Frequently used bioinformatics tools overestimate the damaging effect of allelic variants
Frequently used bioinformatics tools overestimate the damaging effect of allelic variants
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DOI：
10.1038/s41435-017-0002-z
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发表时间：
2019-01-01
影响因子：
5
通讯作者：
Hartmann, Rune
中科院分区：
文献类型：
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作者：
Andersen, Line Lykke;Terczynska-Dyla, Ewa;Hartmann, Rune
我们选择了两组天然存在的人类先天免疫基因内的错义等位基因变体。第一组代表干扰素（IFN）诱导中涉及的6个不同基因中的11个非同义变体，存在于患有单纯疱疹病毒性脑炎（HSE）的患者队列中，第二组代表IFNLR1基因的16个等位基因变体。我们在体外重建了这些变体，并在基于HEK293T细胞的测定中测试了它们对蛋白质功能的影响。然后，我们使用了一系列14个可用的生物信息学工具来预测这些变体对蛋白质功能的影响。令我们惊讶的是，两个最常用的工具，CADD和SIFT，产生了很高的假阳性率，而SNPs和GO在我们的测试中表现出最低的假阳性率。由于我们测试中的问题通常是假阳性变体，因此包含突变显著性截止值（MSC）并不能提高准确性。
We selected two sets of naturally occurring human missense allelic variants within innate immune genes. The first set represented eleven non-synonymous variants in six different genes involved in interferon (IFN) induction, present in a cohort of patients suffering from herpes simplex encephalitis (HSE) and the second set represented sixteen allelic variants of the IFNLR1 gene. We recreated the variants in vitro and tested their effect on protein function in a HEK293T cell based assay. We then used an array of 14 available bioinformatics tools to predict the effect of these variants upon protein function. To our surprise two of the most commonly used tools, CADD and SIFT, produced a high rate of false positives, whereas SNPs&GO exhibited the lowest rate of false positives in our test. As the problem in our test in general was false positive variants, inclusion of mutation significance cutoff (MSC) did not improve accuracy.