MOTIVATION A variety of in silico tools have been developed and frequently used to aid high-throughput rapid variant classification, but their performances vary, and their ability to classify variants of uncertain significance were not systemically assessed previously due to lack of validation data. This has been changed recently by advances of functional assays, where functional impact of genetic changes can be measured in single-nucleotide resolution using saturation genome editing (SGE) assay. RESULTS We demonstrated the neural network model AIVAR (Artificial Intelligent VARiant classifier) was highly comparable to human experts on multiple verified datasets. Although highly accurate on known variants, AIVAR together with CADD and PhyloP showed non-significant concordance with SGE function scores. Moreover, our results indicated that neural network model trained from functional assay data may not produce accurate prediction on known variants. AVAILABILITY AND IMPLEMENTATION All source code of AIVAR is deposited and freely available at https://github.com/TopGene/AIvar. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

中文翻译：

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评估人类，计算机预测和遗传变异分类功能分析之间的一致性。

动机已经开发了多种计算机软件工具，并经常使用它们来辅助高通量快速变体分类，但是由于缺乏验证数据，以前并未系统地评估它们的性能，并且对分类不确定性显着的变体的能力并未得到系统评估。最近，随着功能测定技术的进步，这种情况已经改变，可以使用饱和基因组编辑（SGE）测定法以单核苷酸分辨率测量遗传变化的功能影响。结果我们证明了神经网络模型AIVAR（人工智能VARiant分类器）在多个经过验证的数据集上与人类专家具有高度的可比性。尽管在已知变体上非常准确，但AIVAR与CADD和PhyloP一起显示出与SGE功能评分没有显着一致性。而且，我们的结果表明，由功能测定数据训练的神经网络模型可能无法对已知变体产生准确的预测。可用性和实现AIVAR的所有源代码都可以在https://github.com/TopGene/AIvar上存放并免费获得。补充信息补充数据可从Bioinformatics在线获得。