Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasets

Autores
Ribone, Andrés I.; Gonzales, Sergio; Paniego, Norma; Lía, Verónica; Rivarola, Máximo
Año de publicación
2021
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
We present the first preliminary sunflower gene co-expression network using public transcriptome data in Helianthus annuus and show its utility in identifying and classifying uncharacterized genes involved in stress response. The locus HanXRQChr09g0248321 was identified and linked to several WRKY transcription factors in an enriched “stressed-response” module. Moreover, the homologue in Arabidopsis thaliana was shown to be differentially expressed in multiple “stress” conditions. We present our work and validate our methodology to existing knowledge and show its capability to identify/rank new candidates for crop breeding programs. Our future goal is to link genetic variation with gene networks to understand phenotypic variability in sunflower stress responses.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Gene Co-expression Networks
RNAseq
Functional Annotation
Big Data
Sunflower
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/140689
Acceder
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasetsRibone, Andrés I.Gonzales, SergioPaniego, NormaLía, VerónicaRivarola, MáximoCiencias InformáticasGene Co-expression NetworksRNAseqFunctional AnnotationBig DataSunflowerWe present the first preliminary sunflower gene co-expression network using public transcriptome data in Helianthus annuus and show its utility in identifying and classifying uncharacterized genes involved in stress response. The locus HanXRQChr09g0248321 was identified and linked to several WRKY transcription factors in an enriched “stressed-response” module. Moreover, the homologue in Arabidopsis thaliana was shown to be differentially expressed in multiple “stress” conditions. We present our work and validate our methodology to existing knowledge and show its capability to identify/rank new candidates for crop breeding programs. Our future goal is to link genetic variation with gene networks to understand phenotypic variability in sunflower stress responses.Sociedad Argentina de Informática e Investigación Operativa2021-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf104-112http://sedici.unlp.edu.ar/handle/10915/140689enginfo:eu-repo/semantics/altIdentifier/url/http://50jaiio.sadio.org.ar/pdfs/cai/CAI-15.pdfinfo:eu-repo/semantics/altIdentifier/issn/2525-0949info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:35:43Zoai:sedici.unlp.edu.ar:10915/140689Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:35:44.105SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasets
title Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasets
spellingShingle Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasets
Ribone, Andrés I.
Ciencias Informáticas
Gene Co-expression Networks
RNAseq
Functional Annotation
Big Data
Sunflower
title_short Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasets
title_full Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasets
title_fullStr Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasets
title_full_unstemmed Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasets
title_sort Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasets
dc.creator.none.fl_str_mv Ribone, Andrés I.
Gonzales, Sergio
Paniego, Norma
Lía, Verónica
Rivarola, Máximo
author Ribone, Andrés I.
author_facet Ribone, Andrés I.
Gonzales, Sergio
Paniego, Norma
Lía, Verónica
Rivarola, Máximo
author_role author
author2 Gonzales, Sergio
Paniego, Norma
Lía, Verónica
Rivarola, Máximo
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Gene Co-expression Networks
RNAseq
Functional Annotation
Big Data
Sunflower
topic Ciencias Informáticas
Gene Co-expression Networks
RNAseq
Functional Annotation
Big Data
Sunflower
dc.description.none.fl_txt_mv We present the first preliminary sunflower gene co-expression network using public transcriptome data in Helianthus annuus and show its utility in identifying and classifying uncharacterized genes involved in stress response. The locus HanXRQChr09g0248321 was identified and linked to several WRKY transcription factors in an enriched “stressed-response” module. Moreover, the homologue in Arabidopsis thaliana was shown to be differentially expressed in multiple “stress” conditions. We present our work and validate our methodology to existing knowledge and show its capability to identify/rank new candidates for crop breeding programs. Our future goal is to link genetic variation with gene networks to understand phenotypic variability in sunflower stress responses.
Sociedad Argentina de Informática e Investigación Operativa
description We present the first preliminary sunflower gene co-expression network using public transcriptome data in Helianthus annuus and show its utility in identifying and classifying uncharacterized genes involved in stress response. The locus HanXRQChr09g0248321 was identified and linked to several WRKY transcription factors in an enriched “stressed-response” module. Moreover, the homologue in Arabidopsis thaliana was shown to be differentially expressed in multiple “stress” conditions. We present our work and validate our methodology to existing knowledge and show its capability to identify/rank new candidates for crop breeding programs. Our future goal is to link genetic variation with gene networks to understand phenotypic variability in sunflower stress responses.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/140689
url http://sedici.unlp.edu.ar/handle/10915/140689
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/issn/2525-0949
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
dc.format.none.fl_str_mv application/pdf
104-112
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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instname_str Universidad Nacional de La Plata
instacron_str UNLP
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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