KvSNP：准确预测电压门控钾通道中遗传变异的影响。

KvSNP: accurately predicting the effect of genetic variants in voltage-gated potassium channels.

机构信息

Institute of Molecular and Cellular Biology, Faculty of Biological Sciences and Institute of Membrane and Systems Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK.

出版信息

Bioinformatics. 2011 Aug 15;27(16):2181-6. doi: 10.1093/bioinformatics/btr365. Epub 2011 Jun 17.

DOI:10.1093/bioinformatics/btr365

PMID:21685056

Abstract

MOTIVATION

Non-synonymous single nucleotide polymorphisms (nsSNPs) in voltage-gated potassium (Kv) channels cause diseases with potentially fatal consequences in seemingly healthy individuals. Identifying disease-causing genetic variation will aid presymptomatic diagnosis and treatment of such disorders. NsSNP-effect predictors are hypothesized to perform best when developed for specific gene families. We, thus, created KvSNP: a method that assigns a disease-causing probability to Kv-channel nsSNPs.

RESULTS

KvSNP outperforms popular non gene-family-specific methods (SNPs&GO, SIFT and Polyphen) in predicting the disease potential of Kv-channel variants, according to all tested metrics (accuracy, Matthews correlation coefficient and area under receiver operator characteristic curve). Most significantly, it increases the separation of the median predicted disease probabilities between benign and disease-causing SNPs by 26% on the next-best competitor. KvSNP has ranked 172 uncharacterized Kv-channel nsSNPs by disease-causing probability.

AVAILABILITY AND IMPLEMENTATION

KvSNP, a WEKA implementation is available at www.bioinformatics.leeds.ac.uk/KvDB/KvSNP.html.

CONTACT

d.r.westhead@leeds.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

电压门控钾 (Kv) 通道中的非同义单核苷酸多态性 (nsSNP) 可导致看似健康的个体出现潜在致命后果的疾病。确定致病的遗传变异将有助于对这类疾病进行症状前诊断和治疗。当为特定的基因家族开发时，nsSNP 效应预测因子被假设能表现出最佳性能。因此，我们创建了 KvSNP：一种为 Kv 通道 nsSNP 分配致病概率的方法。

结果

根据所有测试指标（准确性、马修斯相关系数和接收器操作特征曲线下的面积），KvSNP 在预测 Kv 通道变体的疾病潜力方面优于流行的非基因特异性方法（SNPs&GO、SIFT 和 Polyphen）。最重要的是，它将下一个最佳竞争对手的良性和致病 SNP 之间的中位预测疾病概率的分离度提高了 26%。KvSNP 按致病概率对 172 个未表征的 Kv 通道 nsSNP 进行了排序。