In silico identification of coffee genome expressed sequences potentially associated with resistance to diseases
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Genetics and Molecular Biology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572010000400031 |
Resumo: | 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. |
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Genetics and Molecular Biology |
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In silico identification of coffee genome expressed sequences potentially associated with resistance to diseasesCoffeadata miningESTsgenomicsin silicobioinformaticsSequences 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.Sociedade Brasileira de Genética2010-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572010000400031Genetics and Molecular Biology v.33 n.4 2010reponame:Genetics and Molecular Biologyinstname:Sociedade Brasileira de Genética (SBG)instacron:SBG10.1590/S1415-47572010000400031info:eu-repo/semantics/openAccessAlvarenga,Samuel MazzinghyCaixeta,Eveline TeixeiraHufnagel,BárbaraThiebaut,FláviaMaciel-Zambolim,EunizeZambolimand,LaércioSakiyama,Ney Sussumueng2011-01-06T00:00:00Zoai:scielo:S1415-47572010000400031Revistahttp://www.gmb.org.br/ONGhttps://old.scielo.br/oai/scielo-oai.php||editor@gmb.org.br1678-46851415-4757opendoar:2011-01-06T00:00Genetics and Molecular Biology - Sociedade Brasileira de Genética (SBG)false |
dc.title.none.fl_str_mv |
In silico identification of coffee genome expressed sequences potentially associated with resistance to diseases |
title |
In silico identification of coffee genome expressed sequences potentially associated with resistance to diseases |
spellingShingle |
In silico identification of coffee genome expressed sequences potentially associated with resistance to diseases Alvarenga,Samuel Mazzinghy Coffea data mining ESTs genomics in silico bioinformatics |
title_short |
In silico identification of coffee genome expressed sequences potentially associated with resistance to diseases |
title_full |
In silico identification of coffee genome expressed sequences potentially associated with resistance to diseases |
title_fullStr |
In silico identification of coffee genome expressed sequences potentially associated with resistance to diseases |
title_full_unstemmed |
In silico identification of coffee genome expressed sequences potentially associated with resistance to diseases |
title_sort |
In silico identification of coffee genome expressed sequences potentially associated with resistance to diseases |
author |
Alvarenga,Samuel Mazzinghy |
author_facet |
Alvarenga,Samuel Mazzinghy Caixeta,Eveline Teixeira Hufnagel,Bárbara Thiebaut,Flávia Maciel-Zambolim,Eunize Zambolimand,Laércio Sakiyama,Ney Sussumu |
author_role |
author |
author2 |
Caixeta,Eveline Teixeira Hufnagel,Bárbara Thiebaut,Flávia Maciel-Zambolim,Eunize Zambolimand,Laércio Sakiyama,Ney Sussumu |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Alvarenga,Samuel Mazzinghy Caixeta,Eveline Teixeira Hufnagel,Bárbara Thiebaut,Flávia Maciel-Zambolim,Eunize Zambolimand,Laércio Sakiyama,Ney Sussumu |
dc.subject.por.fl_str_mv |
Coffea data mining ESTs genomics in silico bioinformatics |
topic |
Coffea data mining ESTs genomics in silico bioinformatics |
description |
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. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572010000400031 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572010000400031 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S1415-47572010000400031 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Genética |
publisher.none.fl_str_mv |
Sociedade Brasileira de Genética |
dc.source.none.fl_str_mv |
Genetics and Molecular Biology v.33 n.4 2010 reponame:Genetics and Molecular Biology instname:Sociedade Brasileira de Genética (SBG) instacron:SBG |
instname_str |
Sociedade Brasileira de Genética (SBG) |
instacron_str |
SBG |
institution |
SBG |
reponame_str |
Genetics and Molecular Biology |
collection |
Genetics and Molecular Biology |
repository.name.fl_str_mv |
Genetics and Molecular Biology - Sociedade Brasileira de Genética (SBG) |
repository.mail.fl_str_mv |
||editor@gmb.org.br |
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1752122383148253184 |