Bioinformatics Division, School of Biosciences and Technology, VIT University, Vellore, Tamil Nadu, India.
Interdiscip Sci. 2010 Dec;2(4):320-46. doi: 10.1007/s12539-010-0003-3. Epub 2010 Dec 12.
The genetics of human phenotype variation and especially, the genetic basis of human complex diseases could be understood by knowing the functions of Single Nucleotide Polymorphisms (SNPs). The main goal of this work is to predict the deleterious non-synonymous SNPs (nsSNPs), so that the number of SNPs screened for association with disease can be reduced to that most likely alters gene function. In this work by using computational tools, we have analyzed the SNPs that can alter the expression and function of cancerous genes involved in colon cancer. To explore possible relationships between genetic mutation and phenotypic variation, different computational algorithm tools like Sorting Intolerant from Tolerant (evolutionary-based approach), Polymorphism Phenotyping (structure-based approach), PupaSuite, UTRScan and FASTSNP were used for prioritization of high-risk SNPs in coding region (exonic nonsynonymous SNPs) and non-coding regions (intronic and exonic 5' and 3'-untranslated region (UTR) SNPs). We developed semi-quantitative relative ranking strategy (non availability of 3D structure) that can be adapted to a priori SNP selection or post hoc evaluation of variants identified in whole genome scans or within haplotype blocks associated with disease. Lastly, we analyzed haplotype tagging SNPs (htSNPs) in the coding and untranslated regions of all the genes by selecting the force tag SNPs selection using iHAP analysis. The computational architecture proposed in this review is based on integrating relevant biomedical information sources to provide a systematic analysis of complex diseases. We have shown a "real world" application of interesting existing bioinformatics tools for SNP analysis in colon cancer.
通过了解单核苷酸多态性（SNP）的功能，可以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。这项工作的主要目标是预测有害的非同义 SNP（nsSNP），从而将与疾病相关的 SNP 筛选数量减少到最有可能改变基因功能的 SNP。在这项工作中，我们使用计算工具分析了可能改变结肠癌相关致癌基因表达和功能的 SNP。为了探索遗传突变与表型变异之间的可能关系，我们使用了不同的计算算法工具，如耐受排序（基于进化的方法）、多态性表型分析（基于结构的方法）、PupaSuite、UTRScan 和 FASTSNP，对编码区（外显子非同义 SNP）和非编码区（内含子和外显子 5'和 3'-非翻译区（UTR）SNP）中的高风险 SNP 进行优先级排序。我们开发了半定量相对排序策略（缺乏 3D 结构），可适用于先验 SNP 选择或全基因组扫描或与疾病相关的单倍型块中鉴定的变体的事后评估。最后，我们通过使用 iHAP 分析选择强制标记 SNP 对所有基因的编码区和非翻译区中的单倍型标记 SNP（htSNP）进行了分析。本综述中提出的计算架构基于整合相关的生物医学信息源，提供对复杂疾病的系统分析。我们展示了在结肠癌 SNP 分析中现有生物信息学工具的“实际应用”。
