Metabolomics and Mass Spectrometry Studies

This study evaluates and refines assumptions in retention index scores for GC-MS metabolite identification to improve accuracy and reduce manual verification.

This paper improves false discovery rate estimation in GC-MS metabolomics by extending Gaussian mixture models to incorporate similarity scores and experimental data.

This paper introduces a generalized tree structure and algorithm to annotate ions in metabolomics data and infer neutral mass, implemented as a Python package called khipu.

MAW automates metabolome annotation using LC-MS2 data by integrating spectral databases and tools, improving candidate ranking and reproducibility.

The paper introduces ChemWalker, a method that improves annotation propagation on molecular networks using random walks, enhancing scalability and efficiency compared to previous approaches.

The paper introduces a novel method that achieves 97.6% correct annotation of small molecule tandem mass spectra by combining spectral data with structural database information.

MetaboDirect is an open-source pipeline for processing FTICR-MS metabolomics data, offering automated analysis, visualization, and biochemical network generation.

This study identifies sex-specific metabolomic differences in C. elegans, revealing sex-dependent metabolites and their biological roles.

openNAU provides a platform for normalizing, analyzing, and visualizing untargeted metabolomics data, offering a repeatable and user-friendly workflow.

LC-MS2Struct improves small molecule structural annotation by combining retention order and MS2 data with a structured prediction model, achieving higher accuracy than existing methods.

The paper introduces an experimental design with anchor strains to reduce batch effects in metabolomics, enabling integration of NMR and LC-MS data across studies.

The paper identifies metabolites decreased under acute psychological stress in pigs, suggesting potential biomarkers for stress-related disorders.

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

IDSL.UFA annotates molecular formulas for untargeted LC/HRMS data, achieving high accuracy in metabolomics and exposomics studies.

The paper introduces a normalization method, MIX, that combines pharmacokinetic and statistical approaches to correct size effects in metabolomic time series data, improving biomarker detection.

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.

LipidMS 3.0 processes and annotates LC-MS/MS lipidomics data using DDA and DIA modes, offering a complete workflow and web-based interface.

The paper compares cosine, modified cosine, and neutral loss methods for MS/MS spectrum alignment to find structurally related molecules, finding modified cosine performs best.

The paper introduces deepPseudoMSI, a deep learning framework that converts LC-MS data into pseudo-MS images for improved disease diagnosis in precision medicine.

massDatabase provides an R package for streamlined compound annotation and pathway enrichment analysis by integrating public compound and pathway databases.

tidyMass provides a reproducible and shareable framework for LC-MS metabolomics data analysis.

The paper introduces a scan-centric method for characterizing peaks in mass spectrometry data, improving the accuracy of isotope abundance analysis.

MAFFIN improves metabolomics sample normalization by using maximal density fold change with high-quality features and signal correction, enhancing data separation and metabolic feature detection.

This study examines heritability of urinary metabolites in children, finding substantial genetic contributions across amines, organic acids, and steroids.

PeakBot is a machine learning tool for detecting chromatographic peaks in LC-HRMS data, achieving high accuracy in distinguishing peaks from background signals.

MEMO enables clustering of chemodiverse samples using LC-MS/MS profiles without feature alignment, achieving performance comparable to state-of-the-art methods faster.

NeatMS uses machine learning to automate peak curation in LC-MS metabolomics, improving robustness and scalability compared to manual methods.

metaboprep provides an R package for standardized pre-analysis processing and reporting of metabolomics data, enabling quality assessment and sample/metabolite selection.

This study shows that a high-fat and high-sucrose diet causes reversible metabolic and behavioral changes in mice, which normalize upon diet reversal.

This study identifies maternal plasma lipid metabolites associated with preterm birth using metabolomic analysis and machine learning in the Puerto Rico PROTECT cohort.