JUMPm

JUMPm performs large-scale metabolite identification from LC-MS/MS mass spectrometry data for untargeted metabolomics.

Key Features:

• Metabolite formula and structure identification: Identifies candidate metabolite formulas and proposes structures from mass spectrometry data.
• False discovery rate control: Employs a target-decoy strategy to evaluate false discovery rate during database searches.
• Decoy generation by octet rule violation: Generates decoys by intentionally violating the octet rule of chemistry to improve decoy realism for FDR estimation.
• Stable isotope labeling integration: Estimates elemental composition of formulas using stable isotope labeling information.
• LC retention time utilization: Incorporates liquid chromatography (LC) retention time during spectral library searches.
• Support for unlabeled and labeled samples: Demonstrated to analyze both unlabeled and stable-isotope labeled samples.
• Benchmarking against established tools: Performance compared with Compound Discoverer 3.1 and MZmine 2 for unlabeled and labeled untargeted metabolomics data.

Scientific Applications:

• Untargeted metabolite discovery: Enables large-scale identification of metabolites from complex LC-MS/MS datasets in untargeted metabolomics studies.
• Metabolic pathway and biomarker research: Supports analyses aimed at uncovering metabolic pathway perturbations and candidate biomarkers in biomedical research.
• Isotope labeling studies: Applied to datasets generated with stable isotope labeling to improve elemental composition assignment.

Methodology:

Identifies metabolite formulas and structures from mass spectra; applies a target-decoy strategy with decoys generated by octet rule violation for FDR estimation; estimates elemental composition using stable isotope labeling; uses LC retention time during spectral library searches.