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Mass Spectrometry: Complex Analysis

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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
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Gas chromatography–mass spectrometry (GC–MS) is the combination of analytical techniques of gas chromatography and mass spectrometry in a single instrument for analyzing a mixture of compounds. The gas chromatograph separates the compounds in the mixture, and the mass spectrometer analyzes each compound separately to determine the molecular masses and molecular structures.
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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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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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GridMass: a fast two-dimensional feature detection method for LC/MS.

Victor Treviño1, Irma-Luz Yañez-Garza, Carlos E Rodriguez-López

  • 1Cátedra de Bioinformática, Departamento de Investigación e Innovación, Escuela de Medicina, Tecnológico de Monterrey, Guadalupe, Nuevo Leon, 64849, Mexico.

Journal of Mass Spectrometry : JMS
|January 21, 2015
PubMed
Summary

GridMass is a new 2D feature detection algorithm for metabolomics data analysis. It improves speed and accuracy in mass spectrometry data processing, offering better results than existing methods.

Keywords:
HPLC/MSMZMine 2feature detectionmetabolomicssoftware & algorithms

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Area of Science:

  • * Computational Biology
  • * Analytical Chemistry
  • * Bioinformatics

Background:

  • * Feature detection is crucial for analyzing liquid chromatography-mass spectrometry (LC-MS) metabolomics data.
  • * Current algorithms in software like MZmine 2 often analyze data in one dimension, limiting accuracy.
  • * Accurate feature detection requires analyzing the two-dimensional mass/charge and elution time data simultaneously.

Purpose of the Study:

  • * To introduce GridMass, an efficient algorithm for two-dimensional (2D) feature detection in LC-MS data.
  • * To provide a faster and more sensitive/specific alternative to existing feature detection methods.
  • * To integrate GridMass into the MZmine 2 environment for broader accessibility.

Main Methods:

  • * GridMass employs a probe-landing strategy across the chromatographic space.
  • * Local maxima are identified by moving probes to accurately estimate feature boundaries.
  • * The algorithm processes data in a two-dimensional manner, considering both mass/charge and elution time.

Main Results:

  • * GridMass demonstrated comparable or superior sensitivity and specificity compared to other algorithms.
  • * The algorithm showed significant speed improvements in feature detection.
  • * Performance was validated across diverse sample types including plasma, plant fruits, and proteome samples.

Conclusions:

  • * GridMass offers an efficient and accurate solution for 2D feature detection in metabolomics.
  • * The algorithm enhances the analysis pipeline for LC-MS data.
  • * GridMass is available as a Java implementation within MZmine 2.