甘氨酸max基因WIN单核苷酸多态性测定的生物信息学方法

P. D. Timkin, A. Penzin
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引用次数: 0

摘要

本文以核糖核酸酶基因WIN为例,提出了一种定位snp(单核苷酸多态性)的假设方法。核糖核酸酶包括一种酶,它参与大豆对真菌感染的防御反应,以及对生物胁迫的其他保护性反应。它属于RNA-ases组,这决定了它的特定性质,即降解外来核酸的能力。这种能力提供了植物对抗原结构入侵的一般非特异性免疫反应。现代生物技术要求发展分子方法和途径,这些方法和途径将增加一种培养物的抵抗力或加速其在该领域的适应过程。这个问题可以通过使用SNP人工诱导技术来解决,这些技术可以在基因组中编码能够对生物应激产生保护反应的蛋白质。本研究利用生物信息学分析提出了5个单核苷酸多态性。由于单核苷酸变化的存在,snp的定位和检测是一项具有挑战性的任务,因此在生物技术实践中,进行预测分析是为了定位发生单核苷酸多态性的潜在序列。在确定假设的SNP位置后,可以使用复杂的分子方法进一步检测,例如实时PCR或局部测序。该技术可以成为培育具有预定性状的大豆品种的有力工具。这种理论和预测模型将允许在人为负荷下对植物的动态环境作出更快的反应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bioinformatic method for determining single nucleotide polymorphisms on the example of gene WIN in Glycine max
In this paper, a hypothetical method for locating SNPs (single nucleotide polymorphisms) on the example of the ribonuclease gene WIN was proposed. Ribonuclease comprises an enzyme that participates in defence reactions against fungal infections in soybeans, as well as other protective responses to biotic stress. Its belonging to the RNA-ases group determines the specific properties, namely the ability to degrade foreign nucleic acids. This ability provides for a general nonspecific immune response of the plant to the invasion of antigenic structures. Modern biotechnology calls for the development of molecular methods and approaches that will increase the resistance of a culture or accelerate the processes of its adaptation in the field. This problem can be solved by using technologies of SNP artificial induction in those parts of the genome that encode proteins capable of acting in protective reactions against biotic stress. In the study, 5 single-nucleotide polymorphisms were proposed using bioinformatic analysis. Since the localisation and detection of SNPs comprise a challenging task due to the presence of a single nucleotide change, in the biotechnological practice, predictive analysis is carried out in order to localise the potential sequence of occurring single-nucleotide polymorphism. Following the identification of the hypothetical SNP location, they can be further detected using complex molecular methods, such as real-time PCR or local sequencing. This technology can become a powerful tool for breeding soybean varieties having predetermined properties. Such theoretical and predictive models will allow for a quicker response to the dynamic environment under manmade load on plants.
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