基于先验知识的突变优先级对罕见疾病中的因果变异发现
Prior Knowledge based mutation prioritization towards causal variant finding in rare disease
摘要：
我们如何在很少或没有统计学证据的情况下确定外显子组测序数据中的突变效应？蛋白质结构信息能填补统计学证据不足的空白吗？在这项工作中，我们回答了这两个问题，目的是确定罕见疾病中罕见变异的致病作用。我们采取的方法，确定点突变位点的重要性集中在蛋白质的结构特征。所提出的基于结构的特征包含突变位点的几何、物理化学和功能信息以及位点的结构邻居的信息。在80%的HumDiv上训练并在20%的HumDiv和ClinVar数据集上测试的基于结构的特征的性能显示出突变的致病或良性影响的高水平可解释性：使用多层感知器的F得分分别为0.71和0.68。结合基于结构和序列的特征进一步提高了准确性：F评分为0.86（HumDiv）和0.75（ClinVar）。此外，对罕见疾病病例中罕见变异的仔细检查表明，基于结构的特征在辨别变异基因座的重要性方面很重要。
英文摘要：
How do we determine the mutational effects in exome sequencing data with little or no statistical evidence? Can protein structural information fill in the gap of not having enough statistical evidence? In this work, we answer the two questions with the goal towards determining pathogenic effects of rare variants in rare disease. We take the approach of determining the importance of point mutation loci focusing on protein structure features. The proposed structure-based features contain information about geometric, physicochemical, and functional information of mutation loci and those of structural neighbors of the loci. The performance of the structure-based features trained on 80\% of HumDiv and tested on 20\% of HumDiv and on ClinVar datasets showed high levels of discernibility in the mutation's pathogenic or benign effects: F score of 0.71 and 0.68 respectively using multi-layer perceptron. Combining structure- and sequence-based feature further improve the accuracy: F score of 0.86 (HumDiv) and 0.75 (ClinVar). Also, careful examination of the rare variants in rare diseases cases showed that structure-based features are important in discerning importance of variant loci.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
