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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...
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...
MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

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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Updated: Jun 7, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Metabolomic analysis and visualization engine for LC-MS data.

Eugene Melamud1, Livia Vastag, Joshua D Rabinowitz

  • 1Department of Chemistry and Integrative Genomics, Carl Icahn Laboratory, Princeton, New Jersey 08544, USA.

Analytical Chemistry
|November 6, 2010
PubMed
Summary
This summary is machine-generated.

MAVEN is a new software package for analyzing liquid chromatography-high-resolution mass spectrometry (LC-MS) data. It offers efficient, interactive tools for metabolomic analysis, including visualization of isotope labeling and pathway mapping.

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

Published on: May 20, 2013

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Last Updated: Jun 7, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)
11:00

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

Published on: May 20, 2013

Area of Science:

  • Metabolomics
  • Computational Biology
  • Biochemistry

Background:

  • Metabolomic analysis using liquid chromatography-high-resolution mass spectrometry (LC-MS) generates complex datasets.
  • These datasets contain numerous features including metabolites, isotopes, and adducts, requiring sophisticated analysis tools.

Purpose of the Study:

  • To introduce Metabolomic Analysis and Visualization ENgine (MAVEN), a software package for efficient and interactive analysis of LC-MS data.
  • To provide tools for the entire data analysis workflow, from feature extraction to pathway visualization, including isotope labeling.

Main Methods:

  • Development of the MAVEN software package with interactive tools for LC-MS data analysis.
  • Implementation of a machine learning algorithm for automated peak quality assessment.
  • Features include extracted ion chromatograms, peak intensity bar graphs, click-based navigation, and pathway map overlays.

Main Results:

  • MAVEN enables efficient, interactive analysis of complex LC-MS metabolomic datasets.
  • The software facilitates visualization of isotope labeling and propagation through metabolic networks.
  • Automated peak quality assessment aids in data validation.

Conclusions:

  • MAVEN provides a comprehensive solution for LC-MS metabolomic data analysis and visualization.
  • The software's interactive and efficient design supports detailed exploration of metabolic pathways and labeled compounds.
  • MAVEN is released as open-source software, promoting accessibility in the scientific community.