MS2DECIDE: Aggregating Multiannotated Tandem Mass Spectrometry Data with Decision Theory Enhances Natural Products Prioritization. Mejri, Y., Cailloux, O., Otogo N’Nang, E., Séon-Méniel, B., Gallard, J., Le Pogam, P., Öztürk-Escoffier, M., & Beniddir, M. A. Chemistry–Methods, 2025. ![MS2DECIDE: Aggregating Multiannotated Tandem Mass Spectrometry Data with Decision Theory Enhances Natural Products Prioritization [link]](https://bibbase.org/img/filetypes/link.svg "link")
Hal doi abstract bibtex 3 downloads Mass spectrometry-based natural products (NPs) targeted discovery often relies on a complicated decision-making process involving tedious comparison of exact masses data and tandem mass spectra-based annotation tools output against various spectral reference libraries. To address this bottleneck, tandem mass spectrum to decision (MS2DECIDE) is presented which leverages decision theory and expert knowledge to aggregate the outputs of three widely used annotation tools (GNPS, Sirius, and ISDB-LOTUS) and computes a recommendation for targeting NPs with regard to their potential novelty. We demonstrate, through two case studies, that MS2DECIDE reliably captures the novelty of natural products from their tandem mass spectra. MS2DECIDE is freely accessible on GitHub.
@article{mejri_ms2decide_2025,
author = {Mejri, Yassine and Cailloux, Olivier and Otogo N’Nang, Elvis and Séon-Méniel, Blandine and Gallard, Jean-François and Le Pogam, Pierre and Öztürk-Escoffier, Meltem and Beniddir, Mehdi A.},
year = {2025},
title = {MS2DECIDE: Aggregating Multiannotated Tandem Mass Spectrometry Data with Decision Theory Enhances Natural Products Prioritization},
journal = {Chemistry–Methods},
keywords = {alkaloids, decision theory, multiannotation, natural products, prioritization},
doi = {10.1002/cmtd.202400088},
url_HAL = {https://hal.science/hal-05286889},
abstract = {Mass spectrometry-based natural products (NPs) targeted discovery often relies on a complicated decision-making process involving tedious comparison of exact masses data and tandem mass spectra-based annotation tools output against various spectral reference libraries. To address this bottleneck, tandem mass spectrum to decision (MS2DECIDE) is presented which leverages decision theory and expert knowledge to aggregate the outputs of three widely used annotation tools (GNPS, Sirius, and ISDB-LOTUS) and computes a recommendation for targeting NPs with regard to their potential novelty. We demonstrate, through two case studies, that MS2DECIDE reliably captures the novelty of natural products from their tandem mass spectra. MS2DECIDE is freely accessible on GitHub.}
}
Downloads: 3
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