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metaXCMS: second-order analysis of untargeted metabolomics data.

Mass spectrometry-based untargeted metabolomics often results in the observation of hundreds to thousands of features that are differentially regulated between sample classes. A major challenge in interpreting the data is distingtushing metabolites that are causally associated with the phenotype of... Full description

Journal Title: Analytical chemistry February 1, 2011, Vol.83(3), pp.696-700
Main Author: Tautenhahn, Ralf
Other Authors: Patti, Gary J , Kalisiak, Ewa , Miyamoto, Takashi , Schmidt, Manuela , Lo, Fang Yin , Mcbee, Joshua , Baliga, Nitin S , Siuzdak, Gary
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1520-6882 ; DOI: 10.1021/ac102980g
Link: http://search.proquest.com/docview/857291752/?pq-origsite=primo
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title: metaXCMS: second-order analysis of untargeted metabolomics data.
format: Article
creator:
  • Tautenhahn, Ralf
  • Patti, Gary J
  • Kalisiak, Ewa
  • Miyamoto, Takashi
  • Schmidt, Manuela
  • Lo, Fang Yin
  • Mcbee, Joshua
  • Baliga, Nitin S
  • Siuzdak, Gary
subjects:
  • Animals–Methods
  • Metabolomics–Methods
  • Mice–Methods
  • Software–Methods
ispartof: Analytical chemistry, February 1, 2011, Vol.83(3), pp.696-700
description: Mass spectrometry-based untargeted metabolomics often results in the observation of hundreds to thousands of features that are differentially regulated between sample classes. A major challenge in interpreting the data is distingtushing metabolites that are causally associated with the phenotype of interest from those that are unrelated but altered in downstream pathways as an effect. To facilitate this distinction, here we describe new software called metaXCMS for performing second-order ("meta") analysis of untargeted metabolomics data from multiple sample groups representing different models of the same phenotype. While the original version of XCMS was designed for the direct comparison of two sample groups, metaXCMS enables meta-analysis of an unlimited number of sample classes to facilitate prioritization of the data and increase the probability of identifying metabolites causally related to the phenotype of interest. metaXCMS is used to import XCMS results that are subsequently filtered, realigned, and ultimately compared to identify shared metabolites that are up- or down-regulated across all sample groups. We demonstrate the software's utility by identifying histamine as a metabolite that is commonly altered in three different models of pain. metaXCMS is freely available at http://metlin.scripps.edu/ metaxcms/. doi: 10.1021/ac102980g
language: eng
source:
identifier: E-ISSN: 1520-6882 ; DOI: 10.1021/ac102980g
fulltext: fulltext
issn:
  • 15206882
  • 1520-6882
url: Link


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titlemetaXCMS: second-order analysis of untargeted metabolomics data.
creatorTautenhahn, Ralf ; Patti, Gary J ; Kalisiak, Ewa ; Miyamoto, Takashi ; Schmidt, Manuela ; Lo, Fang Yin ; Mcbee, Joshua ; Baliga, Nitin S ; Siuzdak, Gary
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identifierE-ISSN: 1520-6882 ; DOI: 10.1021/ac102980g
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descriptionMass spectrometry-based untargeted metabolomics often results in the observation of hundreds to thousands of features that are differentially regulated between sample classes. A major challenge in interpreting the data is distingtushing metabolites that are causally associated with the phenotype of interest from those that are unrelated but altered in downstream pathways as an effect. To facilitate this distinction, here we describe new software called metaXCMS for performing second-order ("meta") analysis of untargeted metabolomics data from multiple sample groups representing different models of the same phenotype. While the original version of XCMS was designed for the direct comparison of two sample groups, metaXCMS enables meta-analysis of an unlimited number of sample classes to facilitate prioritization of the data and increase the probability of identifying metabolites causally related to the phenotype of interest. metaXCMS is used to import XCMS results that are subsequently filtered, realigned, and ultimately compared to identify shared metabolites that are up- or down-regulated across all sample groups. We demonstrate the software's utility by identifying histamine as a metabolite that is commonly altered in three different models of pain. metaXCMS is freely available at http://metlin.scripps.edu/ metaxcms/. doi: 10.1021/ac102980g
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