Lin Jianan, Chen Yang, Zhang Yuping, Ouyang Zhengqing
Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003, USA.
Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.
NAR Genom Bioinform. 2020 Aug 14;2(3):lqaa057. doi: 10.1093/nargab/lqaa057. eCollection 2020 Sep.
RNA conformational alteration has significant impacts on cellular processes and phenotypic variations. An emerging genetic factor of RNA conformational alteration is a new class of single nucleotide variant (SNV) named riboSNitch. RiboSNitches have been demonstrated to be involved in many genetic diseases. However, identifying riboSNitches is notably difficult as the signals of RNA structural disruption are often subtle. Here, we introduce a novel computational framework-RIboSNitch Predictor based on Robust Analysis of Pairing probabilities (Riprap). Riprap identifies structurally disrupted regions around any given SNVs based on robust analysis of local structural configurations between wild-type and mutant RNA sequences. Compared to previous approaches, Riprap shows higher accuracy when assessed on hundreds of known riboSNitches captured by various experimental RNA structure probing methods including the parallel analysis of RNA structure (PARS) and the selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE). Further, Riprap detects the experimentally validated riboSNitch that regulates human catechol-O-methyltransferase haplotypes and outputs structurally disrupted regions precisely at base resolution. Riprap provides a new approach to interpreting disease-related genetic variants. In addition, we construct a database (RiboSNitchDB) that includes the annotation and visualization of all presented riboSNitches in this study as well as 24 629 predicted riboSNitches from human expression quantitative trait loci.
RNA构象改变对细胞过程和表型变异有重大影响。RNA构象改变的一个新出现的遗传因素是一类名为核糖核酸特异性单核苷酸变异（riboSNitch）的新型单核苷酸变异（SNV）。核糖核酸特异性单核苷酸变异已被证明与许多遗传疾病有关。然而，识别核糖核酸特异性单核苷酸变异非常困难，因为RNA结构破坏的信号往往很微弱。在此，我们基于配对概率的稳健分析（Riprap）引入了一种新颖的计算框架——核糖核酸特异性单核苷酸变异预测器。Riprap基于对野生型和突变型RNA序列之间局部结构构型的稳健分析，识别任何给定SNV周围的结构破坏区域。与先前的方法相比，在通过包括RNA结构平行分析（PARS）和引物延伸分析的选择性2'-羟基酰化（SHAPE）在内的各种实验性RNA结构探测方法捕获的数百个已知核糖核酸特异性单核苷酸变异上进行评估时，Riprap显示出更高的准确性。此外，Riprap检测到了调节人类儿茶酚-O-甲基转移酶单倍型的经实验验证的核糖核酸特异性单核苷酸变异，并以碱基分辨率精确输出结构破坏区域。Riprap为解释与疾病相关的遗传变异提供了一种新方法。此外，我们构建了一个数据库（核糖核酸特异性单核苷酸变异数据库），其中包括本研究中所有呈现的核糖核酸特异性单核苷酸变异的注释和可视化，以及来自人类表达数量性状位点的24629个预测的核糖核酸特异性单核苷酸变异。
