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Related Concept Videos

Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

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Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass. One common type of ionization, known as electron ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave behind a...
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Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
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Mass Spectrometry of Amines01:15

Mass Spectrometry of Amines

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In mass spectroscopy, amines undergo fragmentation to give parent ions with odd molecule weights. This observed mass spectrum follows the nitrogen rule; a molecule with an odd number of nitrogen atoms produces a molecular ion with an odd molecular weight. Amines undergo fragmentation through α cleavage, producing nitrogen-containing cations—iminium ions—and alkyl radicals. Mass spectra of aromatic and cyclic aliphatic amines exhibit strong molecular ion peaks, but acyclic...
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Mass Spectrometry: Isotope Effect01:13

Mass Spectrometry: Isotope Effect

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Most elements exist in nature as a mixture of isotopes. The isotopes differ in weight due to their respective number of neutrons. The molecular weight of a molecule is different depending on the specific isotope of its elements involved. As a result, the mass spectrum of the molecule exhibits peaks from the same fragment at multiple positions. The positions of these mass signals depend on the mass differences between isotopes. Furthermore, the intensity of these signals is dependent on the...
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Chemical Ionization (CI) Mass Spectrometry01:21

Chemical Ionization (CI) Mass Spectrometry

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The molecular ion peak of a molecule in the mass spectrum provides vital information for molecular identification. However, conventional electron impact ionization can lead to the rapid dissociation of some molecular ions before they reach the detector. A milder ionization method is required to increase the lifetime of such ionized analyte molecules. Chemical ionization (CI) is a gas-phase protonation reaction useful for mass-analyzing analyte molecules that are easily protonated to yield the...
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Mass Spectrometry: Alkene Fragmentation00:59

Mass Spectrometry: Alkene Fragmentation

3.6K
Alkenes lose one electron from the unsaturated π bond upon ionization and form stable molecular ions. Further fragmentation of alkenes occurs through three different reaction pathways. The most prominent fragmentation is the cleavage at the allylic position. The resultant allylic carbocation is resonance stabilized. In the mass spectra of terminal alkenes, this fragment appears at a mass-to-charge ratio of 41. In the internal alkenes, where there are two choices of allylic cleavage, the...
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Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry
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A biplot correlation range for group-wise metabolite selection in mass spectrometry.

Youngja H Park1, Taewoon Kong2, James R Roede3

  • 11College of Pharmacy, Korea University, Sejong, 30019 South Korea.

Biodata Mining
|February 12, 2019
PubMed
Summary

A new method called biplot correlation range (BCR) identifies key metabolic markers for personalized medicine. BCR improves upon existing methods for analyzing complex biological data, aiding disease diagnosis and risk assessment.

Keywords:
Biplot correlationFeature selectionMetabolomics

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Area of Science:

  • Metabolomics
  • Systems Biology
  • Personalized Medicine

Background:

  • Current disease diagnosis relies on individual biomarkers, which are limited in complex biological systems.
  • Existing data reduction tools lack statistical basis for selecting group-wise metabolic contributors.
  • Functional redundancies and multi-layered regulation in biology necessitate advanced analytical approaches.

Purpose of the Study:

  • To introduce a novel dimensionality-reduction approach, biplot correlation range (BCR), for identifying metabolic phenotypes.
  • To provide a statistical basis for selecting group-wise metabolic markers contributing to observed phenotypes.
  • To enhance disease characterization and risk assessment in personalized medicine.

Main Methods:

  • Developed a dimensionality-reduction approach termed 'biplot correlation range (BCR)'.
  • Integrated biplot correlation analysis with direct orthogonal signal correction and partial least squares (PLS).
  • Applied BCR to simulated and real-life LC-MS metabolomics data from mouse liver mitochondria.

Main Results:

  • BCR demonstrated feasibility and superiority over existing methods (false discovery rate, correlation) in simulated data.
  • Identified discriminatory metabolites contributing to class separation in wild-type and transgenic mouse liver mitochondria.
  • BCR effectively selects key metabolic markers that statistical methods like false discovery rate or statistical total correlation spectroscopy struggle to find.

Conclusions:

  • The biplot correlation range (BCR) method offers a robust statistical framework for identifying critical metabolic markers.
  • BCR enhances the analysis of complex metabolomics data for improved disease diagnosis and personalized medicine.
  • This approach facilitates a deeper understanding of metabolic phenotypes and their contribution to health and disease.