Analytical and statistical approaches to metabolomics research

✍ Scribed by Haleem J. Issaq; Que N. Van; Timothy J. Waybright; Gary M. Muschik; Timothy D. Veenstra

• Publisher: John Wiley and Sons
• Year: 2009
• Tongue: English
• Weight: 808 KB
• Volume: 32
• Category: Article
• ISSN: 1615-9306
• DOI: 10.1002/jssc.200900152

✦ Synopsis

Abstract

Metabolomics, the global profiling of metabolites in different living systems, has experienced a rekindling of interest partially due to the improved detection capabilities of the instrumental techniques currently being used in this area of biomedical research. The analytical methods of choice for the analysis of metabolites in search of disease biomarkers in biological specimens, and for the study of various low molecular weight metabolic pathways include NMR spectroscopy, GC/MS, CE/MS, and HPLC/MS. Global metabolite analysis and profiling of two different sets of data results in a plethora of data that is difficult to manage or interpret manually because of their subtle differences. Multivariate statistical methods and pattern‐recognition programs were developed to handle the acquired data and to search for the discriminating features between data acquired from two sample sets, healthy and diseased. Metabolomics have been used in toxicology, plant physiology, and biomedical research. In this paper, we discuss various aspects of metabolomic research including sample collection, handling, storage, requirements for sample analysis, peak alignment, data interpretation using statistical approaches, metabolite identification, and finally recommendations for successful analysis.