merge_h5.py b/train_scripts/merge_h5.py
deleted file mode 100644
index 587b5dcd..00000000
--- a/train_scripts/merge_h5.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import sys
-import pandas as pd
-
-tmp = []
-for fn in sys.argv[1:]:
- tmp.append(pd.read_hdf(fn, key='table'))
-
-tmp2 = pd.concat(tmp)
-tmp2.to_hdf("merged.h5", "table")
diff --git a/train_scripts/plot_optimization_result.py b/train_scripts/plot_optimization_result.py
deleted file mode 100644
index 77c7353a..00000000
--- a/train_scripts/plot_optimization_result.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import sys
-import pandas as pd
-import matplotlib.pyplot as plt
-from matplotlib.backends.backend_pdf import PdfPages
-import seaborn as sns
-
-data=[]
-with open(sys.argv[1]) as f:
- for row in f:
- l=row.replace(">>","").split()
- x = []
- for i in range(0,len(l)-5):
- x.append(float(l[i]))
- xx = x.copy()
- xx.append(float(l[-5]))
- xx.append(10)
- data.append(xx)
- xx = x.copy()
- xx.append(float(l[-4]))
- xx.append(30)
- data.append(xx)
- xx = x.copy()
- xx.append(float(l[-3]))
- xx.append(50)
- data.append(xx)
- xx = x.copy()
- xx.append(float(l[-2]))
- xx.append(70)
- data.append(xx)
- xx = x.copy()
- xx.append(float(l[-1]))
- xx.append(90)
- data.append(xx)
-
-d = pd.DataFrame(data,columns=["charge","eval-set","max_depth","num_leaves","value","percentile"])
-
-with PdfPages('%s.pdf'%sys.argv[1]) as pdf:
- sns.catplot(x="percentile",y="value",hue="charge",col="eval-set",data=d,kind="point",ci="sd")
- pdf.savefig()
- plt.close()
- sns.catplot(x="percentile",y="value",hue="max_depth",col="eval-set",data=d,kind="point",ci="sd")
- pdf.savefig()
- plt.close()
- sns.catplot(x="percentile",y="value",hue="num_leaves",col="eval-set",data=d,kind="point",ci="sd")
- pdf.savefig()
- plt.close()
-
diff --git a/train_scripts/run_synth_kuster.py b/train_scripts/run_synth_kuster.py
deleted file mode 100644
index 4650c170..00000000
--- a/train_scripts/run_synth_kuster.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import sys
-import os.path
-import multiprocessing
-
-def runM(dn, x):
- os.system("python3.5 ms2pipC.py -m 1 -c config.file -w %s.h5 -s %s %s"% (dn,dn,dn.replace("/mgf/","/peprecs/") + ".peprecR"))
-
-myPool = multiprocessing.Pool(24)
-
-for dn in sys.argv[1:]:
- t = myPool.apply_async(runM, args=(dn,0))
-
-myPool.close()
-myPool.join()
diff --git a/train_scripts/train_lightGBM.py b/train_scripts/train_lightGBM.py
deleted file mode 100644
index 04d9dfcb..00000000
--- a/train_scripts/train_lightGBM.py
+++ /dev/null
@@ -1,191 +0,0 @@
-import os
-import sys
-import argparse
-import pandas as pd
-import numpy as np
-import lightgbm as lgb
-from sklearn.metrics import precision_recall_curve
-from sklearn.metrics import average_precision_score
-import random
-import operator
-import pickle as pickle
-import matplotlib.pyplot as plt
-
-np.random.seed(1)
-
-
-def load_data(vector_filename, ion_type):
- # Read file
- if vector_filename.split(".")[-1] == "pkl":
- vectors = pd.read_pickle(vector_filename)
- elif vector_filename.split(".")[-1] == "h5":
- # vectors = pd.read_hdf(vector_filename, key='table', stop=1000)
- vectors = pd.read_hdf(vector_filename, key="table")
- else:
- print("Unsuported feature vector format")
- exit(1)
-
- # Extract targets for given ion type
- target_names = list(vectors.columns[vectors.columns.str.contains("targets")])
- if not "targets{}".format(ion_type) in target_names:
- print("Targets for {} could not be found in vector file.".format(ion_type))
- print("Vector file only contains these targets: {}".format(target_names))
- exit(1)
-
- targets = vectors.pop("targets{}".format(ion_type))
- target_names.remove("targets{}".format(ion_type))
- for n in target_names:
- vectors.pop(n)
-
- # Get psmids
- psmids = vectors.pop("psmid")
-
- return (vectors, targets, psmids)
-
-
-fragtype = "y"
-nul_cpu = 24
-
-print("loading train data")
-
-vectors, targets, psmids = load_data(sys.argv[1], fragtype)
-
-print("Splitting up into train and test set...")
-upeps = psmids.unique()
-np.random.shuffle(upeps)
-test_psms = upeps[: int(len(upeps) * 0.3)]
-
-train_vectors = vectors[~psmids.isin(test_psms)]
-train_targets = targets[~psmids.isin(test_psms)]
-train_psmids = psmids[~psmids.isin(test_psms)]
-
-test_vectors = vectors[psmids.isin(test_psms)]
-test_targets = targets[psmids.isin(test_psms)]
-test_psmids = psmids[psmids.isin(test_psms)]
-
-print("Creating LightGBM datastructures...")
-data = lgb.Dataset(train_vectors, label=train_targets)
-datatest = lgb.Dataset(test_vectors, label=test_targets)
-
-valid_sets = [datatest]
-vector_sets = [test_vectors]
-target_sets = [test_targets]
-psmid_sets = [test_psmids]
-
-print("loading evaluation data")
-for fn in sys.argv[2:]:
- vectors, targets, psmids = load_data(fn, fragtype)
- tmp = lgb.Dataset(vectors, label=targets)
- valid_sets.append(tmp)
- psmid_sets.append(psmids)
- vector_sets.append(vectors)
- target_sets.append(targets)
-
-sys.stderr.write("loading data done\n")
-
-tmp2 = pd.DataFrame()
-tmp3 = pd.DataFrame()
-tmp3["psmid"] = test_psmids[test_vectors["charge"] == 3]
-tmp3["target"] = test_targets[test_vectors["charge"] == 3]
-tmp4 = pd.DataFrame()
-tmp4["psmid"] = test_psmids[test_vectors["charge"] == 4]
-tmp4["target"] = test_targets[test_vectors["charge"] == 4]
-for max_depth in [7, 9, 11]:
- for num_leaves in [50, 100, 200]:
- params = {}
- params["objective"] = "regression"
- params["metric"] = "l1"
- params["learning_rate"] = 0.8
- # params['sub_feature'] = 1
- params["num_leaves"] = num_leaves
- # params['min_data'] = 50
- params["max_depth"] = max_depth
-
- num_round = 100
- # lgb.cv(param, data, num_round, nfold=5)
- bst = lgb.train(params, data, num_round, valid_sets=valid_sets)
-
- for c in [2, 3, 4]:
- for i in range(len(valid_sets)):
- tmp = pd.DataFrame()
- tmp["psmid"] = psmid_sets[i][vector_sets[i]["charge"] == c]
- tmp["target"] = target_sets[i][vector_sets[i]["charge"] == c]
- tmp["prediction"] = bst.predict(
- vector_sets[i][vector_sets[i]["charge"] == c]
- )
- tmpp = (
- tmp.groupby("psmid")[["target", "prediction"]].corr().iloc[0::2, -1]
- )
- print(
- ">>%i %i %i %i %s"
- % (
- c,
- i,
- max_depth,
- num_leaves,
- " ".join(
- [
- str(x)
- for x in np.nanpercentile(
- tmpp.values, [10, 30, 50, 70, 90]
- )
- ]
- ),
- )
- )
-
-exit()
-# bst.save_model('model.txt')
-print(bst.feature_importance())
-model_json = bst.dump_model()
-print(model_json["tree_info"])
-
-
-def parseOneTree(root, index, array_type="double", return_type="double"):
- def ifElse(node):
- if "leaf_index" in node:
- return "return " + str(node["leaf_value"]) + ";"
- else:
- condition = "arr[" + str(node["split_feature"]) + "]"
- if node["decision_type"] == "no_greater":
- condition += " <= " + str(node["threshold"])
- else:
- condition += " == " + str(node["threshold"])
- left = ifElse(node["left_child"])
- right = ifElse(node["right_child"])
- return "if ( " + condition + " ) { " + left + " } else { " + right + " }"
-
- return (
- return_type
- + " predictTree"
- + str(index)
- + "("
- + array_type
- + "[] arr) { "
- + ifElse(root)
- + " }"
- )
-
-
-def parseAllTrees(trees, array_type="double", return_type="double"):
- return (
- "\n\n".join(
- [
- parseOneTree(tree["tree_structure"], idx, array_type, return_type)
- for idx, tree in enumerate(trees)
- ]
- )
- + "\n\n"
- + return_type
- + " predict("
- + array_type
- + "[] arr) { "
- + "return "
- + " + ".join(["predictTree" + str(i) + "(arr)" for i in range(len(trees))])
- + ";"
- + "}"
- )
-
-
-with open("if.else", "w+") as f:
- f.write(parseAllTrees(model_json["tree_info"]))
diff --git a/train_scripts/train_lightGBM_single.py b/train_scripts/train_lightGBM_single.py
deleted file mode 100644
index 84c1e009..00000000
--- a/train_scripts/train_lightGBM_single.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import os
-import sys
-import argparse
-import pandas as pd
-import numpy as np
-import lightgbm as lgb
-from sklearn.metrics import precision_recall_curve
-from sklearn.metrics import average_precision_score
-import random
-import operator
-import pickle as pickle
-import matplotlib.pyplot as plt
-
-def load_data(vector_filename, ion_type):
- # Read file
- if vector_filename.split('.')[-1] == 'pkl':
- vectors = pd.read_pickle(vector_filename)
- elif vector_filename.split('.')[-1] == 'h5':
- #vectors = pd.read_hdf(vector_filename, key='table', stop=1000)
- vectors = pd.read_hdf(vector_filename, key='table')
- else:
- print("Unsuported feature vector format")
- exit(1)
- print("{} contains {} feature vectors".format(args.vectors, len(vectors)))
-
- #vectors = vectors.sample(frac=0.3)
-
- # Extract targets for given ion type
- target_names = list(vectors.columns[vectors.columns.str.contains('targets')])
- if not 'targets_{}'.format(ion_type) in target_names:
- print("Targets for {} could not be found in vector file.".format(ion_type))
- print("Vector file only contains these targets: {}".format(target_names))
- exit(1)
-
- targets = vectors.pop('targets_{}'.format(ion_type))
- target_names.remove('targets_{}'.format(ion_type))
- for n in target_names:
- vectors.pop(n)
-
- # Get psmids
- psmids = vectors.pop('psmid')
-
- #vectors = vectors.astype(int)
- #print(vectors.dtypes)
-
- return(vectors, targets, psmids)
-
-
-sys.stderr.write('loading data\n')
-
-parser = argparse.ArgumentParser(description='XGBoost training')
-parser.add_argument('vectors', metavar='<_vectors.pkl>',
- help='feature vector file')
-parser.add_argument('type', metavar='',
- help='model type')
-parser.add_argument('-c', metavar='INT', action="store", dest='num_cpu', default=24,
- help='number of CPUs to use')
-parser.add_argument('-t', metavar='INT', action="store", dest='num_trees', default=30,
- help='number of trees in XGBoost model')
-parser.add_argument('-e', metavar='FILE', action="store", dest='vectorseval',
- help='additional evaluation file')
-parser.add_argument("-p", action="store_true", dest='make_plots', default=False,
- help="output plots")
-parser.add_argument("-g", action="store_true", dest='gridsearch', default=False,
- help="perform Grid Search CV to select best parameters")
-args = parser.parse_args()
-
-np.random.seed(1)
-
-filename = "{}_{}_{}".format(args.vectors.split('.')[-2], args.num_trees, args.type)
-print("Using output filename {}".format(filename))
-
-print("Loading train and test data...")
-vectors, targets, psmids = load_data(args.vectors, args.type)
-
-print("Splitting up into train and test set...")
-upeps = psmids.unique()
-np.random.shuffle(upeps)
-test_psms = upeps[:int(len(upeps) * 0.2)]
-
-train_vectors = vectors[~psmids.isin(test_psms)]
-train_targets = targets[~psmids.isin(test_psms)]
-train_psmids = psmids[~psmids.isin(test_psms)]
-
-test_vectors = vectors[psmids.isin(test_psms)]
-test_targets = targets[psmids.isin(test_psms)]
-test_psmids = psmids[psmids.isin(test_psms)]
-
-# Rename features to understand decision tree dump
-#train_vectors.columns = ['Feature' + str(i) for i in range(len(train_vectors.columns))]
-#test_vectors.columns = ['Feature' + str(i) for i in range(len(test_vectors.columns))]
-
-print(train_vectors.shape)
-print("Creating LightGBM datastructures...")
-
-print(train_vectors.dtypes)
-
-data = lgb.Dataset(train_vectors, label=train_targets)
-datatest = lgb.Dataset(test_vectors, label=test_targets)
-sys.stderr.write('loading data done\n')
-
-params = {}
-params['objective'] = 'regression'
-params['metric'] = 'l1'
-params['learning_rate'] = 0.4
-#params['sub_feature'] = 1
-params['num_leaves'] = 100
-params['min_data'] = 50
-params['max_depth'] = 32
-
-num_round = 10
-#lgb.cv(param, data, num_round, nfold=5)
-bst = lgb.train(params, data, num_round, valid_sets=[datatest])
-
-#bst.save_model('model.txt')
-print(bst.feature_importance())
-model_json = bst.dump_model()
-print(model_json["tree_info"])
-
-def parseOneTree(root, index, array_type='double', return_type='double'):
- def ifElse(node):
- if 'leaf_index' in node:
- return 'return ' + str(node['leaf_value']) + ';'
- else:
- condition = 'arr[' + str(node['split_feature']) + ']'
- if node['decision_type'] == '<=':
- condition += ' <= ' + str(node['threshold'])
- else:
- condition += ' == ' + str(node['threshold'])
- left = ifElse(node['left_child'])
- right = ifElse(node['right_child'])
- return 'if ( ' + condition + ' ) { ' + left + ' } else { ' + right + ' }'
- return return_type + ' predictTree' + str(index) + '(' + array_type + ' arr) { ' + ifElse(root) + ' }'
-
-def parseAllTrees(trees, array_type='unsigned int*', return_type='float'):
- return '\n\n'.join([parseOneTree(tree['tree_structure'], idx, array_type, return_type) for idx, tree in enumerate(trees)]) \
- + '\n\n' + return_type + ' score_Y(' + array_type + ' arr) { ' \
- + 'return ' + ' + '.join(['predictTree' + str(i) + '(arr)' for i in range(len(trees))]) + ';' \
- + '}'
-
-with open('if.else', 'w+') as f:
- f.write(parseAllTrees(model_json["tree_info"]))
-