Prediction of metabolite identity from accurate mass, migration time prediction and isotopic pattern information in CE-TOFMS data
| Autor: | Tomoyoshi Soga, Shinobu Abe, Akiyoshi Hirayama, Masahiro Sugimoto, Martin Robert, Masaru Tomita |
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| Rok vydání: | 2010 |
| Předmět: |
Urine chemistry
Models Statistical Databases Factual Metabolite Clinical Biochemistry Ce tofms Analytical chemistry Electrophoresis Capillary Urine Biology Biochemistry Mass Spectrometry Analytical Chemistry Support vector machine Mass migration chemistry.chemical_compound Metabolomics chemistry Metabolome Humans Biological system |
| Zdroj: | ELECTROPHORESIS. 31:2311-2318 |
| ISSN: | 1522-2683 0173-0835 |
| DOI: | 10.1002/elps.200900584 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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