Some more news from the SpecReBoot team! 🔁 After releasing our SpecReBoot preprint, one big question remained: Could our framework handle much larger datasets? Were we ready for repository-scale anal...

Thanks Esteban Charria Girón for all your contributions to the Bioinformatics Group. It has been a privilege to supervise and mentor you with Marnix Medema and to see you growing as a scientist and p...

Thanks to all participants of the Utrecht Bioinformatics Center Symposium today! 😎 It was my pleasure to highlight in my keynote how (the power of) collaboration enabled the creation of innovative me...

Many thanks to Daniel Globisch & Ingela Lanekoff for inviting me to Uppsala University to connect with the Nordic Metabolomics Society #community and deliver a #keynote on scalable and explainable omi...

🚨 New Preprint Alert 🚨 Many congrats 👏 🎉 to Larissa Della Vedova for posting her PhenoReWire preprint online! 😎 Are you working with microbiomes or microbial consortia? It is commonly known that sm...

🚨 New Preprint Alert 🚨 Congratulations 👏 🎉 to the CompMet team for presenting this preprint on boosting library matching for untargeted mass spectrometry-based metabolomics workflows 🙌 Metabolite a...

Many thanks Sven Heiles for the kind invitation to come to #Dortmund Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. to discuss metabolomics and multiomics strategies to better understan...

Goodbye 👋 Paula Derksen Macruz and mucho obrigado for your great contributions and your drive to learn many tools including #FERMO and #SpecReBoot during your six months' stay here with the CompMet Gr...

Goodbye 👋 Elena Ferri and grazie mille for your great contributions and your enthusiasm during your three months' stay here with the CompMet Group! Good luck with all your future metabolomics puzzles ...

🚨 Are you ready to reboot your molecular networks?! 😎 🚨 If you hadn't tried it yet, it is even faster now! Due to great team work, the SpecReBoot workflow is further optimized and we extended the b...

In my #nontarget2026 #Ascona keynote that kicked off the two computational sessions this morning, I highlighted several advances that are relevant for nontarget screening! 😎 Huge thanks 🙏 to the team...

Grazie mille per caldo benvenuto a Napoli! 😎 It has been a great first experience in the south of Italy where nature, history, and cuisine come together - and of course many #naturalproducts 🙂 The ...

Mass spectral molecular networking (MN) has emerged as a key computational approach to organize and analyze the vast volumes of tandem mass spectrometry (MS/MS) data generated in natural product research. MN connections are based on mass spectral similarities derived from cosine-based scores or machine learning-based scores such as Spec2Vec and MS2DeepScore. These similarity scores are single, deterministic values and provide no estimate of the statistical robustness of the inferred mass spectral connections. As a result, molecular networks frequently contain edges arising from noise, missing fragments, or experimental variability, while, simultaneously, authentic chemical relationships remain hidden. To remedy this situation, here, we introduce SpecReBoot, a statistical framework that adapts Felsensteins bootstrap principle from phylogenetics to metabolomics. Within this framework, mass fragmentation peaks are treated as resampling units with replacement to generate pseudo-replicate spectra. Spectral similarities are recalculated across replicates, and the robustness of each edge between a pair of spectra is quantified by how frequently they appear as mutual top-k neighbors across bootstrap replicates. This approach generates bootstrap-derived confidence scores for every spectral connection, transforming mass spectral similarity from an absolute score into a distribution-based, confidence-aware measure. We show how, across public GNPS spectral library and natural products discovery case study datasets on bioactive metabolites produced by bacteria and fungi, SpecReBoot reliably identifies high-confidence spectral connections, filters unstable or noise-driven edges, and rescues chemically meaningful relationships that conventional metrics systematically miss. Applying SpecReBoot to study the polyketide-lactones produced by the endophytic fungus Diaporthe caliensis revealed previously hidden spectral relationships leading to the discovery of the novel caliensomycin macrolactone scaffold, biosynthetically and biochemically related to the bioactive phomol known polyketide present in D. caliensis. In conclusion, this study provides the first statistical framework for quantifying uncertainty in MS/MS mass spectral similarity. Due to its context-agnostic nature, we anticipate that our computational metabolomics framework can also be adopted across other disciplines like clinical and environmental metabolomics. Altogether, SpecReBoot introduces statistical rigor, improves reproducibility, thus enhancing molecular networking-based natural product discovery.