Research Article

rNMR: open source software for identifying and quantifying metabolites in NMR spectra

  1. Ian A. Lewis,
  2. Seth C. Schommer,
  3. John L. Markley*

Article first published online: 9 OCT 2009

DOI: 10.1002/mrc.2526

Issue

Magnetic Resonance in Chemistry

Magnetic Resonance in Chemistry

Supplement: NMR-based mixture analysis – metabolomics and beyond

Volume 47, Issue S1, pages S123–S126, December 2009

Additional Information

How to Cite

Lewis, I. A., Schommer, S. C. and Markley, J. L. (2009), rNMR: open source software for identifying and quantifying metabolites in NMR spectra. Magn. Reson. Chem., 47: S123–S126. doi: 10.1002/mrc.2526

Author Information

  1. National Magnetic Resonance Facility at Madison, Department of Biochemistry, University of Wisconsin, Madison, 433 Babcock Drive, Madison, WI 53706-1544, USA

*Department of Biochemistry, University of Wisconsin, Madison, 433 Babcock Drive, Madison, WI 53706-1544, USA.

Publication History

  1. Issue published online: 6 NOV 2009
  2. Article first published online: 9 OCT 2009
  3. Manuscript Accepted: 9 SEP 2009
  4. Manuscript Received: 20 AUG 2009

Funded by

  • NIH. Grant Number: P41 RR02301.I.A.L.
  • NIH. Grant Numbers: P41 RR02301, P41 GM GM66326

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Keywords:

  • data mining;
  • data organization;
  • data visualization;
  • metabolite identification;
  • metabolite quantification;
  • NMR-based metabolomics;
  • region of interest;
  • software;
  • two-dimensional proton-carbon NMR

Abstract

Despite the extensive use of nuclear magnetic resonance (NMR) for metabolomics, no publicly available tools have been designed for identifying and quantifying metabolites across multiple spectra. We introduce here a new open source software tool, rNMR, which provides a simple graphics-based method for visualizing, identifying, and quantifying metabolites across multiple one- or two-dimensional NMR spectra. rNMR differs from existing software tools for NMR spectroscopy in that analyses are based on regions of interest (ROIs) rather than peak lists. ROIs contain all of the underlying NMR data within user-defined chemical shift ranges. ROIs can be inspected visually, and they support robust quantification of NMR signals. ROI-based analyses support simultaneous views of metabolite signals from up to hundreds of spectra, and ROI boundaries can be adjusted dynamically to ensure that signals corresponding to assigned atoms are analyzed consistently throughout the dataset. We describe how rNMR greatly reduces the time required for robust bioanalytical analysis of complex NMR data. An rNMR analysis yields a compact and transparent way of archiving the results from a metabolomics study so that it can be examined and evaluated by others. The rNMR website at http://rnmr.nmrfam.wisc.edu offers downloadable versions of rNMR for Windows, Macintosh, and Linux platforms along with extensive help documentation, instructional videos, and sample data. Copyright © 2009 John Wiley & Sons, Ltd.

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