- Article -
| In Silico Biology 9, 0014 (2009); ©2009, Bioinformation Systems e.V. |
An emergent self-organizing map based analysis pipeline for comparative metabolome studies
Isam Haddad1,#, Karsten Hiller2,#,*, Eliane Frimmersdorf3, Beatrice Benkert1, Dietmar Schomburg3 and Dieter Jahn1
1 Technische Universität Braunschweig, Institut of Microbiology, Spielmannstraße 7, 38106 Braunschweig, Germany
2 Massachusetts Institute of Technology, Department of Chemical Engineering, 77 Massachusetts Ave., 56-439, Cambridge, MA 02140, USA
3 Technische Universität Braunschweig, Department of Bioinformatics and Biochemistry, Langer Kamp 19b, 38106 Braunschweig, Germany
* Corresponding author
Email: khiller@mit.edu
Edited by H. Michael; received February 06, 2009; revised March 23, 2009; accepted March 30, 2009; published May 16, 2009
Abstract
Modern high-throughput techniques allow for the identification and quantification of hundreds of metabolites of a biological system which cover central parts of the metabolome. Due to the amount and complexity of obtained data there is an increasing need for the development of appropriate computational interpretation methods.
A novel data analysis pipeline designed for high-throughput determined metabolomic data is presented. The combination of principal component analysis (PCA) with emergent self-organizing maps (ESOM) and hierarchical cluster analysis (HCA) algorithms is used to unravel the structure underlying metabolomic data sets, including the detection of outliers. Observed differences between various analyzed metabolomes are automatically mapped and visualized using KEGG metabolic pathway maps. This way typical metabolic biomarker for data sets from various analyzed growth conditions and genetic backgrounds become visible. In order to validate the described methods we analyzed time resolved metabolomic datasets obtained for Corynebacterium glutamicum cells grown on various carbon sources consisting of 126 different metabolic patterns.
The analysis pipeline was implemented in the user-friendly Java software eSOMet. The software was successfully used for the clustering of the metabolome data mentioned above. Metabolic biomarkers typical for the utilized carbon sources and analyzed growth phases were identified.
Keywords: emergent self-organizing maps, cluster analysis, comparative metabolome data analysis