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Related Concept Videos

Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

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...
Mass Spectrometry: Complex Analysis01:21

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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.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
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Gas Chromatography–Mass Spectrometry (GC–MS)

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.
A gas chromatograph consists of a long, narrow capillary column with a polysiloxane coating on the inner wall. The coating...
Peptide Identification Using Tandem Mass Spectrometry01:33

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...

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MSeasy: unsupervised and untargeted GC-MS data processing.

Florence Nicolè1, Yann Guitton, Elodie A Courtois

  • 1Université de Lyon, F-69003, Lyon, France. florence.nicole@univ-st-etienne.fr

Bioinformatics (Oxford, England)
|July 12, 2012
PubMed
Summary

MSeasy offers unsupervised data mining for gas chromatography-mass spectrometry (GC-MS) data, identifying compounds in complex mixtures. This R package aids in metabolite profiling and molecule identification for researchers.

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Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Gas chromatography-mass spectrometry (GC-MS) is crucial for analyzing complex metabolic mixtures.
  • Unsupervised data mining techniques are needed to efficiently process large GC-MS datasets.
  • Existing tools may lack user-friendliness or specific functionalities for metabolite identification.

Purpose of the Study:

  • To introduce MSeasy, an R package for unsupervised data mining of GC-MS data.
  • To provide a user-friendly interface (MSeasyTkGUI) for R novices.
  • To facilitate the detection and quality control of putative compounds in metabolic samples.

Main Methods:

  • Unsupervised data mining using spectral clustering of mass spectra.
  • Utilizing retention times or retention indices for compound validation.
  • Generating a profiling matrix compatible with NIST and ARISTO for molecule identification.

Main Results:

  • MSeasy successfully detects putative compounds in complex GC-MS data.
  • The package supports common data formats like NetCDF, mzXML, and ASCII.
  • MSeasyTkGUI enhances accessibility for users with limited R programming experience.

Conclusions:

  • MSeasy provides an effective solution for unsupervised analysis of GC-MS data.
  • The tool aids in metabolite profiling and molecule identification.
  • MSeasy and MSeasyTkGUI are valuable resources for metabolomics research.