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Spec2vec is a novel spectral similarity score inspired by a natural language processing algorithm -- Word2Vec. Where Word2Vec learns relationships between words in sentences, spec2vec does so for mass fragments and neutral losses in MS/MS spectra. The spectral similarity score is based on spectral embeddings learnt from the fragmental relationships within a large set of spectral data.
If you use spec2vec for your research, please cite the following references:
Huber F, Ridder L, Verhoeven S, Spaaks JH, Diblen F, Rogers S, van der Hooft JJJ, (2021) "Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships". PLoS Comput Biol 17(2): e1008724. doi:10.1371/journal.pcbi.1008724
(and if you use matchms as well: 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 )
Thanks!
For more extensive documentation see our readthedocs or get started with our spec2vec introduction tutorial.
Since version 0.5.0 Spec2Vec uses gensim >= 4.0.0 which should make it faster and more future proof. Model trained with older versions should still be importable without any issues. If you had scripts that used additional gensim code, however, those might occationally need some adaptation, see also the gensim documentation on how to migrate your code.
Prerequisites:
- Python 3.7, 3.8, or 3.9
- Recommended: Anaconda
We recommend installing spec2vec from Anaconda Cloud with
conda create --name spec2vec python=3.8
conda activate spec2vec
conda install --channel bioconda --channel conda-forge spec2vecAlternatively, spec2vec can also be installed using pip. When using spec2vec together with matchms it is important to note that only the Anaconda install will make sure that also rdkit is installed properly, which is requried for a few matchms filter functions (it is not required for any spec2vec related functionalities though).
pip install spec2vecBelow a code example of how to process a large data set of reference spectra to
train a word2vec model from scratch. Spectra are converted to documents using SpectrumDocument which converts spectrum peaks into "words" according to their m/z ratio (for instance "peak@100.39"). A new word2vec model can then trained using train_new_word2vec_model which will set the training parameters to spec2vec defaults unless specified otherwise. Word2Vec models learn from co-occurences of peaks ("words") across many different spectra.
To get a model that can give a meaningful representation of a set of
given spectra it is desirable to train the model on a large and representative
dataset.
import os
import matchms.filtering as msfilters
from matchms.importing import load_from_mgf
from spec2vec import SpectrumDocument
from spec2vec.model_building import train_new_word2vec_model
def spectrum_processing(s):
"""This is how one would typically design a desired pre- and post-
processing pipeline."""
s = msfilters.default_filters(s)
s = msfilters.add_parent_mass(s)
s = msfilters.normalize_intensities(s)
s = msfilters.reduce_to_number_of_peaks(s, n_required=10, ratio_desired=0.5, n_max=500)
s = msfilters.select_by_mz(s, mz_from=0, mz_to=1000)
s = msfilters.add_losses(s, loss_mz_from=10.0, loss_mz_to=200.0)
s = msfilters.require_minimum_number_of_peaks(s, n_required=10)
return s
# Load data from MGF file and apply filters
spectrums = [spectrum_processing(s) for s in load_from_mgf("reference_spectrums.mgf")]
# Omit spectrums that didn't qualify for analysis
spectrums = [s for s in spectrums if s is not None]
# Create spectrum documents
reference_documents = [SpectrumDocument(s, n_decimals=2) for s in spectrums]
model_file = "references.model"
model = train_new_word2vec_model(reference_documents, iterations=[10, 20, 30], filename=model_file,
workers=2, progress_logger=True)Once a word2vec model has been trained, spec2vec allows to calculate the similarities
between mass spectrums based on this model. In cases where the word2vec model was
trained on data different than the data it is applied for, a number of peaks ("words")
might be unknown to the model (if they weren't part of the training dataset). To
account for those cases it is important to specify the allowed_missing_percentage,
as in the example below.
import gensim
from matchms import calculate_scores
from spec2vec import Spec2Vec
# query_spectrums loaded from files using https://matchms.readthedocs.io/en/latest/api/matchms.importing.load_from_mgf.html
query_spectrums = [spectrum_processing(s) for s in load_from_mgf("query_spectrums.mgf")]
# Omit spectrums that didn't qualify for analysis
query_spectrums = [s for s in query_spectrums if s is not None]
# Import pre-trained word2vec model (see code example above)
model_file = "references.model"
model = gensim.models.Word2Vec.load(model_file)
# Define similarity_function
spec2vec_similarity = Spec2Vec(model=model, intensity_weighting_power=0.5,
allowed_missing_percentage=5.0)
# Calculate scores on all combinations of reference spectrums and queries
scores = calculate_scores(reference_documents, query_spectrums, spec2vec_similarity)
# Find the highest scores for a query spectrum of interest
best_matches = scores.scores_by_query(query_documents[0], sort=True)[:10]
# Return highest scores
print([x[1] for x in best_matches])| Term | Description |
|---|---|
| adduct / addition product | During ionization in a mass spectrometer, the molecules of the injected compound break apart into fragments. When fragments combine into a new compound, this is known as an addition product, or adduct. Wikipedia |
| GNPS | Knowledge base for sharing of mass spectrometry data (link). |
InChI / INCHI |
InChI is short for International Chemical Identifier. InChIs are useful in retrieving information associated with a certain molecule from a database. |
InChIKey / InChI key / INCHIKEY |
An indentifier for molecules. For example, the InChI key for carbon
dioxide is InChIKey=CURLTUGMZLYLDI-UHFFFAOYSA-N (yes, it
includes the substring InChIKey=). |
| MGF File / Mascot Generic Format | A plan ASCII file format to store peak list data from a mass spectrometry experiment. Links: matrixscience.com, fiehnlab.ucdavis.edu. |
parent mass / parent_mass |
Actual mass (in Dalton) of the original compound prior to fragmentation. It can be recalculated from the precursor m/z by taking into account the charge state and proton/electron masses. |
precursor m/z / precursor_mz |
Mass-to-charge ratio of the compound targeted for fragmentation. |
| SMILES | A line notation for describing the structure of chemical species using
short ASCII strings. For example, water is encoded as O[H]O,
carbon dioxide is encoded as O=C=O, etc. SMILES-encoded species may be converted to InChIKey using a resolver like this one. The Wikipedia entry for SMILES is here. |
To install spec2vec, do:
git clone https://github.com/iomega/spec2vec.git
cd spec2vec
conda env create --file conda/environment-dev.yml
conda activate spec2vec-dev
pip install --editable .Run the linter with:
prospectorRun tests (including coverage) with:
pytestThe 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/spec2vec
To remove spec2vec package from the active environment:
conda remove spec2vecTo remove spec2vec environment:
conda env remove --name spec2vecIf you want to contribute to the development of spec2vec, have a look at the contribution guidelines.
Copyright (c) 2023, Netherlands eScience Center & Düsseldorf University of Applied Sciences
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.
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