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Prediction of metabolite identity from accurate mass, migration time prediction and isotopic pattern information in CE‐TOFMS data
Author(s) -
Sugimoto Masahiro,
Hirayama Akiyoshi,
Robert Martin,
Abe Shinobu,
Soga Tomoyoshi,
Tomita Masaru
Publication year - 2010
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.200900584
Subject(s) - metabolite , identity (music) , chemistry , mass spectrometry , computational biology , pattern recognition (psychology) , biological system , chromatography , computer science , artificial intelligence , biology , physics , biochemistry , acoustics
CE‐TOFMS is a powerful method for profiling charged metabolites. However, the limited availability of metabolite standards hinders the process of identifying compounds from detected features in CE‐TOFMS data sets. To overcome this problem, we developed a method to identify unknown peaks based on the predicted migration time ( t m ) and accurate m/z values. We developed a predictive model using 375 standard cationic metabolites and support vector regression. The model yielded good correlations between the predicted and measured t m ( R =0.952 and 0.905 using complete and cross‐validation data sets, respectively). Using the trained model, we subsequently predicted the t m for 2938 metabolites available from the public databases and assigned tentative identities to noise‐filtered features in human urine samples. While 38.9% of the peaks were assigned metabolite names by matching with the standard library alone, the proportion increased to 52.2%. The proposed methodology increases the value of metabolomic data sets obtained from CE‐TOFMS profiling.