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Related Concept Videos

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Related Experiment Video

Updated: Jan 22, 2026

Method for Recording Broadband High Resolution Emission Spectra of Laboratory Lightning Arcs
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Alphabet Projection of Spectra.

Patrick A Kreitzberg1, Marshall Bern2, Qingbo Shu3

  • 1Department of Computer Science , University of Montana , Missoula , Montana 59801 , United States.

Journal of Proteome Research
|July 19, 2019
PubMed
Summary

This study introduces a new method to identify mass difference values for de novo analysis in metabolomics and glycomics. This approach enables the construction of molecular graphs and the discovery of recurring substructures from mass spectrometry data.

Keywords:
Gibbs sampleralgorithmde novo sequencingglycomicslocality sensitive hashingmass spectrometrymetabolomicsproteomicssmall moleculessubgraph isomorphism

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Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Biochemistry

Background:

  • De novo analysis of structured small molecules is crucial in metabolomics and glycomics due to the vast number of possible combinations.
  • Current de novo methods are often hindered by the unknown alphabet of mass differences required to link spectral peaks.
  • Mass spectrometry is a key technology for analyzing small molecules in biological systems.

Purpose of the Study:

  • To propose a method for identifying the alphabet of m/z differences from fragmentation mass spectra.
  • To enable de novo analysis when the mass difference alphabet is unknown.
  • To develop an efficient approach for finding recurring substructures within the de novo analysis results.

Main Methods:

  • A novel algorithm is presented to identify a set of m/z differences from fragmentation spectra.
  • This method constructs large, connected graphs by linking intense peaks based on the identified mass differences.
  • The study also introduces an efficient technique for substructure discovery in the resulting graphs.

Main Results:

  • The proposed method successfully identifies the alphabet of m/z differences from experimental data.
  • The approach facilitates the creation of extensive molecular graphs from mass spectrometry data.
  • An efficient algorithm for identifying recurring molecular substructures is demonstrated.

Conclusions:

  • The developed method overcomes limitations in de novo analysis by automatically determining the mass difference alphabet.
  • This facilitates more comprehensive structural elucidation of small molecules in metabolomics and glycomics.
  • The efficient substructure discovery component aids in identifying common molecular patterns.