Prediction of metabolite identity from accurate mass, migration time prediction and isotopic pattern information in CE-TOFMS data.
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.
Sugimoto
Masahiro
M
Institute for Advanced Biosciences, Keio University, Yamagata, Japan. msugi@sfc.keio.ac.jp
Hirayama
Akiyoshi
A
Robert
Martin
M
Abe
Shinobu
S
Soga
Tomoyoshi
T
Tomita
Masaru
M
D016208:Databases, Factual; D019075:Electrophoresis, Capillary / Q000379:methods*; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D055442:Metabolome; D055432:Metabolomics / Q000379:methods*; D015233:Models, Statistical; D014556:Urine / Q000737:chemistry