Journal Article

Characterization of <sup>1</sup>H NMR spectroscopic data and the generation of synthetic validation sets

Paul E. Anderson, Michael L. Raymer, Benjamin J. Kelly, Nicholas V. Reo, Nicholas J. DelRaso and T. E. Doom

in Bioinformatics

Volume 25, issue 22, pages 2992-3000
Published in print November 2009 | ISSN: 1367-4803
Published online September 2009 | e-ISSN: 1460-2059 | DOI:
Characterization of 1H NMR spectroscopic data and the generation of synthetic validation sets

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Motivation: Common contemporary practice within the nuclear magnetic resonance (NMR) metabolomics community is to evaluate and validate novel algorithms on empirical data or simplified simulated data. Empirical data captures the complex characteristics of experimental data, but the optimal or most correct analysis is unknown a priori; therefore, researchers are forced to rely on indirect performance metrics, which are of limited value. In order to achieve fair and complete analysis of competing techniques more exacting metrics are required. Thus, metabolomics researchers often evaluate their algorithms on simplified simulated data with a known answer. Unfortunately, the conclusions obtained on simulated data are only of value if the data sets are complex enough for results to generalize to true experimental data. Ideally, synthetic data should be indistinguishable from empirical data, yet retain a known best analysis.

Results: We have developed a technique for creating realistic synthetic metabolomics validation sets based on NMR spectroscopic data. The validation sets are developed by characterizing the salient distributions in sets of empirical spectroscopic data. Using this technique, several validation sets are constructed with a variety of characteristics present in ‘real’ data. A case study is then presented to compare the relative accuracy of several alignment algorithms using the increased precision afforded by these synthetic data sets.

Availability: These data sets are available for download at


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  7547 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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