Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/12411
Título : In silico and quantitative analyses of mads-box genes in Coffea arabica
Autor: Oliveira, Raphael Ricon de
Chalfun-Junior, Antonio
Paiva, Luciano Vilela
Andrade, Alan Carvalho
Palavras-chave: Coffea arabica
Floral development
Transcriptome
MADS-box genes
qRT-PCR
Publicador: Springer
Data da publicação: Set-2010
Referência: OLIVEIRA, R. R. et al. In silico and quantitative analyses of mads-box genes in Coffea arabica. Plant Molecular Biology Reporter, Athens, v. 28, n. 3, p. 460-472, 2010.
Abstract: MADS-box genes comprise a family of transcription factors that act as key regulators in many cellular development processes of several organisms. Members of this family have highly conserved regions and play important roles as transcription factors, activating target genes. The present work aimed at analyzing the MADS-box gene family present in a database generated by the Brazilian Coffee Genome Project (CAFEST) as well as of observing their respective sites of expression within these data. Through the use of bioinformatics tools, it was possible to identify and classify 26 expressed sequence tag contigs, 13 of them were expressed exclusively in vegetative tissues, 11 in reproductive tissues, and two in both. Later, quantitative analysis by quantitative reverse transcriptase PCR was carried out for three of them belonging to the groups of genes APETALA3 (B genes), AGAMOUS (C genes), and SEPALLATAS (E genes), which could be compared with the expression profile of in silico analysis and of its putative orthologous genes. Therefore, this study aims at a better understanding of the development processes performed by this family of genes in Coffea arabica, mainly in reproductive organs, as well as to compare functions of MADS-box orthologous studied in other species.
URI: http://link.springer.com/article/10.1007/s11105-009-0173-5
http://repositorio.ufla.br/jspui/handle/1/12411
Idioma: en
Aparece nas coleções:DBI - Artigos publicados em periódicos

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