Contributed by Narine Arabyan
Metabolomics is a newly blooming and rapidly expanding field that combines the sciences of biology, chemistry, mathematics, and computer technology. Metabolomics is the study of metabolites or small molecules inside the cells and biological systems. This provides a unique chemical fingerprints that specific cellular processes leave behind, producing a snapshot for a certain point in time. A complete or global metabolic profiles and their comparative analysis can be obtained. However, the major limitation profiling these metabolites is the analysis and identification of these compounds. Hence, to analyze such details in a biological system requires robust platforms. Separation techniques (GC and LC) are used along with detection techniques (MS and NMR) to help metabolic profiling. Hyphenated techniques such as GC-MS, LC-MS, LC-NMR, and others are now being used for high-resolution.
Metabolomics is expanding at a fast speed along with the enhancement of technology. This provides large applications in life sciences. The importance of metabolomics has been identified in food sciences to help check the quality, taste, and nutritional value of food and drinks; in pharmaceutical industry to help identify new compounds which can be used to develop new drugs; in infectious diseases to help identify unique metabolites that can be used as biomarkers to identify diseases; in cancer to identify early stages of cancer to provide better and more effective treatment; and in agriculture to investigate how plants respond to different environmental conditions.
There are many programs and databases that help to analyze data and to identify compounds. I like to use MetaboAnalyst 3.0 (http://www.metaboanalyst.ca/) to perform analysis of metabolomic data. This program has many different functional modules for different purposes, such as: Statistical analysis, Enrichment analysis, Pathway analysis, Time series analysis, Power analysis, Biomarker analysis, Integrated pathway analysis, and other utilities. For more details please see the three references.
Xia, J., Sinelnikov, I., Han, B., and Wishart, D.S. (2015) MetaboAnalyst 3.0 – making metabolomics more meaningful. Nucl. Acids Res. (DOI: 10.1093/nar/gkv380).
Xia, J., Mandal, R., Sinelnikov, I., Broadhurst, D., and Wishart, D.S. (2012) MetaboAnalyst 2.0 – a comprehensive server for metabolomic data analysis. Nucl. Acids Res. 40, W127-W133.
Xia, J., Psychogios, N., Young, N. and Wishart, D.S. (2009) MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucl. Acids Res. 37, W652-660.