Functional Annotations Improve the Predictive Score of Human Disease-Related Mutations in Proteins
Functional Annotations Improve the Predictive Score of Human Disease-Related Mutations in Proteins
复制标题
DOI：
10.1002/humu.21047
复制
发表时间：
2009-08-01
期刊：
影响因子：
3.9
通讯作者：
Casadio, Rita
中科院分区：
文献类型：
--
作者：
Calabrese, Remo;Capriotti, Emidio;Casadio, Rita
单核苷酸多态性（SNPs）是人类DNA变异中最简单、最常见的形式，也是疾病易感性的遗传标记。研究最多的SNP是错义突变，导致蛋白质中的残基取代。在这里，我们提出了SNPs&GO，一个准确的方法，从蛋白质序列开始，可以预测突变是否与疾病相关或不利用蛋白质功能注释。SNP &GO的评分效率高达82%，在广泛的蛋白质注释非同义突变组中，马修斯相关系数等于0.63，包括16，330个疾病相关和17，432个中性多态性。SNPs&GO收集来自蛋白质序列的独特框架信息，进化信息和基因本体论术语中编码的功能，并优于其他可用的预测方法。《Mutat》30，1237-1244，2009年。(C)2009 Wiley-Liss，Inc.
Single nucleotide polymorphisms (SNPs) are the simplest and most frequent form of human DNA variation, also valuable as genetic markers of disease susceptibility. The most investigated SNPs are missense mutations resulting in residue substitutions in the protein. Here we propose SNPs&GO, an accurate method that, starting from a protein sequence, can predict whether a mutation is disease related or not by exploiting the protein functional annotation. The scoring efficiency of SNPs&GO is as high as 82%, with a Matthews correlation coefficient equal to 0.63 over a wide set of annotated nonsynonymous mutations in proteins, including 16,330 disease-related and 17,432 neutral polymorphisms. SNPs&GO collects in unique framework information derived from protein sequence, evolutionary information, and function as encoded in the Gene Ontology terms, and outperforms other available predictive methods. Hum Mutat 30, 1237-1244, 2009. (C) 2009 Wiley-Liss, Inc.