Shipei Xing

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

This study examines how maternal antibiotic use during breastfeeding affects the infant gut microbiome and metabolome, finding altered microbial profiles and increased BMI in some infants.

The paper systematically extracts and catalogs acylcarnitine metabolites from multiple repositories, identifying previously unknown carnitines and providing a resource for further metabolic research.

The paper identifies microbial bile acid conjugation as a drug-activating function for 5-ASA, revealing its therapeutic relevance through metabolomics analysis.

This study maps conjugated metabolites by analyzing MS/MS spectra from public repositories, identifying potential conjugation events and providing a resource for structural discovery.

The paper introduces microbiomeMASST, a metadata network graph linking metabolites across datasets, enabling cross-study analysis of microbial metabolites and their biological context.

The paper introduces a collaborative platform for curating microbial metabolite knowledge to enhance metabolomics data interpretation.

ChemEmbed improves metabolite identification by combining enhanced MS/MS data with multidimensional molecular embeddings, achieving high accuracy in benchmark tests.

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

This paper introduces a mass spectrometry filtering tree to distinguish bile acid isomers in LC-MS/MS data and identifies a novel deoxycholyl-2-aminophenol amidate linked to whole grain consumption.

The paper introduces ms1-id, a tool for structural annotation of MS1 data in LC-MS and MS imaging, enabling level 2/3 annotation and uncovering previously overlooked metabolites.

The paper develops a drug exposure resource from untargeted metabolomics data to identify medications without clinical records.

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

The paper creates a suspect spectral library for untargeted metabolomics by propagating annotations from public MS/MS spectra to improve interpretation of unannotated spectra.

This paper introduces a bottom-up MS/MS approach for molecular formula discovery, improving annotation accuracy and identifying novel formulas in metabolomics datasets.