Yasin El Abiead

The paper introduces mzQC, a standard for representing quality control metrics in mass spectrometry, enabling interoperable and machine-readable QC reporting.

The Everything Bagel feature finder automates metabolomics data processing, achieving comparable or better performance than existing methods with significantly reduced computational time.

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 paper introduces MT-GEM and STEP to predict discrete structural transformations in metabolites from MS/MS spectra, achieving significant improvements over existing methods.

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 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.

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

This study examines how maternal ampicillin exposure during pregnancy and postpartum affects fecal microbiome and metabolic profiles, linking these changes to increased offspring weight gain.

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 creates a spectral library of N-acyl lipids from metabolomics data, identifies novel compounds, and links them to health conditions like HIV and cognitive impairment.

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.

ModiFinder localizes structural modification sites in small molecules by aligning MS/MS spectra of known and unknown compounds.

The paper introduces homologueDiscoverer, an R package for detecting and managing homologue series in LC-MS data through interactive plots and local databases.

The paper identifies and annotates heterogeneous multimeric metabolite ions in LC-MS data using labeled and unlabeled Pichia pastoris extracts and demonstrates their presence in public and additional datasets.

The paper benchmarks quality assurance strategies for non-targeted metabolomics data pre-processing using publicly available datasets.