In vitro and in silico analysis reveals an efficient algorithm to predict the splicing consequences of mutations at the 5' splice sites.
In vitro and in silico analysis reveals an efficient algorithm to predict the splicing consequences of mutations at the 5' splice sites.
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DOI：
10.1093/nar/gkm647
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发表时间：
2007
影响因子：
14.9
通讯作者：
Ohno K
中科院分区：
文献类型：
--
作者：
Sahashi K;Masuda A;Matsuura T;Shinmi J;Zhang Z;Takeshima Y;Matsuo M;Sobue G;Ohno K
我们发现之前报道的 PINK1 和 PARK7 基因中的两个外显子突变会影响前 mRNA 剪接。为了开发一种算法来预测 5' 剪接位点外显子突变的被低估的剪接后果，我们构建并分析了 31 个携带外显子剪接突变及其衍生物的小基因。我们还检查了整个人类基因组的 189 249 个 U2 依赖性 5' 剪接位点，发现一个新变量 SD-Score 代表特定 5' 剪接位点频率的常用对数，可以有效预测这些小基因的剪接结果。我们还利用信息内容（Ri）来提高预测精度。我们通过分析 32 个额外的小基因以及 179 个先前报道的剪接突变来验证我们的算法。 SD-Score 算法预测了 204 个位点中 198 个位点的异常剪接（灵敏度 = 97.1%）和 38 个位点中的 36 个位点的正常剪接（特异性 = 94.7%）。对 189 249 个位点的 -3、-2 和 -1 位点上所有可能的外显子突变的模拟预测，这些突变中的 37.8%、88.8% 和 96.8% 将分别影响前 mRNA 剪接。我们认为 SD-Score 算法是预测影响 5' 剪接位点的突变的剪接后果的实用工具。
We have found that two previously reported exonic mutations in the PINK1 and PARK7 genes affect pre-mRNA splicing. To develop an algorithm to predict underestimated splicing consequences of exonic mutations at the 5′ splice site, we constructed and analyzed 31 minigenes carrying exonic splicing mutations and their derivatives. We also examined 189 249 U2-dependent 5′ splice sites of the entire human genome and found that a new variable, the SD-Score, which represents a common logarithm of the frequency of a specific 5′ splice site, efficiently predicts the splicing consequences of these minigenes. We also employed the information contents (Ri) to improve the prediction accuracy. We validated our algorithm by analyzing 32 additional minigenes as well as 179 previously reported splicing mutations. The SD-Score algorithm predicted aberrant splicings in 198 of 204 sites (sensitivity = 97.1%) and normal splicings in 36 of 38 sites (specificity = 94.7%). Simulation of all possible exonic mutations at positions −3, −2 and −1 of the 189 249 sites predicts that 37.8, 88.8 and 96.8% of these mutations would affect pre-mRNA splicing, respectively. We propose that the SD-Score algorithm is a practical tool to predict splicing consequences of mutations affecting the 5′ splice site.
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