信使核糖核酸(mRNA)的非翻译区(UTR),包括5'UTR和3'UTR,在调节基因表达和翻译中起着至关重要的作用。UTR内的变异可能导致与人类特征和疾病相关的变化;然而,UTR变体效应的计算预测具有挑战性。当前的非编码变体预测主要集中在启动子和增强子上,忽略UTR的独特序列,从而限制了它们的预测准确性。在这项研究中,使用来自疾病数据库和大规模实验数据的UTR变异的合并数据集,我们系统分析了UTR的50多个区域特异性特征,包括功能元素,二级结构,序列组成和场地保护。我们的分析表明,某些特征,如5'UTR中的C/G相关序列组成和3'UTR中的A/T相关序列组成,有效区分非功能性和功能性变体集,揭示功能性UTR变体的潜在序列决定因素。利用这些见解,我们开发了两个分类模型来使用机器学习预测功能UTR变体,5UTR的曲线下面积(AUC)值为0.94,3UTR为0.85,优于所有现有方法。我们的模型将有助于提高遗传变异的临床解释,促进疾病风险的预测和管理。
The untranslated region (
UTR) of messenger ribonucleic acid (mRNA), including the 5\'
UTR and 3\'
UTR, plays a critical role in regulating gene expression and translation. Variants within the UTR can lead to changes associated with human traits and diseases; however, computational prediction of
UTR variant effect is challenging. Current noncoding variant prediction mainly focuses on the promoters and enhancers, neglecting the unique sequence of the UTR and thereby limiting their predictive accuracy. In this study, using consolidated datasets of UTR variants from disease databases and large-scale experimental data, we systematically analyzed more than 50 region-specific features of
UTR, including functional elements, secondary structure, sequence composition and site conservation. Our analysis reveals that certain features, such as C/G-related sequence composition in 5\'UTR and A/T-related sequence composition in 3\'UTR, effectively differentiate between nonfunctional and functional variant sets, unveiling potential sequence determinants of functional
UTR variants. Leveraging these insights, we developed two classification models to predict functional UTR variants using machine learning, achieving an area under the curve (AUC) value of 0.94 for 5\'UTR and 0.85 for 3\'
UTR, outperforming all existing methods. Our models will be valuable for enhancing clinical interpretation of genetic variants, facilitating the prediction and management of disease risk.