Metabolomics and Mass Spectrometry Studies

MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities

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.

Heritability of Urinary Amines, Organic Acids, and Steroid Hormones in Children

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

PeakBot: Machine learning based chromatographic peak picking

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

Systems analysis of metabolic responses to a mixed meal test in an obese cohort reveals links between tissue metabolism and the gut microbiota

The paper analyzes metabolic responses to a mixed meal test in an obese cohort, linking tissue metabolism with gut microbiota changes and identifying potential biomarkers for T2DM.

MEMO: Mass Spectrometry-based Sample Vectorization to Explore Chemodiverse Datasets

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

Increased fatty acid metabolism and decreased glycolysis are hallmarks of metabolic reprogramming in the brain during recovery from experimental stroke

metaPathwayMap: A tool to predict metabolic pathway neighborhoods from structural classes of untargeted metabolomics peaks

metaPathwayMap predicts metabolic pathway neighborhoods from untargeted metabolomics peaks using structural class predictions, enhancing pathway understanding in model and non-model species.

Deep Learning assisted Peak Curation for large scale LC-MS Metabolomics

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

Comparison of two sampling, quenching and extraction methods for quantitative yeasts metabolomics

This study compares two metabolite extraction methods for yeast, finding that cold-solvent extraction underestimates phosphorylated sugars and nucleotides, while methanol-quenching with boiling-ethanol-extraction provides more accurate quantification.

The usefulness of sparse k-means in metabolomics data: Anexample from breast cancer data

This study evaluates sparse k-means for metabolomics data, showing it improves clustering accuracy with fewer metabolites in breast cancer subtypes.

IDSL.CCDB: a database for exploring inter-chemical correlations in metabolomics and exposomics datasets

The paper introduces IDSL.CCDB, a database that catalogs inter-chemical correlations in metabolomics and exposomics datasets, enabling exploration of chemical relationships in human studies.

metaboprep: an R package for pre-analysis data description and processing.

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

Cognitive impairment and metabolite profile alterations in the hippocampus and cortex of male and female mice exposed to a fat and sugar-rich diet are normalized by diet reversal

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

Maternal plasma lipids are involved in the pathogenesis of preterm birth

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

Human blood lipoprotein predictions from 1H NMR spectra: protocol, model performances and cage of covariance

The paper develops partial least squares regression models to predict human blood lipoprotein subfractions from 1H NMR spectra, achieving high accuracy in independent cohorts.

BATL: Bayesian annotations for targeted lipidomics

BATL is a Bayesian classifier for targeted lipidomics that annotates LC-ESI-MS/MS peaks with high accuracy using eight features, achieving over 95% correct identification in SRM datasets.

From mass spectral features to molecules in molecular networks: a novel workflow for untargeted metabolomics.

The paper introduces MolNotator, a method that improves molecular networking in untargeted metabolomics by predicting neutral molecules from ion data, enhancing dereplication and network integration.

Boosting annotation in nutrimetabolomics by Feature-Based Molecular Networking: analytical and computational strategies applied to human urine samples from an untargeted LC-MS/MS based bilberry-blueberry intervention study.

This paper improves metabolite annotation in human urine using Feature-Based Molecular Networking and spectral libraries, identifying 65 berry-related metabolites and enhancing annotation coverage by 72%.

Development and Application of Multidimensional Lipid Libraries to Investigate Lipidomic Dysregulation Related to Smoke Inhalation Injury Severity

The paper develops a multidimensional lipid library using Skyline to annotate lipidomic data and applies it to identify lipid markers for smoke inhalation injury severity.

A novel paradigm for optimal mass feature peak picking in large scale LC-MS datasets using the isopair: isoLock, autoCredential and anovAlign

The paper introduces three computational tools to improve peak picking and validation in LC-MS datasets, showing better sensitivity and reproducibility than existing methods.

Single-cell metabolomics reveals the metabolic heterogeneity among microbial cells

This paper introduces RespectM, a method for single-cell metabolomics in microbes, achieving high accuracy in cell classification and revealing metabolic heterogeneity.

Metabolite discovery through global annotation of untargeted metabolomics data

The paper introduces NetID, a global network optimization method for annotating untargeted metabolomics data, identifying novel metabolites in yeast and mouse datasets.

MetaFetcheR: An R package for complete mapping of small compound data

MetaFetcheR maps small compound data by linking metabolite information across databases, resolving inconsistencies, and improving data fetching efficiency.

CPExtract, a Software for the Automated Tracer-Based Pathway Specific Screening of Secondary Metabolites in LC-HRMS Data

CPExtract automatically searches LC-HRMS data for user-defined isotopolog patterns from labeled tracers, enabling pathway-specific metabolite detection in fungi.

Assigning confidence to structural annotations from mass spectra with COSMIC

COSMIC assigns confidence to structural annotations from mass spectra by combining database generation, in silico annotation, and a confidence score, outperforming existing methods and annotating novel structures.

A new pipeline for the normalization and pooling of metabolomics data

The paper introduces a pipeline to normalize and pool metabolomics data across studies, improving comparability and reducing inter-study variability.

A Multi-omics Method Enabled by Sequential Metabolomics and Proteomics for Human Pluripotent Stem Cell-derived Cardiomyocytes

The paper develops a multi-omics method to analyze metabolome and proteome of hPSC-CMs, identifying overrepresented pathways in protein synthesis and cardiac function.

Benchmarking feature quality assurance strategies for non-targeted metabolomics

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

metID: A R package for automatable compound annotation for LC-MS-based data

metID automates compound annotation for LC-MS data by integrating multiple databases, improving efficiency and reproducibility.

hRUV: Hierarchical approach to removal of unwanted variation for large-scale metabolomics data

The paper introduces hRUV, a hierarchical method to remove unwanted variation in large-scale metabolomics data, showing improved preservation of biological signals.