Tandem Mass Spectral Databases and Their Use in Non-Targeted Metabolomics.

Comprehensive LC-MS Metabolomics Data Processing with notame R/Bioconductor Package.

Liquid chromatography-mass spectrometry (LC-MS) is widely used in metabolomics. Raw LC-MS data is relatively complex, consisting of molecular features originating not only from unique metabolites but also from redundant adducts, in-source fragments, artifacts, and impurities. It is also prone to signal intensity drift...

Computational Tools for LC-IMS-MS Data Processing in Metabolomics.

A Practical Guide to Dereplication in Natural Products Metabolomics

Liquid chromatography-tandem mass spectrometry (LC−MS/MS)-based metabolomics has become a central approach for characterizing the chemical diversity of biological systems, particularly in natural product discovery. However, the scale and complexity of untargeted LC−MS/MS datasets present a persistent challenge: only...

LC-MS Data Processing Using xcms.

Liquid chromatography-mass spectrometry (LC-MS) is a key technology in metabolomics, enabling high-throughput detection of small molecules across diverse biological samples. However, raw LC-MS data are complex, requiring careful preprocessing to ensure accurate and reproducible feature detection. This chapter introduce...

Large-Scale Metabolite Annotation in Untargeted Metabolomics Using MetDNA.

MSMICA: computational metabolite identification in untargeted metabolomics by integrating MS, retention time, and biological evidence

Mass Spectrometry Metabolomics Identification Connection Algorithm (MSMICA) is an algorithm for automated metabolite identification in untargeted liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analyses. Limitations in metabolite identification can occur due to the availability and cost of standards a...