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Volume 26, Issue 3, Pages 151-154

Meta-analysis of global metabolomics and proteomics data to link alterations with phenotype

Gary J. Patti,1 Ralf Tautenhahn,2 Bryan R. Fonslow,3 Yonghoon Cho,2 Adam Deutschbauer,4 Adam Arkin,4 Trent Northen,5 and Gary Siuzdak2

1Departments of Chemistry, Genetics and Medicine, Washington University School of Medicine, St. Louis, MO, USA
2Center for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, CA, USA
3Department of Chemical Physiology, The Scripps Research Institute, La Jolla, CA, USA
4Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
5Department of Bioengergy/GTL & Structural Biology, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Copyright © 2011 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Global metabolomics has emerged as a powerful tool to interrogate cellular biochemistry at the systems level by tracking alterations in the levels of small molecules. One approach to define cellular dynamics with respect to this dysregulation of small molecules has been to consider metabolic flux as a function of time. While flux measurements have proven effective for model organisms, acquiring multiple time points at appropriate temporal intervals for many sample types (e.g., clinical specimens) is challenging. As an alternative, meta-analysis provides another strategy for delineating metabolic cause and effect perturbations. That is, the combination of untargeted metabolomic data from multiple pairwise comparisons enables the association of specific changes in small molecules with unique phenotypic alterations. We recently developed metabolomic software called metaXCMS to automate these types of higher order comparisons. Here we discuss the potential of metaXCMS for analyzing proteomic datasets and highlight the biological value of combining meta-results from both metabolomic and proteomic analyses. The combined meta-analysis has the potential to facilitate efforts in functional genomics and the identification of metabolic disruptions related to disease pathogenesis.