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Advisor(s)
Abstract(s)
During must fermentation by Saccharomyces cerevisiae strains thousands of volatile aroma compounds
are formed. The objective of the present work was to adapt computational approaches to analyze
pheno-metabolomic diversity of a S. cerevisiae strain collection with different origins. Phenotypic and
genetic characterization together with individual must fermentations were performed, and metabolites
relevant to aromatic profiles were determined. Experimental results were projected onto a common coordinates
system, revealing 17 statistical-relevant multi-dimensional modules, combining sets of mostcorrelated
features of noteworthy biological importance. The present method allowed, as a breakthrough,
to combine genetic, phenotypic and metabolomic data, which has not been possible so far due to difficulties
in comparing different types of data. Therefore, the proposed computational approach revealed as
successful to shed light into the holistic characterization of S. cerevisiae pheno-metabolome in must fermentative
conditions. This will allow the identification of combined relevant features with application in
selection of good winemaking strains.
Description
Keywords
Saccharomyces cerevisiae Data-fusion Wine yeasts Metabolomics Matrix factorization
Pedagogical Context
Citation
FRANCO-DUARTE, Ricardo; UMEK, Lan; MENDES, Inês; CASTRO, Cristiana C.; FONSECA, Nuno; MARTINS, Rosa; FERREIRA, António César Silva; SAMPAIO, Paula; PAIS, Célia; SCHULLER, Dorit - New integrative computational approaches unveil the saccharomyces cerevisiae pheno-metabolomic fermentative profile and allow strain selection for winemaking. Food Chemistry. ISSN 0308-8146. Vol. 211 (2016), p. 509-520
Publisher
Elsevier