iFish: predicting the pathogenicity of human nonsynonymous variants using gene-specific/family-specific attributes and classifiers
iFish: predicting the pathogenicity of human nonsynonymous variants using gene-specific/family-specific attributes and classifiers
复制标题
iFish：使用基因特异性/家族特异性属性和分类器预测人类非同义变异的致病性
DOI：
10.1038/srep31321
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发表时间：
2016-08
影响因子：
4.6
通讯作者：
Wei Liping
中科院分区：
文献类型：
--
作者：
Wang Meng;Wei Liping
准确预测基因组变异，尤其是非同义单核苷酸变异（nsSNV）的致病性，对于生物医学研究和临床遗传学至关重要。当前大多数预测方法为所有基因构建通用分类器。然而，不同的基因和基因家族具有不同的特征。我们研究了基因特异性和家族特异性的定制分类器是否可以提高预测准确性。使用AIC、BIC和LASSO选择定制的基因特异性和家族特异性属性，并为254个基因和152个基因家族生成支持向量机分类器，总共覆盖5,985个基因。我们的结果表明，定制的属性反映了基因和基因家族的关键特征，定制的分类器比通用分类器实现了更高的预测精度。定制的分类器和其他基因和家族的通用分类器被集成到名为 iFish 的新工具中（integratedFunctionalinference ofSNVs in human，http://ifish.cbi.pku.edu.cn）。 iFish 在基准数据集以及全外显子组测序中候选因果变异的优先级方面优于其他方法。 iFish 提供用户友好的基于网络的界面，并支持其他功能，例如遗传证据的整合。 iFish 将促进人类遗传学研究中 nsSNV 的高通量评估和优先排序。
Accurate prediction of the pathogenicity of genomic variants, especially nonsynonymous single nucleotide variants (nsSNVs), is essential in biomedical research and clinical genetics. Most current prediction methods build a generic classifier for all genes. However, different genes and gene families have different features. We investigated whether gene-specific and family-specific customized classifiers could improve prediction accuracy. Customized gene-specific and family-specific attributes were selected with AIC, BIC, and LASSO, and Support Vector Machine classifiers were generated for 254 genes and 152 gene families, covering a total of 5,985 genes. Our results showed that the customized attributes reflected key features of the genes and gene families, and the customized classifiers achieved higher prediction accuracy than the generic classifier. The customized classifiers and the generic classifier for other genes and families were integrated into a new tool named iFish (integratedFunctionalinference ofSNVs inhuman, http://ifish.cbi.pku.edu.cn). iFish outperformed other methods on benchmark datasets as well as on prioritization of candidate causal variants from whole exome sequencing. iFish provides a user-friendly web-based interface and supports other functionalities such as integration of genetic evidence. iFish would facilitate high-throughput evaluation and prioritization of nsSNVs in human genetics research.
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影响因子：
9.8
作者：
Easton, Douglas F.;Deffenbaugh, Amie M.;Goldgar, David E.
通讯作者：
Goldgar, David E.
来自1,092个人基因组的遗传变异的综合图。
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
3.9
作者：
Shihab, Hashem A.;Gough, Julian;Cooper, David N.;Stenson, Peter D.;Barker, Gary L. A.;Edwards, Keith J.;Day, Ian N. M.;Gaunt, Tom R.
通讯作者：
Gaunt, Tom R.
影响因子：
4.4
作者：
Zhang W;Wan YW;Allen GI;Pang K;Anderson ML;Liu Z
通讯作者：
Liu Z
影响因子：
12.3
作者：
Reva B;Antipin Y;Sander C
通讯作者：
Sander C