Nuno Bandeira

The paper identifies alkamines as a previously hidden class of metabolites formed via reductive amination, revealing significant gaps in metabolomics and drug metabolism understanding.

The paper benchmarks peptide spectral library search methods using a new dataset, finding poor recall (~70%) and varying performance across scoring functions and preprocessing scenarios.

The paper uses synthetic multiplexing to annotate uncharacterized metabolites, identifying ibuprofen-carnitine as a metabolite linked to muscle toxicity.

The paper curates LC-MS/MS data from plant extracts to create a taxonomically focused database for exploring plant-derived molecules.

NPOmix connects mass spectrometry fragmentation data to biosynthetic gene clusters using a k-nearest neighbor classifier, improving metabolite discovery and structure prediction.

The paper introduces a standardized metadata format for proteomics datasets to improve reusability and integration across public repositories.

The paper introduces the Universal Spectrum Identifier (USI) to standardize referencing mass spectra in proteomics repositories, enabling transparent traceability of spectral evidence in publications.

The paper introduces a web-based tool for collaborative analysis of mass spectrometry data, enabling remote inspection, visualization, and analysis of private and public datasets.

The paper introduces FBMN, a method for improving molecular networking by enhancing quantitative analysis and isomer distinction using feature detection and alignment tools in the GNPS platform.

The paper develops ReDU, a tool for discovering and re-analyzing metabolomics data in GNPS, following FAIR principles.

The paper updates the HPP MS Data Interpretation Guidelines to improve accuracy and address new workflows and data standards.

MASST enables querying of public small-molecule tandem MS data to discover molecular relationships in metabolomics and natural product datasets.

This paper uses untargeted mass spectrometry to analyze chemical changes in foods during preparation and storage, finding that some processes alter molecular composition while others primarily involve extraction.

The paper develops methods to estimate false discovery rates in untargeted metabolomics annotations and shows how adjusting spectral matching parameters can significantly increase the number of annotations.