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Bioinformatics Advance Access published online on September 10, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm427
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

2D NMR Metabonomic Analysis: A Novel Method for Automated Peak Alignment

Ming Zheng 1, Peng Lu 1, Yanzhou Liu 2, Joseph Pease 2, Jonathan Usuka 1, Guochun Liao 1,* and Gary Peltz 1

1Department of Genetics & Genomics, 2NMR Spectroscopy, Roche Palo Alto LLC, Palo Alto, CA 94304

*To whom correspondence should be addressed. Dr. Guochun Liao, E-mail: guochun.liao{at}roche.com


  Abstract

Motivation: Comparative metabolic profiling by nuclear magnetic resonance (NMR) is showing increasing promise for identifying interindividual differences to drug response. Two dimensional (2D) 1H-13C NMR can reduce spectral overlap, a common problem of 1D 1H NMR. However, the peak alignment tools for 1D NMR spectra are not well suited for 2D NMR. An automated and statistically robust method for aligning 2D NMR peaks is required to enable comparative metabonomic analysis using 2D NMR.

Results: A novel statistical method was developed to align NMR peaks that represent the same chemical groups across multiple 2D NMR spectra. The degree of local pattern match among peaks in different spectra is assessed using a similarity measure, and a heuristic algorithm maximizes the similarity measure for peaks across the whole spectrum. This peak alignment method was used to align peaks in 2D NMR spectra of endogenous metabolites in liver extracts obtained from four inbred mouse strains in the study of acetaminophen-induced liver toxicity. This automated alignment method was validated by manual examination of the top fifty peaks as ranked by signal intensity. Manual inspection of 1872 peaks in 39 different spectra demonstrated that the automated algorithm correctly aligned 1810 (96.7%) peaks.

Availability: Algorithm is available upon request.

Contact: guochun.liao@roche.com

Associate Editor: Prof. John Quackenbush

Received on May 17, 2007; revised on August 15, 2007; accepted on August 16, 2007

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