Metabolomics and Mass Spectrometry Studies

The paper uses longitudinal wastewater sampling to analyze metabolite temporal dynamics across buildings, revealing distinct feature temporal dynamics and enabling building classification.

Goslin simplifies lipid name handling by unifying lipid nomenclature dialects and providing implementations in multiple programming languages.

MS-CleanR improves metabolite annotation rates in untargeted LC-MS by reducing redundant signals while retaining most unique metabolites, enhancing analysis of plant metabolomes.

The paper identifies BMI-associated metabolites using untargeted metabolomics and causal inference, finding both known and unknown metabolites with causal links to obesity.

This study identifies early metabolic features associated with genetic liability to type 2 diabetes across childhood, adolescence, and young adulthood using longitudinal metabolomics data.

AutoTuner improves metabolomics data processing by rapidly and robustly selecting parameters, outperforming existing methods in speed and consistency.

The paper describes the fecal metabolome in cryogenically collected samples from healthy participants, identifying metabolites and their variation across different areas of the stool.

MESSAR recommends metabolite substructures from tandem mass spectra without requiring predefined metabolite candidates.

This paper introduces ncGTW, a method to align LC-MS profiles by detecting and correcting misalignments using compound-specific warping functions and neighbor constraints.

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

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

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.

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

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

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

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

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

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

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

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

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.

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.

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

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.

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

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.

The paper improves metabolite identification in LC-MS datasets by incorporating in-source fragment information from MS/MS spectral libraries.

The paper evaluates missing data handling strategies in untargeted metabolomics data, finding that KNN imputation with variable pre-selection is robust across multiple evaluation schemes.

The paper identifies and addresses high peak density artifacts in Fourier transform mass spectra, which can mislead data analysis by creating false peaks.