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Tipo: Artigo
Título: In silico identification of coffee genome expressed sequences potentially associated with resistance to diseases
Autor(es): Alvarenga, Samuel Mazzinghy
Maciel-Zambolim, Eunize
Zambolim, Laércio
Sakiyama, Ney Sussumu
Caixeta, Eveline Teixeira
Thiebaut, Flávia
Hufnagel, Bárbara
Abstract: Sequences potentially associated with coffee resistance to diseases were identified by in silico analyses using the database of the Brazilian Coffee Genome Project (BCGP). Keywords corresponding to plant resistance mechanisms to pathogens identified in the literature were used as baits for data mining. Expressed sequence tags (ESTs) related to each of these keywords were identified with tools available in the BCGP bioinformatics platform. A total of 11,300 ESTs were mined. These ESTs were clustered and formed 979 EST-contigs with similarities to chitinases, kinases, cytochrome P450 and nucleotide binding site-leucine rich repeat (NBS-LRR) proteins, as well as with proteins related to disease resistance, pathogenesis, hypersensitivity response (HR) and plant defense responses to diseases. The 140 EST-contigs identified through the keyword NBS-LRR were classified according to function. This classification allowed association of the predicted products of EST-contigs with biological processes, including host defense and apoptosis, and with molecular functions such as nucleotide binding and signal transducer activity. Fisher's exact test was used to examine the significance of differences in contig expression between libraries representing the responses to biotic stress challenges and other libraries from the BCGP. This analysis revealed seven contigs highly similar to catalase, chitinase, protein with a BURP domain and unknown proteins. The involvement of these coffee proteins in plant responses to disease is discussed.
Palavras-chave: Coffea
Data mining
In silico
Editor: Genetics and Molecular Biology
Tipo de Acesso: Open Access
Data do documento: 1-Dez-2010
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