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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
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AStream: an R package for annotating LC/MS metabolomic data.

Arnald Alonso1, Antonio Julià, Antoni Beltran

  • 1Grup de Recerca de Reumatologia, Institut de Recerca de l'Hospital Universitari Vall d'Hebrón (Univ. Autònoma de Barcelona), Barcelona, Spain.

Bioinformatics (Oxford, England)
|March 19, 2011
PubMed
Summary
This summary is machine-generated.

AStream is a new R software package that helps researchers accurately identify compounds in metabolomics data. It speeds up data analysis and improves the reliability of results from liquid chromatography-mass spectrometry (LC-MS).

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Published on: March 14, 2013

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)
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Area of Science:

  • Metabolomics
  • Computational Biology
  • Analytical Chemistry

Background:

  • Liquid chromatography-mass spectrometry (LC-MS) generates complex metabolomics data.
  • Curation and identification of feature peaks are critical steps in metabolomics analysis.
  • Existing methods can be time-consuming and inconsistent.

Purpose of the Study:

  • To introduce AStream, an R statistical software package.
  • To facilitate the curation and identification of feature peaks from LC-MS metabolomics data.
  • To improve the speed, reliability, and consistency of metabolomic data analysis.

Main Methods:

  • AStream detects isotopic, fragment, and adduct patterns.
  • It identifies feature pairs based on expected relational patterns.
  • The software performs data reduction for reliable compound identification.

Main Results:

  • AStream enables reliable compound identification by linking feature pairs to metabolite databases.
  • It significantly reduces data curation time for researchers.
  • The package increases the consistency of results in metabolomic studies.

Conclusions:

  • AStream is a fast and reliable tool for summarizing metabolomic data.
  • It enhances the ability to identify and curate feature peaks from LC-MS data.
  • The software supports efficient linking of identified compounds to metabolite databases.