Metabolomics and Mass Spectrometry Studies

A chemical structure and machine learning approach to assess the potential bioactivity of endogenous metabolites and their association with early-childhood hs-CRP levels

The paper uses metabolomics and machine learning to identify metabolites associated with early childhood hs-CRP levels, finding 52 metabolites with potential bioactivity linked to systemic inflammation.

Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics

The paper evaluates peak quality metrics in metabolomics to improve automated LC-MS workflows by reducing false positives and enhancing statistical power.

Bayesian optimization of separation gradients to maximize the performance of untargeted LC-MS

This paper introduces BAGO, a Bayesian optimization method for optimizing LC gradients in untargeted LC-MS, improving compound annotation and spectral purity.

In search of disentanglement in tandem mass spectrometry datasets

This study uses variational autoencoders to disentangle factors in tandem mass spectrometry data, enabling better interpretation and comparison across instruments.

Bayesian statistics improves biological interpretability of metabolomics data from human cohorts

The paper applies Bayesian statistics to metabolomics data from human ME/CFS cohorts, revealing more consistent and biologically meaningful metabolic differences compared to frequentist methods.

Brain map of aging-induced alterations in membrane ganglioside pattern

The paper analyzes ganglioside profiles in rat brains and fluids across age and regions, finding age-related shifts in a-series to b-series ratios and region-specific changes.

Annotating metabolite mass spectra with domain-inspired chemical formula transformers

This paper introduces MIST, a deep learning framework for metabolite structure annotation from mass spectra, outperforming existing methods and identifying new metabolites in a disease cohort.

LORA, Lipid Over-Representation Analysis based on structural information

LORA analyzes lipidomic data to identify overrepresented lipids using standardized nomenclature and hierarchical structure, providing insights into molecular composition.

Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites

This study aligns metabolomics LC-MS datasets from malaria, leishmaniasis, and Zika virus to identify fever-associated metabolites, finding the kynurenine pathway as most affected.

Spatial Metabolome Lipidome and Glycome from a Single brain Section

The paper presents a method for simultaneous spatial analysis of metabolome, lipidome, and glycome from a single tissue section using mass spectrometry imaging and a computational framework called Sami.

Non-targeted multimodal metabolomics data from ovine rumen fluid fractions

The paper presents a non-targeted metabolomics dataset of ovine rumen fluid fractions using HILIC and C18 chromatography coupled with high-resolution mass spectrometry.

Metabolomics investigation of post-mortem human pericardial fluid

This study applies metabolomics to post-mortem human pericardial fluid to estimate time since death using multivariate regression models.

OzFAD: Ozone-enabled fatty acid discovery reveals unexpected diversity in the human lipidome

OzFAD uses ozonolysis with LC-MS to discover new unsaturated fatty acids in human samples, doubling identified plasma fatty acids and revealing non-canonical double bond positions.

Entropy-based decoy generation methods for accurate FDR estimation in large-scale metabolomics annotations.

This paper introduces entropy-based decoy generation methods to improve FDR estimation in metabolomics and presents a tool for accurate FDR-controlled analysis.

TopNEXt: Automatic DDA Exclusion Framework for Multi-Sample Mass Spectrometry Experiments

TopNEXt improves data acquisition in multi-sample LC-MS/MS metabolomics by extending DDA exclusion methods across samples using a Region of Interest and intensity-based scoring system.

IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets

IDSL.CSA generates composite mass spectra libraries from MS1-only data to annotate untargeted metabolomics datasets without MS2 fragmentation spectra, achieving comparable results to MS/MS libraries in human blood samples.

Metabolomic and genomic prediction of common diseases in 477,706 participants in three national biobanks

The paper develops metabolomic risk scores from half a million blood samples to predict 12 common diseases, showing they outperform polygenic scores and benefit from repeat measurements.

Offline Two-dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome

This paper introduces an offline two-dimensional liquid chromatography-mass spectrometry approach to improve metabolome annotation in fecal samples, identifying more compounds than traditional methods.

Diazo-carboxyl click chemistry enables rapid and sensitive quantification of carboxylic acid metabolites

The paper introduces DQmB-HA, a method for sensitive quantification of carboxylic acid metabolites, enabling single-cell analysis and blood sample profiling in mice and humans.

Evaluating Retention Index Score Assumptions to Refine GC-MS Metabolite Identification

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

Variance Analysis of LC-MS Experimental Factors and Their Impact on Machine Learning

The paper analyzes variance sources in LC-MS data and evaluates transfer learning's impact on ML performance across public datasets.

MetPlast: an R package to evaluate Metabolic Plasticity using Information Theory statistical framework

MetPlast quantifies metabolic plasticity using Shannon entropy-based metrics and provides visualization tools, demonstrated on tomato domestication data.

Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics

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

Wide scope analysis of bioactive lipids, including steroids, bile acids, and polyunsaturated fatty acid metabolites, in human plasma by LC/MS/MS

The paper develops an LC/MS/MS method to measure over 140 bioactive lipids in human plasma, revealing sex and circadian differences.

Generalized tree structure to annotate untargeted metabolomics and stable isotope tracing 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.

Comparison of classification accuracy and feature selection between sparse and non-sparse modeling of metabolomics data

The paper compares classification accuracy and feature selection between sparse and non-sparse modeling of metabolomics data using serum lipidomic data from breast cancer patients.

Functional metabolomics of the human scalp: A metabolic niche for Staphylococcus epidermidis

The paper develops a functional metabolomics strategy to annotate metabolites in the human scalp and identifies the metabolic niche of Staphylococcus epidermidis.

MAW - The Reproducible Metabolome Annotation Workflow for Untargeted Tandem Mass Spectrometry

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

NMRQNet: a deep learning approach for automatic identification and quantification of metabolites using Nuclear Magnetic Resonance (NMR) in human plasma samples

NMRQNet automatically identifies and quantifies metabolites in human plasma NMR spectra, outperforming existing methods and identifying potential biomarkers for neurological disorders.

Improving annotation propagation on molecular networks through random walks: Introducing ChemWalker

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