Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics.
Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics.
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DOI：
10.1186/s40246-017-0104-8
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发表时间：
2017-05-16
期刊：
影响因子：
4.5
通讯作者：
Park DJ
中科院分区：
文献类型：
--
作者：
Mahmood K;Jung CH;Philip G;Georgeson P;Chung J;Pope BJ;Park DJ
遗传变异效应预测算法广泛用于临床基因组学和研究，以确定氨基酸取代对蛋白质功能的可能后果。我们必须更好地理解它们的准确性和局限性，因为已发布的性能指标被严重的循环性和错误传播问题所混淆。在这里，我们获得了三个独立的，功能确定的人类突变数据集，UniFun，BRCA 1-DMS和TP 53-TA，并将它们与先前描述的数据集一起使用，以评估卓越的变异效应预测工具。变异效应预测工具的表观准确性受到基准数据集的显著影响。使用测定确定的数据集UniFun和BRCA 1-DMS进行基准测试，分别在0.52至0.63和0.54至0.75的适度范围内产生了接收器操作特征曲线下的面积，大大低于其他可能更冲突的数据集的观察结果。这些结果引起了人们对如何使用这些算法的关注，特别是在临床环境中。当代变异效应预测工具不太可能像以前报道的那样准确地预测对蛋白质的功能影响。使用功能测定为基础的数据集，避免先前的依赖关系，承诺是有价值的持续发展和准确的基准测试等工具。本文的在线版本（doi：10.1186/s40246-017-0104-8）包含补充材料，可供授权用户使用。
Genetic variant effect prediction algorithms are used extensively in clinical genomics and research to determine the likely consequences of amino acid substitutions on protein function. It is vital that we better understand their accuracies and limitations because published performance metrics are confounded by serious problems of circularity and error propagation. Here, we derive three independent, functionally determined human mutation datasets, UniFun, BRCA1-DMS and TP53-TA, and employ them, alongside previously described datasets, to assess the pre-eminent variant effect prediction tools. Apparent accuracies of variant effect prediction tools were influenced significantly by the benchmarking dataset. Benchmarking with the assay-determined datasets UniFun and BRCA1-DMS yielded areas under the receiver operating characteristic curves in the modest ranges of 0.52 to 0.63 and 0.54 to 0.75, respectively, considerably lower than observed for other, potentially more conflicted datasets. These results raise concerns about how such algorithms should be employed, particularly in a clinical setting. Contemporary variant effect prediction tools are unlikely to be as accurate at the general prediction of functional impacts on proteins as reported prior. Use of functional assay-based datasets that avoid prior dependencies promises to be valuable for the ongoing development and accurate benchmarking of such tools. The online version of this article (doi:10.1186/s40246-017-0104-8) contains supplementary material, which is available to authorized users.