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


BACKGROUND
http://appliedbioinformatics.wur.nl/GC-MS.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Mihaleva
V V
VV
Applied Bioinformatics, Plant Research International, Droevendaalsesteeg 1, Wageningen, The Netherlands.
Verhoeven
H A
HA
de Vos
R C H
RC
Hall
R D
RD
van Ham
R C H J
RC
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods*; D016014:Linear Models; D055432:Metabolomics / Q000379:methods*