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RRGPredictor, a set-theory-based tool for predicting pathogen-associated molecular pattern receptors (PRRs) and resistance (R) proteins from plants

机译:RRGPRedictor,一种用于预测来自植物的病原体相关的分子模式受体(PRRS)和抗性(R)蛋白的基于设定理论的工具

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摘要

In plant-pathogen interactions, plant immunity through pathogen-associated molecular pattern receptors (PAMPs) and R proteins, also called pattern recognition receptors (PRRs), occurs in different ways depending on both plant and pathogen species. The use and search for a structural pattern based on the presence and absence of characteristic domains, regardless of their disposition within a sequence, could be efficient in identifying PRRs proteins. Here, we develop a method mainly based on text mining and set theory to identify PRR and R genes that classify them into 13 categories based on the presence and absence of the main domains. Analyzing 24 plant and algae genomes, we showed that the RRGPredictor was more efficient, specific and sensitive than other tools already available, and identified PRR proteins with variations in size and in domain distribution throughout the sequence. Besides an easy identification of new plant PRRs proteins, RRGPredictor provided a low computational cost.
机译:在植物 - 病原体相互作用中,通过病原体相关分子模式受体(PAMP)和R蛋白的植物免疫,也称为模式识别受体(PRRS),取决于植物和病原体种类的不同方式发生。使用和搜索基于特征域的存在和不存在的结构模式,无论它们在序列内的处置如何,都可以有效地鉴定PRRS蛋白。在这里,我们主要基于文本挖掘和设定理论来识别PRR和R基因,以根据主域的存在和不存在将它们分为13类的PRR和R基因。分析24种植物和藻类基因组,我们表明RRGPRedictor比已经可用的其他工具更有效,特异性和敏感性,并鉴定了在整个序列中具有尺寸变化和结构域分布的PRR蛋白。除了易于鉴定新的植物PRRS蛋白,RRGPRedictor还提供了低计算成本。

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