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MetaboMiner – semi-automated identification of metabolites from 2D NMR spectra of complex biofluids
Open accessAbstract
Background: One-dimensional (1D) 1 H nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomic studies involving biofluids and tissue extracts. There are several software packages that support compound identification and quantification via 1D 1 H NMR by spectral fitting techniques. Because 1D 1 H NMR spectra are characterized by extensive peak overlap or spectral congestion, two-dimensional (2D) NMR, with its increased spectral resolution, could potentially improve and even automate compound identification or quantification. However, the lack of dedicated software for this purpose significantly restricts the application of 2D NMR methods to most metabolomic studies. Results: We describe a standalone graphics software tool, called MetaboMiner, which can be used to automatically or semi-automatically identify metabolites in complex biofluids from 2D NMR spectra. MetaboMiner is able to handle both 1 H-1 H total correlation spectroscopy (TOCSY) and 1 H-13 C heteronuclear single quantum correlation (HSQC) data. It identifies compounds by comparing 2D spectral patterns in the NMR spectrum of the biofluid mixture with specially constructed libraries containing reference spectra of ~500 pure compounds. Tests using a variety of synthetic and real spectra of compound mixtures showed that MetaboMiner is able to identify >80% of detectable metabolites from good quality NMR spectra. Conclusion: MetaboMiner is a freely available, easy-to-use, NMR-based metabolomics tool that facilitates automatic peak processing, rapid compound identification, and facile spectrum annotation from either 2D TOCSY or HSQC spectra. Using comprehensive reference libraries coupled with robust algorithms for peak matching and compound identification, the program greatly simplifies the process of metabolite identification in complex 2D NMR spectra.
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Cited by
Thomas, C. M., Hong, T., van Pijkeren, J. P., Hemarajata, P., Trinh, D. V., Hu, W., Britton, R. A., Kalkum, M., & Versalovic, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0031951 · Cited by 491
Advances in metabolite identification
Wishart, D. S. (2011) · Bioanalysis · doi:10.4155/bio.11.155 · Cited by 284
Self-citationAnalytical and statistical approaches to metabolomics research
Issaq, H. J., Van, Q. N., Waybright, T. J., Muschik, G. M., & Veenstra, T. D. (2009) · Journal of Separation Science · doi:10.1002/jssc.200900152 · Cited by 227
ECMDB: the E-coli Metabolome Database
Guo, A. C., Jewison, T., Wilson, M., Liu, Y., Knox, C., Djoumbou, Y., Lo, P., Mandal, R., Krishnamurthy, R., & Wishart, D. S. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks992 · Cited by 164
Self-citationDiémé, B., Mavel, S., Blasco, H., Tripi, G., Bonnet-Brilhault, F., Malvy, J., Bocca, C., Andres, C. R., Nadal-Desbarats, L., & Emond, P. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00699 · Cited by 122
Metadata
- Authors
- Xia, J., Bjorndahl, T. C., Tang, P., & Wishart, D. S.
- Venue
- BMC Bioinformatics
- Published
- 2008-11-28
- Volume
- 9
- Issue
- 1
- Pages
- 507-507
- ISSN
- 1471-2105 (ISSN-L)
- OpenAlex
- W2135842485
- Cited by
- 204
- References
- 0
- FWCI
- 4.96
- Type
- journal article
- Language
- en
- Open access
- gold
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- Xia, J., Bjorndahl, T. C., Tang, P., & Wishart, D. S. (2008). MetaboMiner – semi-automated identification of metabolites from 2D NMR spectra of complex biofluids. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-9-507
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