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Optimization of in silico tools for predicting genetic variants: individualizing for genes with molecular sub-regional stratification

错义突变 生物信息学 癫痫 计算生物学 致病性 计算机科学 表型 基因 生物 遗传学 机器学习 神经科学 微生物学
作者
Bin Tang,Bin Li,Liang‐Di Gao,Na He,Xiao‐Rong Liu,Yue‐Sheng Long,Yang Zeng,Yong‐Hong Yi,Tao Su,Wei‐Ping Liao
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:21 (5): 1776-1786 被引量:44
标识
DOI:10.1093/bib/bbz115
摘要

Genes are unique in functional role and differ in their sensitivities to genetic defects, but with difficulties in pathogenicity prediction. This study attempted to improve the performance of existing in silico algorithms and find a common solution based on individualization strategy. We initiated the individualization with the epilepsy-related SCN1A variants by sub-regional stratification. SCN1A missense variants related to epilepsy were retrieved from mutation databases, and benign missense variants were collected from ExAC database. Predictions were performed by using 10 traditional tools with stepwise optimizations. Model predictive ability was evaluated using the five-fold cross-validations on variants of SCN1A, SCN2A, and KCNQ2. Additional validation was performed in SCN1A variants of damage-confirmed/familial epilepsy. The performance of commonly used predictors was less satisfactory for SCN1A with accuracy less than 80% and varied dramatically by functional domains of Nav1.1. Multistep individualized optimizations, including cutoff resetting, domain-based stratification, and combination of predicting algorithms, significantly increased predictive performance. Similar improvements were obtained for variants in SCN2A and KCNQ2. The predictive performance of the recently developed ensemble tools, such as Mendelian clinically applicable pathogenicity, combined annotation-dependent depletion and Eigen, was also improved dramatically by application of the strategy with molecular sub-regional stratification. The prediction scores of SCN1A variants showed linear correlations with the degree of functional defects and the severity of clinical phenotypes. This study highlights the need of individualized optimization with molecular sub-regional stratification for each gene in practice.
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