An emergent self-organizing map based analysis pipeline for comparative metabolome studies.

Modern high-throughput techniques allow for the identification and quantification of hundreds of metabolites ofa 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.