@Article{IPB-1299,
author = {Dunn, W. B. and Erban, A. and Weber, R. J. M. and Creek, D. J. and Brown, M. and Breitling, R. and Hankemeier, T. and Goodacre, R. and Neumann, S. and Kopka, J. and Viant, M. R.},
title = {{Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics}},
year = {2013},
pages = {44-66},
journal = {Metabolomics},
doi = {10.1007/s11306-012-0434-4},
volume = {9},
abstract = {Metabolomics has advanced significantly in the past 10 years with important developments related to hardware, software and methodologies and an increasing complexity of applications. In discovery-based investigations, applying untargeted analytical methods, thousands of metabolites can be detected with no or limited prior knowledge of the metabolite composition of samples. In these cases, metabolite identification is required following data acquisition and processing. Currently, the process of metabolite identification in untargeted metabolomic studies is a significant bottleneck in deriving biological knowledge from metabolomic studies. In this review we highlight the different traditional and emerging tools and strategies applied to identify subsets of metabolites detected in untargeted metabolomic studies applying various mass spectrometry platforms. We indicate the workflows which are routinely applied and highlight the current limitations which need to be overcome to provide efficient, accurate and robust identification of metabolites in untargeted metabolomic studies. These workflows apply to the identification of metabolites, for which the structure can be assigned based on entries in databases, and for those which are not yet stored in databases and which require a de novo structure elucidation.}    
}