Use the latest metabolomics algorithms with a few lines of code

Omigami is an open source Python and R package that gives you access to scalable APIs for the latest metabolomics algorithms
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Credit: Courtesy of Richard Yost
Omigami APIs are built in collaboration with the research labs who are at the forefront of novel algorithms as Wageningen University, The Dorrestein Lab and Fiehn Lab. Omigami makes it easy to use and test a new algorithm and pre-trained model in your metabolomics pipeline - shortly after publication.

Use new algorithms in less than 5 minutes

Omgami is backed by an auto-scaling kubernetes infrastructure that adjusts to your dataset. Never wait days for a result again.
Python
R (Coming soon)
pip install omigami_client
from omigami_client import Spec2VecClient
client = Spec2VecClient­(token="your_token")
mgf_file_path = "path_to_file.mgf"
n_best_matches = 10
result = client.match_spectra_from_path­(mgf_file_path,
n_best_matches)
Long term support feature

Long term support

Omigami APIs are maintained, documented and supported by a full-time team of researchers and machine learning engineers - and funded by a auto-renewing grant from Data Revenue.
Apis that scale feature

APIs that scale with your dataset

Omgami is backed by an auto-scaling kubernetes infrastructure that adjusts to your dataset. Never wait days for a result again.
Free forever feature

Free forever

Omigami is free for academic use. Financing comes exclusively from commercial projects. If you want to use Omigami APIs for a commercial project reach out to us
Transparent algorithms feature

Transparent algorithms

The algorithms behind all APIs are open source. You can find detailed information on each and every model in the documentation

Try a better algorithms - upgrade your pipeline today

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Spec2Vec

Find more accurate library matches for your MS/MS spectra.
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MS2DeepScore

Supervised deep learning model trained to predict structural similarity directly from MS-spectra.