MS2DECIDE: Aggregating Multi-Annotated Tandem Mass Spectrometry Data with Decision Theory Enhances Natural Products Prioritization

Mass spectrometry-based natural products 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, we present tandem mass spectrum to decision (MS2DECIDE) which leverages Decision Theory and expert knowledge to aggregate the outputs of three widely used annotation tools (GNPS, Sirius, and ISDB-LOTUS) and compute a recommendation for targeting natural products 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.