Matchms is an open-source Python package to import, process, clean, and compare mass spectrometry data (MS/MS). It allows to implement and run an easy-to-follow, easy-to-reproduce workflow from raw mass spectra to pre- and post-processed spectral data. Spectral data can be imported from common formats such mzML, mzXML, msp, metabolomics-USI, MGF, or json (e.g. GNPS-syle json files). Matchms then provides filters for metadata cleaning and checking, as well as for basic peak filtering. Finally, matchms was build to import and apply different similarity measures to compare large amounts of spectra. This includes common Cosine scores, but can also easily be extended by custom measures. One example for a spectrum similarity measure that was designed to work in matchms is Spec2Vec.

If you use matchms in your research, please cite the following software paper:

F Huber, S. Verhoeven, C. Meijer, H. Spreeuw, E. M. Villanueva Castilla, C. Geng, J.J.J. van der Hooft, S. Rogers, A. Belloum, F. Diblen, J.H. Spaaks, (2020). matchms - processing and similarity evaluation of mass spectrometry data. Journal of Open Source Software, 5(52), 2411, https://doi.org/10.21105/joss.02411

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For more extensive documentation see our readthedocs and our matchms introduction tutorial.

Prerequisites:

• Python 3.7, 3.8 or 3.9
• Anaconda (recommended)

We recommend installing matchms from Anaconda Cloud with

# install matchms in a new virtual environment to avoid dependency clashes
conda create --name matchms python=3.8
conda activate matchms
conda install --channel bioconda --channel conda-forge matchms

Alternatively, matchms can also be installed using pip but users will then either have to install rdkit on their own or won't be able to use the entire functionality. Without rdkit installed several filter functions related to processing and cleaning chemical metadata will not run. To install matchms with pip simply run

pip install matchms

Matchms functionalities can be complemented by additional packages. To date we are aware of:

• Spec2Vec an alternative machine-learning spectral similarity score that can simply be installed by pip install spec2vec and be imported as from spec2vec import Spec2Vec following the same API as the scores in matchms.similarity.
• MS2DeepScore a supervised, deep-learning based spectral similarity score that can simply be installed by pip install ms2deepscore and be imported as from ms2deepscore import MS2DeepScore following the same API as the scores in matchms.similarity.
• matchmsextras which contains additional functions to create networks based on spectral similarities, to run spectrum searchers against PubChem, or additional plotting methods.

(if you know of any other packages that are fully compatible with matchms, let us know!)

To get started with matchms, we recommend following our matchms introduction tutorial.

Alternatively, here below is a small example of using matchms to calculate the Cosine score between mass Spectrums in the tests/pesticides.mgf file.

from matchms.importing import load_from_mgf
from matchms.filtering import default_filters
from matchms.filtering import normalize_intensities
from matchms import calculate_scores
from matchms.similarity import CosineGreedy

# Read spectrums from a MGF formatted file, for other formats see https://matchms.readthedocs.io/en/latest/api/matchms.importing.html 
file = load_from_mgf("tests/pesticides.mgf")

# Apply filters to clean and enhance each spectrum
spectrums = []
for spectrum in file:
    # Apply default filter to standardize ion mode, correct charge and more.
    # Default filter is fully explained at https://matchms.readthedocs.io/en/latest/api/matchms.filtering.html .
    spectrum = default_filters(spectrum)
    # Scale peak intensities to maximum of 1
    spectrum = normalize_intensities(spectrum)
    spectrums.append(spectrum)

# Calculate Cosine similarity scores between all spectrums
# For other similarity score methods see https://matchms.readthedocs.io/en/latest/api/matchms.similarity.html .
scores = calculate_scores(references=spectrums,
                          queries=spectrums,
                          similarity_function=CosineGreedy())

# Print the calculated scores for each spectrum pair
for score in scores:
    (reference, query, score) = score
    # Ignore scores between same spectrum and
    # pairs which have less than 20 peaks in common
    if reference is not query and score["matches"] >= 20:
        print(f"Reference scan id: {reference.metadata['scans']}")
        print(f"Query scan id: {query.metadata['scans']}")
        print(f"Score: {score['score']:.4f}")
        print(f"Number of matching peaks: {score['matches']}")
        print("----------------------------")

To install matchms, do:

git clone https://github.com/matchms/matchms.git
cd matchms
conda create --name matchms-dev python=3.8
conda activate matchms-dev
# Install rdkit using conda, rest of dependencies can be installed with pip
conda install -c conda-forge rdkit
python -m pip install --upgrade pip
pip install --editable .[dev]

Run the linter with:

prospector

Automatically fix incorrectly sorted imports:

isort --recursive .

Files will be changed in place and need to be committed manually.

Run tests (including coverage) with:

pytest

The conda packaging is handled by a recipe at Bioconda.

Publishing to PyPI will trigger the creation of a pull request on the bioconda recipes repository Once the PR is merged the new version of matchms will appear on https://anaconda.org/bioconda/matchms

If you want to contribute to the development of matchms, have a look at the contribution guidelines.

Copyright (c) 2020, Netherlands eScience Center

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

This package was created with Cookiecutter and the NLeSC/python-template.