Journal Article

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

V. V. Mihaleva, H. A. Verhoeven, R. C. H. de Vos, R. D. Hall and R. C. H. J. van Ham

in Bioinformatics

Volume 25, issue 6, pages 787-794
Published in print March 2009 | ISSN: 1367-4803
Published online January 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp056
Automated procedure for candidate compound selection in GC-MS metabolomics based on prediction of Kovats retention index

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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: roeland.vanham@wur.nl

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6498 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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