Single nucleotide polymorphisms (SNPs) are the most common type of genetic variations in humans. Understanding the functions of SNPs can greatly help to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. The method to identify functional SNPs from a pool, containing both functional and neutral SNPs is challenging by experimental protocols. To explore possible relationships between genetic mutation and phenotypic variation, different computational algorithm tools like Sorting Intolerant from Tolerant, Polymorphism Phenotyping, UTRscan, FASTSNP, and PupaSuite were used for prioritization of high-risk SNPs in coding region (exonic nonsynonymous SNPs) and noncoding regions (intronic and exonic 5' and 3'-untranslated region (UTR) SNPs). In this work, we have analyzed the SNPs that can alter the expression and function of transcriptional factor TP53 as a pipeline and for providing a guide to experimental work. We identified the possible mutations and proposed modeled structure for the mutant proteins and compared them with the native protein. These nsSNPs play a critical role in cancer association studies aiming to explain the disparity in cancer treatment responses as well as to improve the effectiveness of the cancer treatments. Our results endorse the study with in vivo experimental protocols.

中文翻译：

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计算算法工具在识别功能性 SNP 中的应用。

单核苷酸多态性 (SNP) 是人类最常见的遗传变异类型。了解SNPs的功能可以极大地帮助理解人类表型变异的遗传学，尤其是人类复杂疾病的遗传基础。从包含功能性和中性 SNP 的池中识别功能性 SNP 的方法对实验方案具有挑战性。为了探索基因突变和表型变异之间可能的关系，使用不同的计算算法工具，如从耐受排序不耐受、多态性表型、UTRscan、FASTSNP 和 PupaSuite，用于对编码区（外显子非同义 SNP）和非编码区中的高风险 SNP 进行优先排序（内含子和外显子 5' 和 3'-非翻译区 (UTR) SNP）。在这项工作中，我们已经分析了可以改变转录因子 TP53 表达和功能的 SNP 作为管道，并为实验工作提供指导。我们确定了可能的突变并提出了突变蛋白质的模型结构，并将它们与天然蛋白质进行了比较。这些 nsSNP 在癌症关联研究中发挥着关键作用，旨在解释癌症治疗反应的差异以及提高癌症治疗的有效性。我们的结果支持体内实验方案的研究。这些 nsSNP 在癌症关联研究中发挥着关键作用，旨在解释癌症治疗反应的差异以及提高癌症治疗的有效性。我们的结果支持体内实验方案的研究。这些 nsSNP 在癌症关联研究中发挥着关键作用，旨在解释癌症治疗反应的差异以及提高癌症治疗的有效性。我们的结果支持体内实验方案的研究。