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Related Concept Videos

High-Resolution Mass Spectrometry (HRMS)01:15

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The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
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compMS2Miner: An Automatable Metabolite Identification, Visualization, and Data-Sharing R Package for High-Resolution

William M B Edmands1, Lauren Petrick1, Dinesh K Barupal2

  • 1Rappaport Lab, UC Berkeley, School of Public Health , GL81 Koshland Hall, Berkeley, California 94720, United States.

Analytical Chemistry
|February 23, 2017
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Summary

The compMS2Miner R package automates untargeted metabolomic analysis, enabling rapid and comprehensive metabolite annotation from mass spectral features. This tool enhances data interpretation and sharing for complex biological samples.

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Analytical Chemistry

Background:

  • Untargeted metabolomic profiling using UHPLC-HRMS presents challenges in annotating unknown mass spectral features.
  • Manual interpretation of MS2 spectra is time-consuming and not scalable for large datasets.

Purpose of the Study:

  • To develop an automated workflow for rapid and comprehensive metabolite annotation in untargeted metabolomics.
  • To integrate various tools for MS2 spectral analysis and data visualization, facilitating data sharing and transparency.

Main Methods:

  • Developed the compMS2Miner R package and compMS2Explorer GUI for automated feature annotation.
  • Implemented steps including MS1 feature matching, noise filtration, composite spectra generation, in-silico fragmentation, and database searching.
  • Utilized machine learning for retention time prediction and text-mining for false positive removal.

Main Results:

  • The compMS2Miner workflow successfully provided rapid annotation of diverse endogenous and microbial metabolites in mouse serum samples.
  • Composite spectra were matched to the Massbank of North America (MoNA) database, identifying 29 compound classes.
  • An average of seven scoring metrics demonstrated effective ranking of MoNA-matched spectra, with minor structural differences impacting annotation accuracy.

Conclusions:

  • CompMS2Miner offers an automated and objective solution for metabolite annotation in untargeted metabolomics.
  • The compMS2Explorer facilitates data exploration, curation, and sharing, enhancing transparency in metabolomic studies.
  • The workflow effectively identifies diet and antibiotic-influenced metabolites, aligning with existing research findings.