MET-XAlign: A Metabolite Cross-Alignment Tool for LC/MS-Based Comparative Metabolomics

  • Zhang W
  • Lei Z
  • Huhman D
 et al. 
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Abstract

Liquid chromatography-mass spectrometry (LC/MS) metabolite profiling has been widely used in comparative metabolomics studies; however, LC/MS-based comparative metabolomics currently faces several critical challenges. One of the greatest challenges is how to effectively align metabolites across different LC/MS profiles; a single metabolite can give rise to multiple peak features, and the grouped peak features that can be used to construct a spectrum pattern of single metabolite can vary greatly between biochemical experiments and even between instrument runs. Another major challenge is that the observed retention time for a single metabolite can also be significantly affected by experimental conditions. To overcome these two key challenges, we present a novel metabolite-based alignment approach entitled MET-XAlign to align metabolites across LC/MS metabolomics profiles. MET-XAlign takes the deduced molecular mass and estimated compound retention time information that can be extracted by our previously published tool, MET-COFEA, and aligns metabolites based on this information. We demonstrate that MET-XAlign is able to cross-align metabolite compounds, either known or unknown, in LC/MS profiles not only across different samples, but also across different biological experiments, and different electrospray ionization modes. Therefore, our proposed metabolite-based cross-alignment approach is a great step forward and its implementation, MET-XAlign, is a very useful tool in LC/MS-based comparative metabolomics. MET-XAlign has been successfully implemented with core algorithm coding in C++, making it very efficient, and visualization interface coding in the Microsoft .NET Framework. The MET-XAlign software along with demonstrative data is freely available at http://bioinfo.noble.org/manuscript-support/met-xalign/.

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Authors

  • Wenchao Zhang

  • Zhentian Lei

  • David Huhman

  • Lloyd W. Sumner

  • Patrick X. Zhao

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