The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Toward Global Metabolomics Analysis with Hydrophilic Interaction Liquid Chromatography–Mass Spectrometry: Improved Metabolite Identification by Retention Time Prediction
Open accessAbstract
Metabolomics is an emerging field of postgenomic biology concerned with comprehensive analysis of small molecules in biological systems. However, difficulties associated with the identification of detected metabolites currently limit its application. Here we demonstrate that a retention time prediction model can improve metabolite identification on a hydrophilic interaction chromatography (HILIC)-high-resolution mass spectrometry metabolomics platform. A quantitative structure retention relationship (QSRR) model, incorporating six physicochemical variables in a multiple-linear regression based on 120 authentic standard metabolites, shows good predictive ability for retention times of a range of metabolites (cross-validated R(2) = 0.82 and mean squared error = 0.14). The predicted retention times improved metabolite identification by removing 40% of the false identifications that occurred with identification by accurate mass alone. The importance of this procedure was demonstrated by putative identification of 690 metabolites in extracts of the protozoan parasite Trypanosoma brucei, thus allowing identified metabolites to be mapped onto an organism-wide metabolic network, providing opportunities for future studies of cellular metabolism from a global systems biology perspective.
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1 retracted
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Wolfender, J.-L., Marti, G., Thomas, A., & Bertrand, S. (2015) · Journal of Chromatography A · doi:10.1016/j.chroma.2014.10.091 · Cited by 635
Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics
Dunn, W. B., Erban, A., Weber, R. J. M., Creek, D. J., Brown, M., Breitling, R., Hankemeier, T., Goodacre, R., Neumann, S., Kopka, J., et al. (2013) · Metabolomics · doi:10.1007/s11306-012-0434-4 · Cited by 555
Self-citationTopic modeling for untargeted substructure exploration in metabolomics
van der Hooft, J. J. J., Wandy, J., Barrett, M. P., Burgess, K. E. V., & Rogers, S. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608041113 · Cited by 365
Self-citationMachine Learning Applications for Mass Spectrometry-Based Metabolomics
Liebal, U. W., Phan, A. N. T., Sudhakar, M., Raman, K., & Blank, L. M. (2020) · Metabolites · doi:10.3390/metabo10060243 · Cited by 357
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Metabolomics View on Gut Microbiome Modulation by Polyphenol-Rich Foods
Moco, S., Martin, F.-P. J., & Rezzi, S. (2012) · Journal of Proteome Research · doi:10.1021/pr300581s · Cited by 234
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Lipina, C., Macrae, K., Suhm, T., Weigert, C., Blachnio-Zabielska, A., Baranowski, M., Gorski, J., Burgess, K., & Hundal, H. S. (2013) · Diabetes · doi:10.2337/db13-0264 · Cited by 26
Self-citationRetractedRamaprasad, A., Burda, P.-C., Calvani, E., Sait, A. J., Palma-Duran, S. A., Withers-Martinez, C., Hackett, F., Macrae, J., Collinson, L., Gilberger, T. W., et al. (2022) · eLife · doi:10.7554/elife.82207 · Cited by 18
Ramaprasad, A., Burda, P.-C., Calvani, E., Sait, A., Palma-Duran, S. A., Withers-Martinez, C., Hackett, F., MacRae, J., Collinson, L., Gilberger, T.-W., et al. (2022) · doi:10.1101/2022.06.14.496138 · Cited by 1
Metadata
- Authors
- Creek, D. J., Jankevics, A., Breitling, R., Watson, D. G., Barrett, M. P., & Burgess, K. E. V.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2011-10-21
- Volume
- 83
- Issue
- 22
- Pages
- 8703-8710
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2125899377
- Cited by
- 344
- References
- 40
- FWCI
- 9.05
- Type
- journal article
- Language
- en
- Open access
- green
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