FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events
Abstract Non-targeted metabolomics holds great promise for advancing precision medicine and biomarker discovery. However, identifying compounds from tandem mass spectra remains a challenging task due to the incomplete nature of spectral reference libraries. Augmenting these libraries with simulated...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-57422-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850072364010700800 |
|---|---|
| author | Yannek Nowatzky Francesco Friedrich Russo Jan Lisec Alexander Kister Knut Reinert Thilo Muth Philipp Benner |
| author_facet | Yannek Nowatzky Francesco Friedrich Russo Jan Lisec Alexander Kister Knut Reinert Thilo Muth Philipp Benner |
| author_sort | Yannek Nowatzky |
| collection | DOAJ |
| description | Abstract Non-targeted metabolomics holds great promise for advancing precision medicine and biomarker discovery. However, identifying compounds from tandem mass spectra remains a challenging task due to the incomplete nature of spectral reference libraries. Augmenting these libraries with simulated mass spectra can provide the necessary references to resolve unmatched spectra, but generating high-quality data is difficult. In this study, we present FIORA, an open-source graph neural network designed to simulate tandem mass spectra. Our main contribution lies in utilizing the molecular neighborhood of bonds to learn breaking patterns and derive fragment ion probabilities. FIORA not only surpasses state-of-the-art fragmentation algorithms, ICEBERG and CFM-ID, in prediction quality, but also facilitates the prediction of additional features, such as retention time and collision cross section. Utilizing GPU acceleration, FIORA enables rapid validation of putative compound annotations and large-scale expansion of spectral reference libraries with high-quality predictions. |
| format | Article |
| id | doaj-art-7c9eb1fa04064e9fa9b2dbf18ac4c636 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-7c9eb1fa04064e9fa9b2dbf18ac4c6362025-08-20T02:47:06ZengNature PortfolioNature Communications2041-17232025-03-0116111710.1038/s41467-025-57422-4FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation eventsYannek Nowatzky0Francesco Friedrich Russo1Jan Lisec2Alexander Kister3Knut Reinert4Thilo Muth5Philipp Benner6Section VP.1 eScience, Federal Institute for Materials Research and Testing (BAM)Department of Analytical Chemistry and Reference Materials, Organic Trace Analysis and Food Analysis, Federal Institute for Materials Research and Testing (BAM)Department of Analytical Chemistry and Reference Materials, Organic Trace Analysis and Food Analysis, Federal Institute for Materials Research and Testing (BAM)Section VP.1 eScience, Federal Institute for Materials Research and Testing (BAM)Department of Mathematics and Computer Science, Freie Universität BerlinDepartment of Mathematics and Computer Science, Freie Universität BerlinSection VP.1 eScience, Federal Institute for Materials Research and Testing (BAM)Abstract Non-targeted metabolomics holds great promise for advancing precision medicine and biomarker discovery. However, identifying compounds from tandem mass spectra remains a challenging task due to the incomplete nature of spectral reference libraries. Augmenting these libraries with simulated mass spectra can provide the necessary references to resolve unmatched spectra, but generating high-quality data is difficult. In this study, we present FIORA, an open-source graph neural network designed to simulate tandem mass spectra. Our main contribution lies in utilizing the molecular neighborhood of bonds to learn breaking patterns and derive fragment ion probabilities. FIORA not only surpasses state-of-the-art fragmentation algorithms, ICEBERG and CFM-ID, in prediction quality, but also facilitates the prediction of additional features, such as retention time and collision cross section. Utilizing GPU acceleration, FIORA enables rapid validation of putative compound annotations and large-scale expansion of spectral reference libraries with high-quality predictions.https://doi.org/10.1038/s41467-025-57422-4 |
| spellingShingle | Yannek Nowatzky Francesco Friedrich Russo Jan Lisec Alexander Kister Knut Reinert Thilo Muth Philipp Benner FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events Nature Communications |
| title | FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events |
| title_full | FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events |
| title_fullStr | FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events |
| title_full_unstemmed | FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events |
| title_short | FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events |
| title_sort | fiora local neighborhood based prediction of compound mass spectra from single fragmentation events |
| url | https://doi.org/10.1038/s41467-025-57422-4 |
| work_keys_str_mv | AT yanneknowatzky fioralocalneighborhoodbasedpredictionofcompoundmassspectrafromsinglefragmentationevents AT francescofriedrichrusso fioralocalneighborhoodbasedpredictionofcompoundmassspectrafromsinglefragmentationevents AT janlisec fioralocalneighborhoodbasedpredictionofcompoundmassspectrafromsinglefragmentationevents AT alexanderkister fioralocalneighborhoodbasedpredictionofcompoundmassspectrafromsinglefragmentationevents AT knutreinert fioralocalneighborhoodbasedpredictionofcompoundmassspectrafromsinglefragmentationevents AT thilomuth fioralocalneighborhoodbasedpredictionofcompoundmassspectrafromsinglefragmentationevents AT philippbenner fioralocalneighborhoodbasedpredictionofcompoundmassspectrafromsinglefragmentationevents |