Metabolite identification and molecular fingerprint prediction via machine learning
published: Oct. 23, 2012, recorded: September 2012, views: 2792
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Motivation: Metabolite identification from tandem mass spectra is an important problem in
metabolomics, underpinning subsequent metabolic modelling and network analysis. Yet, currently
this task requires matching the observed spectrum against a database of reference spectra
originating from similar equipment and closely matching operating parameters, a condition that is
rarely satisfied in public repositories. Furthermore, the computational support for identification of
molecules not present in reference databases is lacking. Recent efforts in assembling large public
mass spectral databases such as MassBank have opened the door for the development of a new
genre of metabolite identification methods.
Results: We introduce a novel framework for prediction of molecular characteristics and identification of metabolites from tandem mass spectra using machine learning with the support vector machine (SVM). Our approach is to first predict a large set of molecular properties of the unknown metabolite from salient tandem mass spectral signals, and in the second step to use the predicted properties for matching against large molecule databases, such as PubChem. We demonstrate that several molecular properties can be predicted to high accuracy, and that they are useful in de novo metabolite identification, where the reference database does not contain any spectra of the same molecule.
Availability: An Matlab/Python package of the FingerID tool is freely available on the web at http://www.sourceforge.net/p/fingerid.
Download slides: mlsb2012_heinonen_metabolite_01.pdf (945.0 KB)
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