Computational approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/10410 |
Resumo: | Within this study, we have used a set of computational techniques to relate the genotypes and phenotypes of natural populations of Saccharomyces cerevisiae, using allelic information from 11 microsatellite loci and results from 24 phenotypic tests. A group of 103 strains was obtained from a larger S. cerevisiae winemaking strain collection by clustering with self-organizing maps. These strains were further characterized regarding their allelic combinations for 11 microsatellites and analysed in phenotypic screens that included taxonomic criteria (carbon and nitrogen assimilation tests, growth at different temperatures) and tests with biotechnological relevance (ethanol resistance, H2S or aromatic precursors formation). Phenotypic variability was rather high and each strain showed a unique phenotypic profile. The results, expressed as optical density (A640) after 22 h of growth, were in agreement with taxonomic data, although with some exceptions, since few strains were capable of consuming arabinose and ribose to a small extent. Based on microsatellite allelic information, na¨ıve Bayesian classifier correctly assigned (AUC = 0.81, p < 10−8) most of the strains to the vineyard from where they were isolated, despite their close location (50–100 km). We also identified subgroups of strains with similar values of a phenotypic feature and microsatellite allelic pattern (AUC >0.75). Subgroups were found for strains with low ethanol resistance, growth at 30 ◦C and growth in media containing galactose, raffinose or urea. The results demonstrate that computational approaches can be used to establish genotype–phenotype relations and to make predictions about a strain’s biotechnological potential. |
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Computational approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collectionSaccharomyces cerevisiaeIndigenous yeastMicrosatelliteGenotypePhenotypeBayesian classifierStrain collectionEthanol resistanceWinemakingScience & TechnologyWithin this study, we have used a set of computational techniques to relate the genotypes and phenotypes of natural populations of Saccharomyces cerevisiae, using allelic information from 11 microsatellite loci and results from 24 phenotypic tests. A group of 103 strains was obtained from a larger S. cerevisiae winemaking strain collection by clustering with self-organizing maps. These strains were further characterized regarding their allelic combinations for 11 microsatellites and analysed in phenotypic screens that included taxonomic criteria (carbon and nitrogen assimilation tests, growth at different temperatures) and tests with biotechnological relevance (ethanol resistance, H2S or aromatic precursors formation). Phenotypic variability was rather high and each strain showed a unique phenotypic profile. The results, expressed as optical density (A640) after 22 h of growth, were in agreement with taxonomic data, although with some exceptions, since few strains were capable of consuming arabinose and ribose to a small extent. Based on microsatellite allelic information, na¨ıve Bayesian classifier correctly assigned (AUC = 0.81, p < 10−8) most of the strains to the vineyard from where they were isolated, despite their close location (50–100 km). We also identified subgroups of strains with similar values of a phenotypic feature and microsatellite allelic pattern (AUC >0.75). Subgroups were found for strains with low ethanol resistance, growth at 30 ◦C and growth in media containing galactose, raffinose or urea. The results demonstrate that computational approaches can be used to establish genotype–phenotype relations and to make predictions about a strain’s biotechnological potential.Slovenian Research Agency - P2-0209, J2-9699, L2-1112AGRO - ENOSAFE, Nº 762JATOON softwareUnião Europeia (EU). Fundo Europeu de Desenvolvimento Regional (FEDER)Fundação para a Ciência e a Tecnologia (FCT) – POCI/AGR/56102/2004, PDTC/AGR-ALI/103392/2008, SFRH/BD/48591/2008John Wiley and SonsUniversidade do MinhoDuarte, Ricardo FrancoUmek, LanZupan, BlazSchuller, Dorit Elisabeth2009-122009-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/10410eng"Yeast". ISSN 0749-503X. 26:12 (2009) 675-692.0749-503X10.1002/yea.172819894212The definitive version is available at www3.interscience.wiley.cominfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:43:37Zoai:repositorium.sdum.uminho.pt:1822/10410Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:41:08.109238Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Computational approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection |
title |
Computational approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection |
spellingShingle |
Computational approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection Duarte, Ricardo Franco Saccharomyces cerevisiae Indigenous yeast Microsatellite Genotype Phenotype Bayesian classifier Strain collection Ethanol resistance Winemaking Science & Technology |
title_short |
Computational approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection |
title_full |
Computational approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection |
title_fullStr |
Computational approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection |
title_full_unstemmed |
Computational approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection |
title_sort |
Computational 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 Indigenous yeast Microsatellite Genotype Phenotype Bayesian classifier Strain collection Ethanol resistance Winemaking Science & Technology |
topic |
Saccharomyces cerevisiae Indigenous yeast Microsatellite Genotype Phenotype Bayesian classifier Strain collection Ethanol resistance Winemaking Science & Technology |
description |
Within this study, we have used a set of computational techniques to relate the genotypes and phenotypes of natural populations of Saccharomyces cerevisiae, using allelic information from 11 microsatellite loci and results from 24 phenotypic tests. A group of 103 strains was obtained from a larger S. cerevisiae winemaking strain collection by clustering with self-organizing maps. These strains were further characterized regarding their allelic combinations for 11 microsatellites and analysed in phenotypic screens that included taxonomic criteria (carbon and nitrogen assimilation tests, growth at different temperatures) and tests with biotechnological relevance (ethanol resistance, H2S or aromatic precursors formation). Phenotypic variability was rather high and each strain showed a unique phenotypic profile. The results, expressed as optical density (A640) after 22 h of growth, were in agreement with taxonomic data, although with some exceptions, since few strains were capable of consuming arabinose and ribose to a small extent. Based on microsatellite allelic information, na¨ıve Bayesian classifier correctly assigned (AUC = 0.81, p < 10−8) most of the strains to the vineyard from where they were isolated, despite their close location (50–100 km). We also identified subgroups of strains with similar values of a phenotypic feature and microsatellite allelic pattern (AUC >0.75). Subgroups were found for strains with low ethanol resistance, growth at 30 ◦C and growth in media containing galactose, raffinose or urea. The results demonstrate that computational approaches can be used to establish genotype–phenotype relations and to make predictions about a strain’s biotechnological potential. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009-12 2009-12-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/10410 |
url |
http://hdl.handle.net/1822/10410 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
"Yeast". ISSN 0749-503X. 26:12 (2009) 675-692. 0749-503X 10.1002/yea.1728 19894212 The definitive version is available at www3.interscience.wiley.com |
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.publisher.none.fl_str_mv |
John Wiley and Sons |
publisher.none.fl_str_mv |
John Wiley and Sons |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132959011241984 |