Bioinformatic approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection

Bibliographic Details
Main Author: Duarte, Ricardo Franco
Publication Date: 2008
Other Authors: Umek, Lan, Zupan, Blaz, Schuller, Dorit Elisabeth
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/1822/8893
Summary: Painel apresentado por D. Schuller
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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spelling Bioinformatic approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collectionSaccharomyces cerevisiaeStrain collectionMicrosatellitePhenotypic diversityGenetic diversityMachine learningPainel apresentado por D. SchullerThe objective of the present study was to compare genetic and phenotypic variation of 103 Saccharomyces cerevisiae strains isolated from winemaking environments. We used bioinformatics approaches to identify genetically similary strains with specific phenotypes and to estimate a strain's biotechnological potential. A S. cerevisiae collection, comprising 440 strains that were obtained from winemaking environments in Portugal has been constituted during the last years. All strains were genetically characterized by a set of eleven highly polymorphic microsatellites and showed unique allelic combinations. Using neural networks, a subset of 103 genetically most diverse strains was chosen for phenotypic analysis, that included growth in synthetic must media at various temperatures, utilization of carbon sources (glucose, ribose, arabinose, xylose, saccharose, galactose, rafinose, maltose, glycerol, potassium acetate and pyruvic acid), growth in ethanol containing media, evaluation of osmotic and oxidative stress resistance, H2S production and utilization of different nitrogen sources. Using supervised data mining approaches we have found that genotype represented with presence/absence of eleven microsatellites relates well with geographical location (performance evaluation using leave-out-out technique resulted in high performance scores; e.g., area under ROC curve was above 0.8 for a number of standard machine learning approaches tested). To find relations between phenotypes and genotypes, we used a two-step approach which first hierarchically clusters the strains according to their phenotype, and then tests if the resulting sub-clusters are identifiable using strain’s genetic data. Several groups of strains with similar phenotype profiles and common features in genotype were identified this way, and they are subject to further investigations.Programa POCI 2010 (FEDER/FCT, POCTI/AGR/56102/2004) e AGRO (ENOSAFE, N.º 762).Universidade do MinhoDuarte, Ricardo FrancoUmek, LanZupan, BlazSchuller, Dorit Elisabeth2008-082008-08-01T00:00:00Zconference posterinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/8893engINTERNATIONAL CONGRESS ON YEASTS, 12, Kiev, 2008 - "ICY 2008". [S.l. : s.n., 2008].info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T06:50:09Zoai:repositorium.sdum.uminho.pt:1822/8893Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T16:05:57.944054Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Bioinformatic approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection
title Bioinformatic approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection
spellingShingle Bioinformatic approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection
Duarte, Ricardo Franco
Saccharomyces cerevisiae
Strain collection
Microsatellite
Phenotypic diversity
Genetic diversity
Machine learning
title_short Bioinformatic approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection
title_full Bioinformatic approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection
title_fullStr Bioinformatic approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection
title_full_unstemmed Bioinformatic approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection
title_sort Bioinformatic approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection
author Duarte, Ricardo Franco
author_facet Duarte, Ricardo Franco
Umek, Lan
Zupan, Blaz
Schuller, Dorit Elisabeth
author_role author
author2 Umek, Lan
Zupan, Blaz
Schuller, Dorit Elisabeth
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Duarte, Ricardo Franco
Umek, Lan
Zupan, Blaz
Schuller, Dorit Elisabeth
dc.subject.por.fl_str_mv Saccharomyces cerevisiae
Strain collection
Microsatellite
Phenotypic diversity
Genetic diversity
Machine learning
topic Saccharomyces cerevisiae
Strain collection
Microsatellite
Phenotypic diversity
Genetic diversity
Machine learning
description Painel apresentado por D. Schuller
publishDate 2008
dc.date.none.fl_str_mv 2008-08
2008-08-01T00:00:00Z
dc.type.driver.fl_str_mv conference poster
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/8893
url http://hdl.handle.net/1822/8893
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv INTERNATIONAL CONGRESS ON YEASTS, 12, Kiev, 2008 - "ICY 2008". [S.l. : s.n., 2008].
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833595735805788160

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