与疾病相关的非同义单核苷酸多态性预测的研究进展

Progress in Prediction of Disease-Associated Non-Synonymous Single Nucleotide Polymorphisms

单核苷酸多态性(single nucleotide polymorphism,SNPs),即在基因组水平上由单个核苷酸的变异而引起的DNA序列多态性变化,具体是指在DNA序列中的单个碱基的变异,其是人类基因组变异种最常见的一种。SNP研究最主要的目的就是对人类表型变异遗传学的理解,尤其是关于人类遗传疾病的研究。而非同义单核苷酸多态性(nsSNPs)是SNPs中的一种,主要是指处于编码区会引起翻译后对应氨基酸序列变化的单核苷酸突变。因为nsSNPs可能会对蛋白质的功能造成影响,被认为是造成人类遗传病的主要原因。因此将与疾病相关的nsSNPs从中性的nsSNPs中区分出来是很重要的。本文根据国内外与疾病相关nsSNPs预测的研究,分析了预测中所涉及到的特征属性,总结了对这些特征进行优化的特征选择方法,并概述了在预测过程中使用的各种分类器。

SNPs stands for single nucleotide polymorphism, which are single-nucleotide substitutions of one base for another in the DNA sequence which represents the most frequent type of human population DNA variation. One of the most important motivations to do SNPs research is to help understand the genetics of the human phenotype variation and especially the genetic base of complex diseases. Non-synonymous single nucleotide polymorphisms（ns SNPs） are coding mutation that introduces amino acid changes in their corresponding proteins. ns SNPs are believed to be the main cause of human genetic disease because they can affect protein function.Therefore, it is important to distinguish the disease-related ns SNPs from those are neutral. Based on domestic and international research about the prediction of disease-associated ns SNPs, the essay analyzed the characteristic attributes involved in prediction and summarized the attribute selection methods that can extract the informative features improving the performance of classifier, meanwhile, an overview of the various classifiers used in the prediction process.