Metabolomics and Mass Spectrometry Studies

A new workflow combining R packages for statistical analysis of metabolites

The paper presents a new workflow using R packages for analyzing metabolite-trait associations, demonstrated on public data and made reproducible.

genuMet: distinguish genuine untargeted metabolic features without quality control samples

genuMet identifies and reduces false metabolic features in untargeted metabolomics without pooled QC samples, achieving high true negative and true positive rates.

Taxonomically informed scoring enhances confidence in natural products annotation

The paper enhances metabolite annotation confidence by integrating taxonomic information into spectral matching scores, improving recall and precision of annotation tools.

Elevated alpha-1 antitrypsin is a major component of GlycA-associated risk for future morbidity and mortality

The paper identifies alpha-1 antitrypsin as a key contributor to GlycA's predictive power for disease risk and mortality across multiple conditions.

Identification of metabolites from tandem mass spectra with a machine learning approach utilizing structural features

The paper introduces SF-Matching, a machine learning method for metabolite identification from MS/MS spectra using structural features, achieving high accuracy when combined with CSI:FingerID.

In-silico Optimisation of Mass Spectrometry Fragmentation Strategies in Metabolomics

The paper introduces ViMMS, a simulator for LC-MS/MS fragmentation strategies, enabling in-silico testing and optimization of mass spectrometry acquisition methods.

WiPP: Workflow for improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) data

WiPP improves GC-MS peak detection by combining multi-algorithm results with machine learning-based quality assessment, enhancing accuracy and enabling algorithm comparison.

Comparison of a GC-Orbitrap-MS with Parallel GC-FID Capabilities for Metabolomics of Human Serum

The paper compares a GC-Orbitrap-MS with FID capabilities to traditional GC-MS for metabolomics of human serum, showing improved quantification and pathway coverage.

Generation and quality control of maternal plasma lipidomics data associated with preterm birth

The paper generates and validates an untargeted lipidomics dataset from maternal plasma samples associated with preterm birth, providing a resource for further research.

Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate metabolites

The paper evaluates three NMR analysis tools for metabolite identification in human urine, finding ISA and ACP robustly identify metabolites while PCA fails.

Meta-analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics

The paper analyzes targeted metabolomics data from diverse biological samples to assess measurement quality and identify metabolite dynamics across conditions.

Mass spectra alignment using virtual lock-masses

This work proposes algorithms to correct variations between mass spectra, improving comparability across large datasets and enabling machine learning applications.

A comprehensive plasma metabolomics dataset for a cohort of mouse knockouts within the international mouse phenotyping consortium

The paper provides a metabolomics dataset from 30 mouse gene knockouts and wildtypes, identifying 832 metabolites across multiple analytical methods.

Continuous in vivo metabolism by NMR

This paper introduces CIVM-NMR, a method for continuous in vivo metabolomics monitoring, and demonstrates its application in human cells and Neurospora crassa.

ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra

ASICS provides an R package for a complete workflow analyzing 1D 1H NMR spectra, improving metabolite quantification and identifying relevant metabolites in metabolomics studies.

Metabolic Reaction Network-based Recursive Metabolite Identification for Untargeted Metabolomics

The paper introduces a recursive algorithm and webserver, MetDNA, for large-scale and unambiguous metabolite identification in untargeted metabolomics.

MASST: A Web-based Basic Mass Spectrometry Search Tool for Molecules to Search Public Data.

MASST enables querying of public small-molecule tandem MS data to discover molecular relationships in metabolomics and natural product datasets.

Using expert driven machine learning to enhance dynamic metabolomics data analysis.

This paper introduces a tool that uses expert knowledge in machine learning to analyze dynamic metabolomics data, showing it performs as well as statistical methods.

Deriving Accurate Lipid Classification based on Molecular Formula

The paper develops a machine learning method to classify molecular formula assignments into lipid categories using SMIRFE data, achieving high accuracy and precision.

Longitudinal plasma metabolomics of aging and sex

This study identifies age and sex-associated metabolite trajectories in longitudinal plasma samples from the WRAP cohort, revealing sex-specific changes in metabolites like sphingolipids and heritability patterns.

Metabolic Reprogramming: Short-term Western Diet Exposure Induces Sustained Changes in Plasma Metabolites

A short-term Western diet exposure in baboons caused long-term metabolic changes, suggesting a reset of the metabolic program.

METASPACE: A community-populated knowledge base of spatial metabolomes in health and disease

An open-source cloud engine annotates imaging mass spectrometry data at controlled false discovery rate; publicly shared results reached over 3200 datasets and 6 million metabolite images by January 2019.

Implementations of the chemical structural and compositional similarity metric in R and Python

The paper provides efficient implementations of the Chemical Structural and Compositional Similarity metric in R and Python, significantly improving speed and usability for metabolomics analysis.

Comprehensive and empirical evaluation of machine learning algorithms for LC retention time prediction

This study evaluates seven machine learning algorithms for predicting LC retention times across 36 metabolomics datasets, finding no single algorithm is optimal for all cases and highlighting the benefits of combining models.

PAIRUP-MS: Pathway Analysis and Imputation to Relate Unknowns in Profiles from Mass Spectrometry-based metabolite data

PAIRUP-MS imputes and annotates unknown metabolite signals in mass spectrometry data, improving pathway analysis and meta-analysis power for metabolomics studies.

Integrated use of LC/MS/MS and LC/Q-TOF/MS targeted metabolomics with automated label-free microscopy for quantification of purine metabolites in cultured mammalian cells

This study develops a targeted metabolomics approach using LC/MS/MS and LC/Q-TOF/MS to quantify purine metabolites in cultured cells, integrating automated label-free microscopy for normalization.

Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra

This paper introduces methods to improve metabolite annotation using substructure discovery and machine learning, along with a database for shared motifs.

Maximizing the quality of NMR automatic metabolite profiling by a machine learning based prediction of signal parameters

This study improves NMR automatic metabolite profiling by using machine learning to predict signal parameters, reducing uncertainty and enhancing profiling quality.

netome: a computational framework for metabolite profiling and omics network analysis

netome provides tools for analyzing LC-MS metabolomics data, including quality control, peak alignment, and network-based statistical testing.

Profound perturbation of the human metabolome by obesity

This study identifies metabolic and genetic signatures of obesity using metabolome analysis and whole genome sequencing in twin and volunteer cohorts, revealing clinically meaningful heterogeneity.