de Jonge, N. F. | bioRxiv Science

bioRxiv2022

FERMO: a Dashboard for Streamlined Rationalized Prioritization of Molecular Features from Mass Spectrometry Data

Small molecules shape phenotypic variation by modulating biological processes, yet linking specific molecules to observed traits remains challenging due to the complexity of biological samples. Liquid chromatography-tandem mass spectrometry routinely detects hundreds of molecules per sample, and computational tools aid in the selection of the biologically relevant subset by organizing, annotating, and integrating orthogonal data. Existing tools typically focus on facilitating data-driven exploration to support manual interpretation, rather than more objective, data-driven prioritization and hypothesis-generation. Here, we introduce FERMO, a free online dashboard interface for prioritization of molecular features and samples associated with phenotypes of interest. FERMOs modular framework automates data processing, annotation, and integration of standardized phenotypic and other metadata. FERMO supports both exploratory and targeted analysis through efficient interactive visualization, reproducible prioritization, and data filtering. We demonstrate FERMOs utility in benchmarking studies prioritizing bioactive compounds from complex biological matrices. FERMO is freely available at https://fermo.bioinformatics.nl/.

bioinformatics↗

bioRxiv2022

MS2Query: Reliable and Scalable MS2 Mass Spectral-based Analogue Search

Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches, but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrates an improved reliability and scalability. Thereby, MS2Query offers exciting opportunities for further increasing the annotation rate of complex metabolite mixtures and for discovering new biology.

bioinformatics↗