Telethon Kids Institute Precision Health Computational Biology, The University of Western Australia, Subiaco, Western Australia, Australia.
Hum Mutat. 2022 May;43(5):539-546. doi: 10.1002/humu.24362. Epub 2022 Mar 9.
Identifying the causal variant for diagnosis of genetic diseases is challenging when using next-generation sequencing approaches and variant prioritization tools can assist in this task. These tools provide in silico predictions of variant pathogenicity, however they are agnostic to the disease under study. We previously performed a disease-specific benchmark of 24 such tools to assess how they perform in different disease contexts. We found that the tools themselves show large differences in performance, but more importantly that the best tools for variant prioritization are dependent on the disease phenotypes being considered. Here we expand the assessment to 37 tools and refine our assessment by separating performance for nonsynonymous single nucleotide variants (nsSNVs) and missense variants (i.e., excluding nonsense variants). We found differences in performance for missense variants compared to nsSNVs and recommend three tools that stand out in terms of their performance (BayesDel, CADD, and ClinPred).
当使用下一代测序方法和变异优先级工具来诊断遗传疾病时,确定因果变异是具有挑战性的,这些工具可以辅助完成这项任务。这些工具提供了变异致病性的计算预测,但它们对所研究的疾病是不可知的。我们之前针对 24 种此类工具进行了特定于疾病的基准测试,以评估它们在不同疾病背景下的表现。我们发现这些工具本身在性能上存在很大差异,但更重要的是,用于变异优先级的最佳工具取决于所考虑的疾病表型。在这里,我们将评估扩展到 37 种工具,并通过将非同义单核苷酸变异 (nsSNV) 和错义变异 (即,排除无意义变异) 的性能分开来改进我们的评估。我们发现错义变异的性能与 nsSNV 存在差异,并推荐了三种在性能方面表现突出的工具(BayesDel、CADD 和 ClinPred)。