Name
Abstract | ||
|---|---|---|
Motivation: Comparative metabolic profiling by nuclear magnetic resonance (NMR) is showing increasing promise for identifying inter-individual differences to drug response. Two dimensional (2D) H-1-C-13 NMR can reduce spectral overlap, a common problem of 1D H-1 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 50 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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1093/bioinformatics/btm427 | BIOINFORMATICS |
Keywords | Field | DocType |
spectrum,algorithms,proteome,magnetic resonance spectroscopy,heuristic algorithm,gene expression profiling,pattern matching,nuclear magnetic resonance,sequence alignment,amino acid sequence | Signal intensity,Similarity measure,NMR spectra database,Computer science,Two-dimensional nuclear magnetic resonance spectroscopy,Spectral line,Liver Extracts,Proton NMR,Bioinformatics,Nuclear magnetic resonance spectroscopy | Journal |
Volume | Issue | ISSN |
23 | 21 | 1367-4803 |
Citations | PageRank | References |
2 | 0.44 | 4 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Zheng | 1 | 2 | 0.78 |
| Peng Lu | 2 | 2 | 0.44 |
| Yanzhou Liu | 3 | 2 | 0.44 |
| Joseph Pease | 4 | 2 | 0.44 |
| Jonathan Usuka | 5 | 31 | 4.73 |
| Guochun Liao | 6 | 2 | 0.44 |
| Gary Peltz | 7 | 2 | 0.44 |