Predicting regulatory variants with composite statistic
Predicting regulatory variants with composite statistic
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DOI：
10.1093/bioinformatics/btw288
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发表时间：
2016-09-15
期刊：
影响因子：
5.8
通讯作者：
Wang, Junwen
中科院分区：
文献类型：
--
作者：
Li, Mulin Jun;Pan, Zhicheng;Wang, Junwen
动机：人类非编码调控变异的预测和优先排序对于理解疾病发病的调控机制和促进个体化医疗至关重要。现有的工具利用功能基因组学数据和进化信息来评估非编码变体的致病性或调节功能。然而，不同的算法会导致不一致甚至相互冲突的预测。结合多种方法可能会增加在监管variant prediction.Results的准确性：在这里，我们编译了一个综合资源的预测从八个不同的工具上的功能注释的非编码变体。我们进一步开发了一种复合策略来整合多个预测，并计算了给定变体为监管变体的复合可能性。以多个独立的因果变量数据集为基准，我们证明了我们的复合模型显著提高了预测性能。可用性和实施：我们将我们的模型和评分程序作为一种工具，名为PRINCIPLE，可免费提供给学术和非营利组织使用，网址为http://jjwanglab.org/PRVCS.Contact：wang. mayo.edu，jliu@stat.harvard.edu，or limx54@gmail.com补充信息：补充数据可在Bioinformatics在线获得。
Motivation: Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction.Results: Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance.Availability and Implementation: We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS.Contact: wang.junwen@mayo.edu, jliu@stat.harvard.edu, or limx54@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.