William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry Queen, Queen Mary University of London, EC1M 6BQ London, UK.
The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac188.
Yuan et al. recently described an independent evaluation of several phenotype-driven gene prioritization methods for Mendelian disease on two separate, clinical datasets. Although they attempted to use default settings for each tool, we describe three key differences from those we currently recommend for our Exomiser and PhenIX tools. These influence how variant frequency, quality and predicted pathogenicity are used for filtering and prioritization. We propose that these differences account for much of the discrepancy in performance between that reported by them (15-26% diagnoses ranked top by Exomiser) and previously published reports by us and others (72-77%). On a set of 161 singleton samples, we show using these settings increases performance from 34% to 72% and suggest a reassessment of Exomiser and PhenIX on their datasets using these would show a similar uplift.
袁等人最近在两个独立的临床数据集上对几种基于表型的孟德尔疾病基因优先级排序方法进行了独立评估。尽管他们试图为每个工具使用默认设置，但我们描述了三个与我们目前为 Exomiser 和 PhenIX 工具推荐的设置有明显不同的关键区别。这些差异影响了如何使用变异频率、质量和预测致病性进行过滤和优先级排序。我们提出，这些差异很大程度上解释了他们报告的性能差异（Exomiser 排名前 15-26%的诊断）与我们和其他人之前发表的报告（72-77%）之间的差异。在一组 161 个单体样本中，我们使用这些设置将性能从 34%提高到 72%，并建议使用这些设置重新评估 Exomiser 和 PhenIX 在其数据集上的性能，结果也将显示类似的提升。
