Automated procedure for candidate compound selection in GC-MS metabolomics based on prediction of Kovats retention index

• Published in: Bioinformatics 2009-03, Vol.25 (6), p.787-794
• Main Authors: Mihaleva, V. V., Verhoeven, H. A., de Vos, R. C. H., Hall, R. D., van Ham, R. C. H. J.
• Format: Article
• Language: English
• Subjects: Algorithms; Biological and medical sciences; Computational Biology - methods; Fundamental and applied biological sciences. Psychology; Gas Chromatography-Mass Spectrometry - methods; General aspects; Linear Models; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Metabolomics - methods
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btp056

Motivation: Matching both the retention index (RI) and the mass spectrum of an unknown compound against a mass spectral reference library provides strong evidence for a correct identification of that compound. Data on retention indices are, however, available for only a small fraction of the compounds in such libraries. We propose a quantitative structure-RI model that enables the ranking and filtering of putative identifications of compounds for which the predicted RI falls outside a predefined window. Results: We constructed multiple linear regression and support vector regression (SVR) models using a set of descriptors obtained with a genetic algorithm as variable selection method. The SVR model is a significant improvement over previous models built for structurally diverse compounds as it covers a large range (360–4100) of RI values and gives better prediction of isomer compounds. The hit list reduction varied from 41% to 60% and depended on the size of the original hit list. Large hit lists were reduced to a greater extend compared with small hit lists. Availability: http://appliedbioinformatics.wur.nl/GC-MS Contact: [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.