Omigami

A package to access Omigami services.

Installation

Omigami supports python 3.7 and 3.8. To install it, simply:

pip install omigami

To be able to visualise the molecule plots, you need to install rdkit. For Mac and Linux users, please do:

pip install omigami[plots]

For Windows users, you need to do the following:

conda install -c conda-forge rdkit

Acknowledgement

Spec2Vec

Huber F, Ridder L, Verhoeven S, Spaaks JH, Diblen F, Rogers S, et al. (2021) Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships. PLoS Comput Biol 17(2): e1008724. https://doi.org/10.1371/journal.pcbi.1008724

MS2DeepScore

Florian Huber, Sven van der Burg, Justin J.J. van der Hooft, Lars Ridder. (2021) MS2DeepScore - a novel deep learning similarity measure for mass fragmentation spectrum comparisons. bioRxiv 2021, doi: https://doi.org/10.1101/2021.04.18.440324

Motivation

Motivation We aim to support metabolomics research by providing the following :

• Easy-to-use tools
• Access and scalability to the newest algorithms
• Maintenance, support and documentation of software, models and data
• A community of metabolomics researchers via our Slack

Features

• Spec2Vec spectra matching
• MS2Deep score

Usage

Spec2Vec

Quickstart

from omigami import Spec2Vec

client = Spec2Vec(token="your_token")
mgf_file_path = "path_to_file.mgf"
n_best_matches = 10
include_metadata = ["Smiles", "Compound_name"]
ion_mode = "positive"  # either positive or negative

result = client.match_spectra_from_path(
    mgf_file_path, n_best_matches, include_metadata, ion_mode=ion_mode,
)

The supported metadata keys for omigami are (case insensitive):

• "smiles",
• "compound_name",
• "instrument",
• "parent_mass",
• "inchikey_smiles",
• "inchikey_inchi"

Notebooks

You can find a tutorial notebook in the /notebooks/ folder.

MS2DeepScore

Quickstart

from omigami import MS2DeepScore

client = MS2DeepScore(token="your_token")

mgf_file_path = "path_to_file.mgf"
n_best_matches = 10
include_metadata = ["Smiles", "Compound_name"]
ion_mode = "positive"  # either positive or negative

result = client.match_spectra_from_path(
    mgf_file_path, n_best_matches, include_metadata, ion_mode=ion_mode,
)

Notebooks

You can find a tutorial notebook in the /notebooks/ folder.

Plotting

Plotting graphs works the same way for both Spec2Vec and MS2DeepScore.

The following example will plot the structures of the molecules

from omigami import MoleculePlotter
plotter = MoleculePlotter()
plotter.plot_molecule_structure_grid(best_matches, 
                                     draw_indices=True, 
                                     molecule_image_size=[600, 600], 
                                     substructure_highlight="C(=O)")

The following code allows us to plot the results of the Classyfire model API.

from omigami import MoleculePlotter
plotter = MoleculePlotter()
plotter.plot_classyfire_result(best_matches)

Furthermore, Omigami provides the possibility to use the NPClassifier API.

from omigami import MoleculePlotter
plotter = MoleculePlotter()
plotter.plot_NPclassifier_result(best_matches, color='orange')

How it works

Spec2Vec

1. Save your spectra data in a MGF file locally
2. Create an Spec2Vec with your user token
3. Call match_spectra_from_path with the location of your mgf file.
4. The MGF spectra data will be processed and sent to the spec2vec model that will convert it into embeddings.
5. These embeddings will be compared against the reference embeddings around the Precursor MZ.
6. The N best matches per spectrum are returned on the response as pandas dataframes.

MS2DeepScore

1. Save your pair of spectra data in a MGF file locally
2. Create an MS2DeepScore object with your user token
3. Call match_spectra_from_path with the location of your mgf file.
4. The MGF spectra data will be processed and sent to the trained neural network that will predict the molecular structural similarity.
5. The prediction is returned on the response as a pandas dataframe.

Contribute to Omigami

1. Fork it (https://github.com/omigami/omigami/fork)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request

License

MIT license - free software.