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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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Highly sensitive feature detection for high resolution LC/MS.

Ralf Tautenhahn1, Christoph Böttcher, Steffen Neumann

  • 1Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Halle, Germany. rtautenh@ipb-halle.de

BMC Bioinformatics
|December 2, 2008
PubMed
Summary
This summary is machine-generated.

A new feature detection algorithm, centWave, enhances metabolomics by accurately identifying compounds in complex LC/MS data. It offers superior recall and precision for reliable analysis of plant extracts.

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

Published on: May 20, 2013

Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Computational Biology

Background:

  • Liquid chromatography coupled to mass spectrometry (LC/MS) is crucial for metabolomics.
  • Accurate feature detection is essential for analyzing complex samples like plant extracts with numerous compounds.

Purpose of the Study:

  • To develop and evaluate a novel feature detection algorithm for high-resolution LC/MS data.
  • To improve the accuracy and reliability of compound identification in metabolomics.

Main Methods:

  • Developed the centWave algorithm utilizing continuous wavelet transformation.
  • Applied and evaluated the algorithm on dilution series and mixtures of plant extracts.
  • Compared centWave performance against existing algorithms like matchedFilter and centroidPicker.

Main Results:

  • The centWave algorithm demonstrated high performance in detecting features in complex LC/MS datasets.
  • It successfully identified close-by and partially overlapping features.
  • Achieved superior recall and precision values compared to established methods.

Conclusions:

  • The centWave algorithm meets the demands of modern metabolomics experiments.
  • It provides a reliable and accurate method for feature detection in LC/MS data.
  • The algorithm is integrated into the Bioconductor R-package XCMS for accessibility.