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Showing posts for the Article of Interest - Xia et al. Nucleic Acids Res. 2009 May 8. [Epub] topic:
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dmutch@uoguelph.ca
[May 21 2009 at 09:10:30 AM] |
The field of transcriptomics (i.e. global gene expression) has, in many ways, provided a useful guide during the development of complementary fields such as proteomics (protein analysis) and metabolomics (metabolite analysis). Indeed, several of the obstacles encountered with gene expression analyses have been avoided and solutions have been reached more quickly by avoiding the pitfalls experienced in the early days of transcriptomics. The recent article by Wishart and colleagues demonstrates that the high dimensionality datasets generated by transcriptomics and metabolomics can be processed in much the same way. Many tools are available on the internet for the analysis of metabolite datasets; however, no single platform existed with which a user could process, normalize, analyze and annotate metabolite data obtained from a variety of sources. MetaboAnalyst is a web-based metabolomic data processing tool that is extremely flexible and user-friendly; thereby providing metabolomic researchers with an important new tool that supports both biomarker discovery and two-group discrimination analyses. MetaboAnalyst is suitable for both global and targeted metabolite analyses, where compound concentration tables, binned spectral data, NMR or MS peak lists, and raw GC-MS / LC-MS data can be examined. A variety of methods are available for data normalization and data analysis, including both unsupervised and supervised algorithms. Finally, data is annotated and pathway mapping is available; however, as recognized by the authors, annotation remains the major obstacle in metabolomics research since this will be only as good as that offered through publicly available databases. Nevertheless, the MetaboAnalyst data processing tool is a welcome resource for current metabolomic researchers and will undoubtedly help attract scientists who were previously hesitant to incorporate metabolomics into their research programs because of the large and daunting datasets that need to be analyzed.
Full Reference: Xia et al. Nucleic Acids Res. 2009 May 8. [Epub]: PMID: 19429898.
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