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

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 tr...

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Autores principales: Ribone, Andrés I., Gonzales, Sergio, Paniego, Norma, Lía, Verónica, Rivarola, Máximo
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2021
Materias:
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/140689
http://50jaiio.sadio.org.ar/pdfs/cai/CAI-15.pdf
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id I19-R120-10915-140689
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Gene Co-expression Networks
RNAseq
Functional Annotation
Big Data
Sunflower
spellingShingle Ciencias Informáticas
Gene Co-expression Networks
RNAseq
Functional Annotation
Big Data
Sunflower
Ribone, Andrés I.
Gonzales, Sergio
Paniego, Norma
Lía, Verónica
Rivarola, Máximo
Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasets
topic_facet Ciencias Informáticas
Gene Co-expression Networks
RNAseq
Functional Annotation
Big Data
Sunflower
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.
format Objeto de conferencia
Objeto de conferencia
author Ribone, Andrés I.
Gonzales, Sergio
Paniego, Norma
Lía, Verónica
Rivarola, Máximo
author_facet Ribone, Andrés I.
Gonzales, Sergio
Paniego, Norma
Lía, Verónica
Rivarola, Máximo
author_sort Ribone, Andrés I.
title Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasets
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
publishDate 2021
url http://sedici.unlp.edu.ar/handle/10915/140689
http://50jaiio.sadio.org.ar/pdfs/cai/CAI-15.pdf
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AT paniegonorma insightsintofunctionalclassificationviagenecoexpressionnetworksinsunflowerusingpublictranscriptomicdatasets
AT liaveronica insightsintofunctionalclassificationviagenecoexpressionnetworksinsunflowerusingpublictranscriptomicdatasets
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