N. Reena, A. Chandrasekar, A. Riju, P. Nima, S. Eapen, M. Anandaraj
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Gene identification in Phytophthora capsici through expressed sequence tags
Expressed Sequence Tags (ESTs) are a rich source of information for gene discovery. In this paper, we describe the annotation of ESTs of Phytophthora capsici whose complete genome is not yet available. P. capsici is an Oomycete plant pathogen capable of infecting wide range of plants including cucumber, squash, melons, pumpkin, pepper, tomato and eggplant. In India it causes severe economic losses in black pepper, chillies and cocoa. Towards the understanding of gene function in P. capsici, we undertook the functional annotation of ESTs available at NCBI. A total of 56,457 ESTs were downloaded from NCBI and assembled into 5966 contigs. Functional categorization of these ESTs was performed using database similarity search. By functional analysis, we estimated that 84.73% of transcripts encode significant proteins. The most prominent sequences corresponds to members of metabolic pathways, avirulence-associated protein, beta-tubulin, calcium/calmodulin dependent protein kinase 3, catalase, endo-1, 4-betaglucanase, cyst germination specific acidic repeat protein precursor, elicitin-like protein, glucanase inhibitor protein, heat shock protein, Kazal-like serine protease inhibitor, mitogen-activated protein kinase, ribosomal protein, serine/threonine kinase, syntaxin and ubiquitin. This EST-gene discovery information can be used to design sequence specific markers for P. capsici identification.
In Silico BiologyComputer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍:
The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.